Establishing the President's Committee on Equality of Treatment and Opportunity In the Armed Forces.
1. It is hereby declared to be the policy of the President that there shall be equality of treatment and opportunity for all persons in the armed services without regard to race, color, religion or national origin. This policy shall be put into effect as rapidly as possible, having due regard to the time required to effectuate any necessary changes without impairing efficiency or morale.
2. There shall be created in the National Military Establishment an advisory committee to be known as the President's Committee on Equality of Treatment and Opportunity in the Armed Services, which shall be composed of seven members to be designated by the President.
3. The Committee is authorized on behalf of the President to examine into the rules, procedures and practices of the Armed Services in order to determine in what respect such rules, procedures and practices may be altered or improved with a view to carrying out the policy of this order. The Committee shall confer and advise the Secretary of Defense, the Secretary of the Army, the Secretary of the Navy, and the Secretary of the Air Force, and shall make such recommendations to the President and to said Secretaries as in the judgment of the Committee will effectuate the policy hereof.
4. All executive departments and agencies of the Federal Government are authorized and directed to cooperate with the Committee in its work, and to furnish the Committee such information or the services of such persons as the Committee may require in the performance of its duties.
5. When requested by the Committee to do so, persons in the armed services or in any of the executive departments and agencies of the Federal Government shall testify before the Committee and shall make available for use of the Committee such documents and other information as the Committee may require.
6. The Committee shall continue to exist until such time as the President shall terminate its existence by Executive order.


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Q: Numpy array comparison using nditer The code below is giving me the correct answer, but only works when the arrays (plan and meas) are relatively small.  When I try to run this over the arrays I actually need to compare (300x300 each), it takes forever (I don't know how long because I have been terminating it after 45 minutes.)  I would like to only iterate over a range of array values around the index being evaluated (p).  I tried to find documentation on the nditer flag 'ranged' but cannot find how to implement a specific range to iterate through.
p = np.nditer(plan, flags = ['multi_index','common_dtype'])
while not p.finished:
    gam_store = 100.0
    m = np.nditer(meas, flags = ['multi_index','common_dtype'])
    while not m.finished:
        dis_eval = np.sqrt(np.absolute(p.multi_index[0]-m.multi_index[0])**2 + np.absolute(p.multi_index[1]-m.multi_index[1])**2)
        if dis_eval <= 6.0:
            a = (np.absolute(p[0] - m[0]) / maxdose) **2
            b = (dis_eval / gam_dist) **2
            gam_eval = np.sqrt(a + b)
            if gam_eval < gam_store:
                gam_store = gam_eval
        m.iternext()
    gamma = np.insert(gamma, location, gam_store, 0)
    location = location + 1
    p.iternext()

A: If you only want to iterate through a small part of the array, I think (unless I am misunderstanding the question) that you should just create an nditer instance from a slice of the array.
Say you only want the array near (i,j), then start with this:
w = 5    # half-size of the window around i, j
p = np.nditer(plan[i-w:i+w, j-w:j+w], flags=...)

This works because, say
a = array([[ 0,  1,  2,  3,  4],
           [ 5,  6,  7,  8,  9],
           [10, 11, 12, 13, 14],
           [15, 16, 17, 18, 19],
           [20, 21, 22, 23, 24]])

Then,
w = 1
i, j = 2,2
print a[i-w:i+w+1, j-w:j+w+1]
#array([[ 6,  7,  8],
#       [11, 12, 13],
#       [16, 17, 18]])

redpajama_set_name: RedPajamaStackExchange


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text:
Determining sample size for progression criteria for pragmatic pilot RCTs: the hypothesis test strikes back!
M. Lewis ORCID: orcid.org/0000-0001-5290-78331,2,
K. Bromley1,2,
C. J. Sutton3,
G. McCray1,2,
H. L. Myers2 &
G. A. Lancaster1,2
The current CONSORT guidelines for reporting pilot trials do not recommend hypothesis testing of clinical outcomes on the basis that a pilot trial is under-powered to detect such differences and this is the aim of the main trial. It states that primary evaluation should focus on descriptive analysis of feasibility/process outcomes (e.g. recruitment, adherence, treatment fidelity). Whilst the argument for not testing clinical outcomes is justifiable, the same does not necessarily apply to feasibility/process outcomes, where differences may be large and detectable with small samples. Moreover, there remains much ambiguity around sample size for pilot trials.
Many pilot trials adopt a 'traffic light' system for evaluating progression to the main trial determined by a set of criteria set up a priori. We construct a hypothesis testing approach for binary feasibility outcomes focused around this system that tests against being in the RED zone (unacceptable outcome) based on an expectation of being in the GREEN zone (acceptable outcome) and choose the sample size to give high power to reject being in the RED zone if the GREEN zone holds true. Pilot point estimates falling in the RED zone will be statistically non-significant and in the GREEN zone will be significant; the AMBER zone designates potentially acceptable outcome and statistical tests may be significant or non-significant.
For example, in relation to treatment fidelity, if we assume the upper boundary of the RED zone is 50% and the lower boundary of the GREEN zone is 75% (designating unacceptable and acceptable treatment fidelity, respectively), the sample size required for analysis given 90% power and one-sided 5% alpha would be around n = 34 (intervention group alone). Observed treatment fidelity in the range of 0–17 participants (0–50%) will fall into the RED zone and be statistically non-significant, 18–25 (51–74%) fall into AMBER and may or may not be significant and 26–34 (75–100%) fall into GREEN and will be significant indicating acceptable fidelity.
In general, several key process outcomes are assessed for progression to a main trial; a composite approach would require appraising the rules of progression across all these outcomes. This methodology provides a formal framework for hypothesis testing and sample size indication around process outcome evaluation for pilot RCTs.
The importance and need for pilot and feasibility studies is clear: "A well-conducted pilot study, giving a clear list of aims and objectives … will encourage methodological rigour … and will lead to higher quality RCTs" [1]. The CONSORT extension to external pilot and feasibility trials was published in 2016 [2] with the following key methodological recommendations: (i) investigate areas of uncertainty about the future definitive RCT; (ii) ensure primary aims/objectives are about feasibility, which should guide the methodology used; (iii) include assessments to address the feasibility objectives which should be the main focus of data collection and analysis; and (iv) build decision processes into the pilot design whether or how to proceed to the main study. Given that many trials incur process problems during implementation—particularly with regard to recruitment [3,4,5]—the need for pilot and feasibility studies is evident.
One aspect of pilot and feasibility studies that remains unclear is the required sample size. There is no consensus but recommendations vary from 10 to 12 per group through to 60–75 per group depending on the main objective of the study. Sample size may be based on precision of a feasibility parameter [6, 7]; precision of a clinical parameter which may inform main trial sample size—particularly the standard deviation (SD) [8,9,10,11] but also event rate [12] and effect size [13, 14]; or, to a lesser degree, for clinical scale evaluation [9, 15]. Billingham et al. [16] reported that the median sample size of pilot and feasibility studies is around 30–36 per group but there is wide variation. Herbert et al. [17] reported that targets within internal as opposed to external pilots are often slightly larger and somewhat different, being based on percentages of the total sample size and timeline rather than any fixed sample requirement.
The need for a clear directive on sample size of studies is of upmost relevance. The CONSORT extension [2] reports that "Pilot size should be based on feasibility objectives and some rationale given" and states that a "confidence interval approach may be used to calculate and justify the sample size based on key feasibility objective(s)". Specifically, item 7a (How sample size was determined: Rationale for numbers in the pilot trial) qualifies: "Many pilot trials have key objectives related to estimating rates of acceptance, recruitment, retention, or uptake … for these sorts of objectives, numbers required in the study should ideally be set to ensure a desired degree of precision around the estimated rate". Item 7b (When applicable, explanation of any interim analyses and stopping guidelines) is generally an uncommon scenario for pilot and feasibility studies and is not given consideration here.
A key aspect of pilot and feasibility studies is to inform progression to the main trial, which has important implications for all key stakeholders (funders, researchers, clinicians and patients). The CONSORT extension [2] states that "decision processes about how to proceed needs to be built into the pilot design (which might involve formal progression criteria to decide whether to proceed, proceed with amendments, or not to proceed)" and authors should present "if applicable, the pre-specified criteria used to judge whether or how to proceed with a future definitive RCT; … implications for progression from pilot to future definitive RCT, including any proposed amendments". Avery et al. [18] published recommendations for internal pilots emphasising a traffic light (stop-amend-go/red-amber-green) approach to progression with focus on process assessment (recruitment, protocol adherence, follow-up) and transparent reporting around the choice of trial design and the decision-making processes for stopping, amending or proceeding to a main trial. The review of Herbert et al. [17] reported that the use of progression criteria (including recruitment rate) and traffic light stop-amend-go as opposed to simple stop-go is increasing for internal pilot studies.
A common misuse of pilot and feasibility studies has been the application of hypothesis testing for clinical outcomes in small under-powered studies. Arain et al. [19] claimed that pilot studies were often poorly reported with inappropriate emphasis on hypothesis testing. They reviewed 54 pilot and feasibility studies published in 2007–2008, of which 81% incorporated hypothesis testing of clinical outcomes. Similarly, Leon et al. [20] stated that a pilot is not a hypothesis testing study: safety, efficacy and effectiveness should not be evaluated. Despite this, hypothesis testing has been commonly performed for clinical effectiveness/efficacy without reasonable justification. Horne et al. [21] reviewed 31 pilot trials published in physical therapy journals between 2012 and 2015 and found that only 4/31 (13%) carried out a valid sample size calculation on effectiveness/efficacy outcomes but 26/31 (84%) used hypothesis testing. Wilson et al. [22] acknowledged a number of statistical challenges in assessing potential efficacy of complex interventions in pilot and feasibility studies. The CONSORT extension [2] re-affirmed many researchers' views that formal hypothesis testing for effectiveness/efficacy is not recommended in pilot/feasibility studies since they are under-powered to do so. Sim's commentary [23] further contests such testing of clinical outcomes stating that treatment effects calculated from pilot or feasibility studies should not be the basis of a sample size calculation for a main trial.
However, when the focus of analysis is on confidence interval estimation for process outcomes, this does not give a definitive basis for acceptance/rejection of progression criteria linked to formal powering. The issue in this regard is that precision focuses on alpha (α, type I error) without clear consideration of beta (β, type II error) and may therefore not reasonably capture true differences if a study is under-powered. Further, it could be argued that hypothesis testing of feasibility outcomes (as well as addressing both alpha and beta) is justified on the grounds that moderate-to-large differences ('process-effects') may be expected rather than small differences that would require large sample numbers. Moore et al. [24] previously stated that some pilot studies require hypothesis testing to guide decisions about whether larger subsequent studies can be undertaken, giving the following example of how this could be done for feasibility outcomes: asking the question "Is taste of dietary supplement acceptable to at least 95% of the target population?", they showed that sample sizes of 30, 50 and 70 provide 48%, 78% and 84% power to reject an acceptance rate of 85% or lower if the true acceptance rate is 95% using a 1-sided α = 0.05 binomial test. Schoenfeld [25] advocates that, even for clinical outcomes, there may be a place for testing at the level of clinical 'indication' rather than 'clinical evidence'. He suggested that preliminary hypothesis testing for efficacy could be conducted with high alpha (up to 0.25), not to provide definitive evidence but as an indication as to whether a larger study should be conducted. Lee et al. [14] also reported how type 1 error levels other than the traditional 5% could be considered to provide preliminary evidence for efficacy, although they did stop short of recommending doing this by concluding that a confidence interval approach is preferable.
Current recommendations for sample sizes of pilot/feasibility studies vary, have a single rather than a multi-criterion basis, and do not necessarily link directly to formal progression criteria. The purpose of this article is to introduce a simple methodology that allows sample size derivation and formal testing of proposed progression cut-offs, whilst offering suggestions for multi-criterion assessment, thereby giving clear guidance and sign-posting for researchers embarking on a pilot/feasibility study to assess uncertainty in feasibility parameters prior to a main trial. The suggestions within the article do not directly apply to internal pilot studies built into the design of a main trial, but given the similarities to external randomised pilot and feasibility studies, many of the principles outlined here for external pilots might also extend to some degree to internal pilots of randomised and non-randomised studies.
The proposed approach focuses on estimation and hypothesis testing of progression criteria for feasibility outcomes that are potentially modifiable (e.g. recruitment, treatment fidelity/ adherence, level of follow up). Thus, it aligns with the main aims and objectives of pilot and feasibility studies and with the progression stop-amend-go recommendations of Eldridge et al. [2] and Avery et al. [18].
Hypothesis concept
Let RUL denote the upper RED zone cut-off and GLL denote the lower GREEN zone cut-off. The concept is to set up hypothesis testing around progression criteria that tests against being in the RED zone (designating unacceptable feasibility—'STOP') based on an alternative of being in the GREEN zone (designating acceptable feasibility—'GO'). This is analogous to the zero difference (null) and clinically important difference (alternative) in a main superiority trial. Specifically, we are testing against RUL when GLL is hypothesised to be true:
Null hypothesis: True feasibility outcome (ε) not greater than the upper "RED" stop limit (RUL)
Alternative hypothesis: True feasibility outcome (ε) is greater than RUL
The test is a 1-tailed test with suggested alpha (α) of 0.05 and beta (β) of 0.05, 0.1 or 0.2, dependent on the required strength of evidence of the test. An example of a feasibility outcome might be percentage recruitment uptake.
Progression rules
Let E denote the observed point estimate (ranging from 0 to 1 for proportions, or for percentages 0–100%). Simple 3-tiered progression criteria would follow as:
E ≤ RUL [P value non-significant (P ≥ α)] -> RED (unacceptable—STOP)
RUL < E < GLL -> AMBER (potentially acceptable—AMEND)
E ≥ GLL [P value significant (P < α)] -> GREEN (acceptable—GO)
Table 1 displays a quick look-up grid for sample size across a range of anticipated proportions for RUL and GLL for one-sample one-sided 5% alpha with typical 80% and 90% (as well as 95%) power for the normal approximation method with continuity correction (see Appendix for corresponding mathematical expression; derived from Fleiss et al. [26]). Table 2 is the same look-up grid relating to the Binomial exact approach with sample sizes derived using G*Power version 3.1.9.7 [27]. Clearly, as the difference between proportions RUL and GLL increases the sample size requirement is reduced.
Table 1 Sample size and significance cut-points for (GLL-RUL) differences for a one-sample test, power (80%, 90%, 95%) and 1-tailed 5% significance level based on normal approximation (with continuity correction)
Table 2 Sample size and significance cut-points for (GLL-RUL) differences for a one-sample test, power (80%, 90%, 95%) and 1-tailed 5% significance level based on the binomial exact test
Multi-criteria assessment
We recommend that progression for all key feasibility criteria should be considered separately, and hence overall progression would be determined by the worst-performing criterion, e.g. RED if at least one signal is RED, AMBER if none of the signals fall into RED but at least one falls into AMBER and GREEN if all signals fall into the GREEN zone. Hence, the GREEN signal to 'GO' across the set of individual criteria will give indication that progression to a main trial can take place without any necessary changes. A signal to 'STOP' and not proceed to a main trial is recommended if any of the observed estimates are 'unacceptably' low (i.e. fall within the RED zone). Otherwise, where neither 'GO' nor 'STOP' are signalled, the design of the trial will need amending by indication of subpar performance on one or more of the criteria.
Sample size requirements across multi-criteria will vary according to the designated parameters linked to the progression criteria, which may be set at different stages of the study on different numbers of patients (e.g. those screened, eligible, recruited and randomised, allocated to the intervention arm, total followed up). The overall size needed will be dictated by the requirement to power each of the multi-criteria statistical tests. Since these tests will yield separate conclusions in regard to the decision to 'STOP', 'AMEND' or 'GO' across all individual feasibility criteria there is no need to consider a multiple testing correction with respect to alpha. However, researchers may wish to increase power (and hence, sample size) to ensure adequate power to detect 'GO' signals across the collective set of feasibility criteria. For example, powering at 90% across three criteria (assumed independent) will ensure a collective power of 73% (i.e. 0.93), which may be considered reasonable, but 80% power across five criteria will reduce the power of the combined test to 33%. The final three columns of Table 1 cover the sample sizes required for 95% power, which may address collective multi-criteria assessment when considering keeping a high overall statistical power.
Further expansion of AMBER zone
Within the same sample size framework, the AMBER zone may be further split to indicate whether 'minor' or 'major' amendments are required according to the significance of the p value. Consider a 2-way split in the AMBER zone denoted by cut-off AC, which indicates the threshold for statistical significance, where an observed estimate below the cut-point will result in a non-significant result and an estimate at or above the cut-point a significant result. Let AMBERR denote the region of Amber zone adjacent to the RED zone between RUL and AC, and AMBERG denote the region of AMBER zone between AC and GLL adjacent to the GREEN zone. This would draw on two possible levels of amendment ('major' AMEND and 'minor' AMEND) and the re-configured approach would follow as:
RUL < E < GLL and P ≥ α {RUL < E < Ac} -> AMBERR (major AMEND)
RUL < E < GLL and P < α { Ac ≤ E < GLL} -> AMBERG (minor AMEND)
In Tables 1 and 2 in relation to designated sample sizes for different RUL and GLL and specified α and β, we show the corresponding cut-points for statistical significance (p < 0.05) both in absolute terms of sample number (n) [AC] and as a percentage of the total sample sizes [AC%].
A motivating example (aligned to the normal approximation approach) is presented in Table 3, which illustrates a pilot trial with three progression criteria. Table 4 presents the sample size calculations for the example scenario following the 3-tiered approach, and Table 5 gives the sample size calculations for the example scenario using the extended 4-tiered approach. Cut-points for the feasibility outcomes relating to the shown sample sizes are also presented to show RED, AMBER and GREEN zones for each of the three progression criteria.
Table 3 Motivating example—feasibility trial for oral protein energy supplements as flavoured drinks to improve nutritional status in children with cystic fibrosis
Table 4 Case illustration (standard 3-tiered approach)
Table 5 Case illustration (re-visited using 4-tiered approach)
Overall sample size requirement should be dictated by the multi-criteria approach. This is illustrated in Table 4 where we have three progression criteria each with a different denominator population. For recruitment uptake, the denominator denotes the total number of children screened and the numerator the number of children randomised; for follow-up, the denominator is the number of children randomised with the numerator being number of those randomised who are successfully followed up; and lastly for treatment fidelity, the denominator is the number allocated to the intervention arm with the numerator being the number of children who were administered the treatment correctly by the dietician. In the example in order to meet the individual ≥ 90% power requirement for all three criteria we would need: (i) for recruitment, the number to be screened to be 78; (ii) for treatment fidelity, the number in the intervention arm to be 34; and (iii) for follow up, the number randomised to be 44. In order to determine the overall sample size for the whole study, we base our decision on the criterion that requires the largest numbers, which is the treatment fidelity criterion which requires 68 to be randomised. We cannot base our decision on the 78 required to be screened for recruitment because this would give only an expected number of 28 randomised (i.e. 35% of 78). If we expect 35% recruitment uptake, then we need to inflate the total 68 (randomised) to be 195 (1/0.35 × 68) children to be screened (rounded to 200). This would give 99.9%, 90% and 98.8% power for criteria (i), (ii) and (iii), respectively (assuming 68 of the 200 screened are randomised), giving a very reasonable collective 88.8% power of rejecting the null hypotheses over the three criteria if the alternative hypotheses (for acceptable feasibility outcomes) are true in each case.
Inherent in our approach are the probabilities around sample size, power and hypothesised feasibility parameters. For example, taking the cut-offs from treatment fidelity as a feasibility outcome from Table 4 (ii), we set a lower GREEN zone limit of GLL = 0.75 ("acceptable" (hypothesised alternative value)) and an upper RED zone limit of RUL = 0.5 ("not acceptable" (hypothesised null value)) for rejecting the null for this criterion based on 90% power and a 1-sided 5% significance level (alpha). Figure 1 presents the normal probability density functions for ε, for the null and alternative hypotheses. In the illustration this would imply through normal sampling theory that if GLL holds true (i.e. true recruitment uptake (ε) = GLL) there would be the following:
A probability of 0.1 (type II error probability β) of the estimate falling within RED/AMBERR zones (i.e. blue shaded area under the curve to the left of AC where the test result will be non-significant (p ≥ 0.05))
Probability of 0.4 of it falling in the AMBERG zone (i.e. area under the curve to the right of AC but below GLL)
Probability of 0.5 of the estimate falling in the GREEN zone (i.e. GLL and above).
Illustration of power using the 1-tailed hypothesis testing against the traffic light signalling approach to pilot progression. E, observed point estimate; RUL, upper limit of RED zone; GLL, lower limit of GREEN zone; Ac, cut-off for statistical significance (at the 1-sided 5% level); α, type I error; β, type II error
If RUL (the null) holds true (i.e. true feasibility outcome (ε) = RUL), there would be the following:
A probability of 0.05 (one-tailed type I error probability α) of the statistic/estimate falling in the AMBERG/GREEN zones (i.e. pink shaded area under the curve to the right of AC where the test result will be significant (p < 0.05) as shown within Fig. 1)
Probability of 0.45 of it falling in the AMBERR zone (i.e. to the left of AC but above RUL)
Probability of 0.5 of the estimate falling in the RED zone (i.e. RUL and below)
Figure 1 also illustrates how changing the sample size affects the sampling distribution and power of the analysis around the set null value (at RUL) when the hypothesised alternative (GLL) is true. The figure emphasises the need for a large enough sample to safeguard against under-powering of the pilot analysis (as shown in the last plot which has a wider bell-shape than the first two plots and where the size of the beta probability is increased).
Figure 2 plots the probabilities of making each type of traffic light decision as functions of the true parameter value (focused on the recruitment uptake example from Table 5 (i)). Additional file 1 presents the R code for reproducing these probabilities and enables readers to insert different parameter values.
Probability of traffic light given true underlying probability of an event using the example from Table 5 (i). Two plots are presented: a relating to normal approximation approach and b relating to binomial exact approach. Based on n = 200, RUL = 40 and GLL = 70
The methodology introduced in this article provides an innovative formal framework and approach to sample size derivation, aligning sample size requirement to progression criteria with the intention of providing greater transparency to the progression process and full engagement with the standard aims and objectives of pilot/feasibility studies. Through the use of both alpha and beta parameters (rather than alpha alone), the method ensures rigour and capacity to address the progression criteria by ensuring there is adequate power to detect an acceptable threshold for moving forward to the main trial. As several key process outcomes are assessed in parallel and in combination, the method embraces a composite multi-criterion approach that appraises signals for progression across all the targeted feasibility measures. The methodology extends beyond the requirement for 'sample size justification but not necessarily sample size calculation' [28].
The focus of the strategy reported here is on process outcomes, which align with the recommended key objectives of primary feasibility evaluation for pilot and feasibility studies [2, 24] and necessary targets to address key issues of uncertainty [29]. The concept of justifying progression is key. Charlesworth et al. [30] developed a checklist for intended use in decision-making on whether pilot data could be carried forward to a main trial. Our approach builds on this philosophy by introducing a formalised hypothesis test approach to address the key objectives and pilot sample size. Though the suggested sample size derivation focuses around the key process objectives, it may also be the case that other objectives are also important, e.g. assessment of precision of clinical outcome parameters. In this case, researchers may also wish to ensure that the size of the study suitably covers the needs of those evaluations, e.g. to estimate the SD of the intended clinical outcome, then the overall sample size may be boosted to cover this additional objective [10]. This tallies with the review by Blatch-Jones et al. [31] who reported that testing recruitment, determining the sample size and numbers available, and the intervention feasibility were the most commonly used targets of pilot evaluations.
Hypothesis testing in pilot studies, particularly in the context of effectiveness/efficacy of clinical outcomes, has been widely criticised due to the improper purpose and lack of statistical power of such evaluations [2, 20, 21, 23]. Hence, pilot evaluations of clinical outcomes are not expected to include hypothesis testing. Since the main focus is on feasibility the scope of the testing reported here is different and importantly relates back to the recommended objectives of the study whilst also aligning with nominated progression criteria [2]. Hence, there is clear justification for this approach. Further, for the simple 3-tiered approach hypothesis testing is somewhat hypothetical: there is no need to physically carry out a test since the zonal positioning of the observed sample statistic estimate for the feasibility outcome will determine the decision in regard to progression; thus adding to the simplicity of the approach.
The link between the sample size and need to adequately power the study to detect a meaningful feasibility outcome gives this approach the extra rigour over the confidence interval approach. It is this sample size-power linkage that is key to the determination of the respective probabilities of falling into the different zones and is a fundamental underpinning to the methodological approach. In the same way as for a key clinical outcome in a main trial where the emphasis is not just on alpha but also on beta thereby addressing the capacity to detect a clinically significant difference, similarly, our approach is to ensure there is sufficient capacity to detect a meaningful signal for progression to a main trial if it truly exists. A statistically significant finding in this context will at least provide evidence to reject RED (signifying a decision to STOP) and in the 4-tiered case it would fall above AMBERR (decision to major-AMEND); hence, the estimate will fall into AMBERG or GREEN (signifying a decision to minor-AMEND or GO, respectively). The importance of adequately powering the pilot trial to address a feasibility criterion can be simply illustrated. For example, if we take RUL as 50% and GLL as 75% but with two different sample sizes of n = 25 and n = 50; the former would have 77.5% power of rejecting RED on the basis of a 1-sided 5% alpha level whereas the larger sample size would have 97.8% power of rejecting RED. So, if GLL holds true, there would be 20% higher probability of rejecting the null and being in the AMBERG/GREEN zone for the larger sample giving an increased chance of progressing to the main trial. It will be necessary to carry out the hypothesis test for the extended 4-tier approach if the observed statistic (E) falls in the AMBER zone to determine statistical significance or not, which will inform whether the result falls into the 'minor' or 'major' AMBER sub-zones.
We provide recommended sample sizes within a look-up grid relating to perceived likely progression cut-points to aid quick access and retrievable sample sizes for researchers. For a likely set difference in proportions between hypothesised null and alternative parameters of 0.15 to 0.25 when α = 0.05 and β = 0.1 the corresponding total sample size requirements for the approach of normal approximation with continuity correction take the range of 33 to 100 (median 56) [similarly these are 33–98 (median 54) for the binomial exact method]. Note, for treatment fidelity/adherence/compliance particularly, the marginal difference could be higher, e.g. ≥ 25%, since in most situations we would anticipate and hope to attain a high value for the outcome whilst being prepared to make necessary changes within a wide interval of below par values (and providing the value is not unacceptably low). As this relates to an arm-specific objective (relating to evaluation of the intervention only), then a usual 1:1 pilot will require twice the size; hence, the arm-specific sample size powered for detecting a ≥ 25% difference from the null would be about 34 (or lower)—as depicted from our illustration (Table 4 (ii), equating to n ≤ 68 overall for a 1:1 pilot; intervention and control arms). Hence, we expect that typical pilot sizes of around 30–40 randomised per arm [16] would likely fit with the proposed methodology within this manuscript (the number needed for screening being extrapolated upward of this figure) but if a smaller marginal difference (e.g. ≤ 15%) is to be tested then these sample sizes may fall short. We stress that the overall required sample size needs to be carefully considered and determined in line with the hypothesis testing approach across all criteria ensuring sufficiently high power. In our paper, we have made recommendations regarding various sample sizes based on both the normal approximation (with continuity correction) and binomial exact approaches; these are conservative compared to the Normal approximation (without continuity correction).
Importantly, the methodology outlines the necessary multi-criterion approach to the evaluation of pilot and feasibility studies. If all progression criteria are performing as well as anticipated (highlighting 'GO' according to all criteria), then the recommendation of the pilot/feasibility study is that all criteria meet their desired levels with no need for adjustment and the main trial can proceed without amendment. However, if the worst signal (across all measured criteria) is an AMBER signal, then adjustment will be required against those criteria that fall within that signal. Consequently, there is the possibility that the criteria may need subsequent re-assessment to re-evaluate processes in line with updated performance for the criteria in question. If one or more of the feasibility statistics fall within the RED zone then this signals 'STOP' and concludes that a main trial is not feasible based on those criteria. This approach to collectively appraising progression based on the results of all feasibility outcomes assessed against their criteria will be conservative as the power of the collective will be lower than the individual power of the separate tests; hence, it is recommended that the power of the individual tests is set high enough (for example, 90–95%) to ensure the collective power is high enough (e.g. at least 70 or 80%) to detect true 'GO' signals across all the feasibility criteria.
In this article, we also expand the possibilities for progression criterion and hypothesis testing where the AMBER zone is sub-divided arbitrarily based on the significance of the p value. This may work well when the AMBER zone has a wide range and is intended to provide a useful and workable indication of the level of amendment ('minor' (non-substantive) or 'major' (substantive)) required to progress to the main trial. Examples of substantial amendments include study re-design with possible re-appraisal and change of statistical parameters, inclusion of several additional sites, adding further data recruitment methods, significant reconfiguration of exclusions, major change to the method of delivery of trial intervention to ensure enhanced treatment fidelity/adherence, enhanced measures to systematically ensure greater patient compliance with allocated treatment, additional mode(s) of collecting and retrieving data (e.g. use of electronic data collection methods in addition to postal questionnaires). Minor amendments include small changes to the protocol and methodology, e.g. addition of one or two sites for attaining a slightly higher recruitment rate, use of occasional reminders in regard to treatment protocol and adding a further reminder process for boosting follow up. For the most likely parametrisation of α = 0.05/β = 0.1, the AMBER zone division will be roughly at the midpoint. However, researchers can choose this point (the major/minor cut-point) based on decisive arguments around how major and minor amendments would align to the outcome in question. This should be factored within the process of sample size determination for the pilot. In this regard, a smaller sample size will move AC upwards (due to increased standard error/reduced precision) and hence increase the size of the AMBERR zone in relation to AMBERG (whereas a larger sample size will shift AC downwards and do the opposite, increasing the ratio of AMBERG:AMBERR). From Table 1, for smaller sample sizes (related to 80% power) the AMBERR zone makes up 56–69% of the total amber zone across presented scenarios, whereas this falls to 47–61% for samples (related to 90% power) and 41–56% for larger samples (related to 95% power) for the same scenarios. Beyond our proposed 4-tier approach, other ways of providing an indication of level of amendment could include evaluation and review of the point and interval estimates or by evaluating posterior probabilities via a Bayesian approach [14, 32].
The methodology illustrated here focuses on feasibility outcomes presented as percentages/proportions, which is likely to be the most common form for progression criteria under consideration. However, the steps that have been introduced can be readily adapted to any feasibility outcomes taking a numerical format, e.g. rate of recruitment per month per centre, count of centres taking part in the study. Also, we point out that in the examples presented in the paper (recruitment, treatment fidelity and percent follow-up), high proportions are acceptable and low ones not. This would not be true for, say, adverse events where a reverse scale is required.
Biased sample estimates are a concern as they may result in a wrong decision being made. This systematic error is over-and-above the possibility of an erroneous decision being made on the basis of sampling error; the latter may be reduced through an increased pilot sample size. Any positive bias will inflate/overestimate the feasibility sample estimate in favour of progressing whereas a negative bias will deflate/underestimate it towards the null and stopping. Both are problematic for opposite reasons; for example, the former may inform researchers that the main trial can 'GO' ahead when in fact it will struggle to meet key feasibility targets, whereas the latter may caution against progression when in reality the feasibility targets of a main trial would be met. For example, in regard to the choice of centres (and hence practitioners and participants), a common concern is that the selection of feasibility trial centres might not be a fair and representative sample of the 'population' of centres to be used for the main trial. It may be that the host centre (likely used in pilot studies) recruits far better than others (positive bias), thus exaggerating the signal to progress and subsequent recruitment to the main trial. Beets et al. [33] 'define "risk of generalizability biases" as the degree to which features of the intervention and sample in the pilot study are NOT scalable or generalizable to the next stage of testing in a larger, efficacy/effectiveness trial … whether aspects like who delivers an intervention, to whom it is delivered, or the intensity and duration of the intervention during the pilot study are sustained in the larger, efficacy/effectiveness trial.' As in other types of studies, safeguards regarding bias should be addressed through appropriate pilot study design and conduct.
Issues relating to progression criteria for internal pilots may be different to those for external pilots and non-randomised feasibility studies. The consequence of a 'stop' within an internal pilot may be more serious for stakeholders (researchers, funders, patients) as it would bring an end to the planned continuation into the main trial phase, whereas there would be less at stake for a negative external pilot. By contrast, the consequence of a 'GO' signal may work the other way with a clear and immediate gain for the internal pilot whereas for an external pilot, the researchers would still need to apply and get the necessary funding and approvals to undertake an intended main trial. The chances of falling into the different traffic light zones are likely to be quite different between the two designs. Possibly external pilot and feasibility studies are more likely to have estimates falling in and around the RED zone than for internal pilots, reflecting the greater uncertainty in the processes for the former and greater confidence in the mechanisms for trial delivery for the latter. However, to counter this, there are often large challenges with recruitment within internal pilot studies where the target population is usually spread over more diverse sites than may be expected for an external pilot. Despite this possible imbalance, the interpretation of zonal indications remains consistent for external and internal pilot studies. As such, our focus with regard to the recommendations in this article are aligned to requirements for external pilots, though application of this methodology to a degree may similarly hold for internal pilots (and further, to non-randomised studies that can include progression criteria—including longitudinal observational cohorts with the omission of the treatment fidelity criterion).
We propose a novel framework that provides a paradigm shift towards formally testing feasibility progression criteria in pilot and feasibility studies. The outlined approach ensures rigorous and transparent reporting in line with CONSORT recommendations for evaluation of STOP-AMEND-GO criteria and presents clear progression sign-posting which should help decision-making and inform stakeholders. Targeted progression criteria are focused on recommended pilot and feasibility objectives, particularly recruitment uptake, treatment fidelity and participant retention, and these criteria guide the methodology for sample size derivation and statistical testing. This methodology is intended to provide a more definitive and rounded structure to pilot and feasibility design and evaluation than currently exists. Sample size recommendations will be dependent on the nature and cut-points for multiple key pre-defined progression criteria and should ensure a sufficient sample size for other feasibility outcomes such as review of the precision of clinical parameters to better inform main trial size.
Alpha (α):
Significance level (Type I error probability)
AMBERG :
AMBER sub-zone split adjacent to the GREEN zone (within 4-tiered approach)
AMBERR :
AMBER sub-zone split adjacent to the RED zone (within 4-tiered approach)
A C :
AMBER-statistical significance threshold (within the AMBER zone) where an observed estimate below the cut-point will result in a non-significant result (p ≥ 0.05) and figures at or above the cut-point will be significant (p < 0.05)
A C%:
AC expressed as a percentage of the sample size
Beta (β):
Type II error probability
Estimate of feasibility outcome
ε :
True feasibility parameter
G LL :
Lower Limit of GREEN zone
n :
Sample size (ns = number of patients screened; nr = number of patients randomised; ni = number of patients randomised to the intervention arm only)
Power = 1-Beta:
(1 – Type II error probability)
R UL :
Upper Limit of RED zone
Lancaster GA, Dodd S, Williamson PR. Design and analysis of pilot studies: recommendations for good practice. J Eval Clin Pract. 2004;10(2):307–12.
Eldridge SM, Chan CL, Campbell MJ, Bond CM, Hopewell S, Thabane L, et al. CONSORT 2010 statement: extension to randomised pilot and feasibility trials. Pilot Feasibility Stud. 2016;2:64.
McDonald AM, Knight RC, Campbell MK, Entwistle VA, Grant AM, Cook JA, et al. What influences recruitment to randomised controlled trials? A review of trials funded by two UK funding agencies. Trials. 2006;7:9.
Sully BG, Julious SA, Nicholl J. A reinvestigation of recruitment to randomised, controlled, multicenter trials: a review of trials funded by two UK funding agencies. Trials. 2013;14:166.
Walters SJ, Bonacho Dos Anjos Henriques-Cadby I, Bortolami O, Flight L, Hind D, Jacques RM, et al. Recruitment and retention of participants in randomised controlled trials: a review of trials funded and published by the United Kingdom Health Technology Assessment Programme. BMJ Open. 2017;7(3):e015276.
Julious SA. Sample size of 12 per group rule of thumb for a pilot study. Pharm Stat. 2005;4:287–91.
Thabane L, Ma J, Chu R, Cheng J, Ismaila A, Rios LP, et al. A tutorial on pilot studies: the what, why and how. BMC Med Res Methodol. 2010;10:1.
Browne RH. On the use of a pilot sample for sample size determination. Stat Med. 1995;14:1933–40.
Hertzog MA. Considerations in determining sample size for pilot studies. Res Nurs Health. 2008;31(2):180–91.
Sim J, Lewis M. The size of a pilot study for a clinical trial should be calculated in relation to considerations of precision and efficiency. J Clin Epidemiol. 2012;65(3):301–8.
Whitehead AL, Julious SA, Cooper CL, Campbell MJ. Estimating the sample size for a pilot randomised trial to minimise the overall trial sample size for the external pilot and main trial for a continuous outcome variable. Stat Methods Med Res. 2016;25(3):1057–73.
Teare MD, Dimairo M, Shephard N, Hayman A, Whitehead A, Walters SJ. Sample size requirements to estimate key design parameters from external pilot randomised controlled trials: a simulation study. Trials. 2014;15:264.
Cocks K, Torgerson DJ. Sample size calculations for pilot randomized trials: a confidence interval approach. J Clin Epidemiol. 2013;66(2):197–201.
Lee EC, Whitehead AL, Jacques RM, Julious SA. The statistical interpretation of pilot trials: should significance thresholds be reconsidered? BMC Med Res Methodol. 2014;14:41.
Johanson GA, Brooks GP. Initial scale development: sample size for pilot studies. Edu Psychol Measurement. 2010;70(3):394–400.
Billingham SA, Whitehead AL, Julious SA. An audit of sample sizes for pilot and feasibility trials being undertaken in the United Kingdom registered in the United Kingdom Clinical Research Network database. BMC Med Res Methodol. 2013;13:104.
Herbert E, Julious SA, Goodacre S. Progression criteria in trials with an internal pilot: an audit of publicly funded randomised controlled trials. Trials. 2019;20(1):493.
Avery KN, Williamson PR, Gamble C, O'Connell Francischetto E, Metcalfe C, Davidson P, et al. Informing efficient randomised controlled trials: exploration of challenges in developing progression criteria for internal pilot studies. BMJ Open. 2017;7(2):e013537.
Arain M, Campbell MJ, Cooper CL, Lancaster GA. What is a pilot or feasibility study? A review of current practice and editorial policy. BMC Med Res Methodol. 2010;10:67.
Leon AC, Davis LL, Kraemer HC. The role and interpretation of pilot studies in clinical research. J Psychiatr Res. 2011;45(5):626–9.
Horne E, Lancaster GA, Matson R, Cooper A, Ness A, Leary S. Pilot trials in physical activity journals: a review of reporting and editorial policy. Pilot Feasibility Stud. 2018;4:125.
Wilson DT, Walwyn RE, Brown J, Farrin AJ, Brown SR. Statistical challenges in assessing potential efficacy of complex interventions in pilot or feasibility studies. Stat Methods Med Res. 2016;25(3):997–1009.
Sim J. Should treatment effects be estimated in pilot and feasibility studies? Pilot Feasibility Stud. 2019;5:107.
Moore CG, Carter RE, Nietert PJ, Stewart PW. Recommendations for planning pilot studies in clinical and translational research. Clin Transl Sci. 2011;4(5):332–7.
Schoenfeld D. Statistical considerations for pilot studies. Int J Radiat Oncol Biol Phys. 1980;6(3):371–4.
Fleiss JL, Levin B, Paik MC. Statistical methods for rates and proportions, Third Edition. New York: John Wiley & Sons; 2003. p. 32.
Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39:175–91.
Julious SA. Pilot studies in clinical research. Stat Methods Med Res. 2016;25(3):995–6.
Lancaster GA. Pilot and feasibility studies come of age! Pilot Feasibility Stud. 2015;1(1):1.
Charlesworth G, Burnell K, Hoe J, Orrell M, Russell I. Acceptance checklist for clinical effectiveness pilot trials: a systematic approach. BMC Med Res Methodol. 2013;13:78.
Blatch-Jones AJ, Pek W, Kirkpatrick E, Ashton-Key M. Role of feasibility and pilot studies in randomised controlled trials: a cross-sectional study. BMJ Open. 2018;8(9):e022233.
Willan AR, Thabane L. Bayesian methods for pilot studies. Clin Trials 2020;17(4):414-9.
Beets MW, Weaver RG, Ioannidis JPA, Geraci M, Brazendale K, Decker L, et al. Identification and evaluation of risk of generalizability biases in pilot versus efficacy/effectiveness trials: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2020;17:19.
We thank Professor Julius Sim, Dr Ivonne Solis-Trapala, Dr Elaine Nicholls and Marko Raseta for their feedback on the initial study abstract.
KB was supported by a UK 2017 NIHR Research Methods Fellowship Award (ref RM-FI-2017-08-006).
Biostatistics Group, School of Medicine, Keele University, Room 1.111, David Weatherall Building, Keele, Staffordshire, ST5 5BG, UK
M. Lewis, K. Bromley, G. McCray & G. A. Lancaster
Keele Clinical Trials Unit, Keele University, Keele, Staffordshire, UK
M. Lewis, K. Bromley, G. McCray, H. L. Myers & G. A. Lancaster
Centre for Biostatistics, School of Health Sciences, University of Manchester, Manchester, Staffordshire, UK
C. J. Sutton
M. Lewis
K. Bromley
G. McCray
H. L. Myers
G. A. Lancaster
ML and CJS conceived the original methodological framework for the paper. ML prepared draft manuscripts. KB and GMcC provided examples and illustrations. All authors contributed to the writing and provided feedback on drafts and steer and suggestions for article updating. All authors read and approved the final manuscript.
Correspondence to M. Lewis.
R codes used for Fig. 2.
Mathematical formulae for derivation of sample size
The required sample size may be derived using normal approximation to binary response data—using a continuity correction, via Fleiss et al. [26] if the convention of np > 5 and n(1 − p) > 5 holds true:
$$ n={\left(\frac{z_{1-\propto}\sqrt{R_{UL}\left(1-{R}_{UL}\right)+}{z}_{1-\beta}\sqrt{G_{LL}\left(1-{G}_{LL}\right)}}{\left({G}_{LL}-{R}_{UL}\right)}\right)}^2+\frac{1}{\left|{G}_{LL}-{R}_{UL}\right|} $$
where RUL = upper limit of RED zone; GLL = lower limit of GREEN zone; z1−α = one-sided statistical significance level (type I error probability); z1−β = beta (type II error probability)
Lewis, M., Bromley, K., Sutton, C.J. et al. Determining sample size for progression criteria for pragmatic pilot RCTs: the hypothesis test strikes back!. Pilot Feasibility Stud 7, 40 (2021). https://doi.org/10.1186/s40814-021-00770-x
DOI: https://doi.org/10.1186/s40814-021-00770-x
Outcome and process assessment
Sample size, Statistics

redpajama_set_name: RedPajamaCommonCrawl


text:
How Fixed-Wing Drones Transform Conservation Research Methods
Waypoint sat down with Jenny Williams, a specialist in conservation-based spatial analyses and piloting UAV remote-area surveys. She explains the research she's been able to accomplish with senseFly's fixed-wing drone technology, how drones have transformed her workflow and why she chose the eBee to help her conduct her conservation-based research.
Hi, Jenny! Thanks for chatting with Waypoint today. Let's get started.
Tell us a little bit about the Royal Botanic Gardens and the work you do there.
The Royal Botanic Gardens, Kew is one of the largest and internationally renowned botanical gardens in the world with more than 350 research scientists dedicated to documenting and understanding the globes plant and fungal diversity. At the Kew site, we have physical botanical gardens, an herbarium (dried plant material, including Darwin collections) and a genetics laboratory. We have another site, Wakehurst Place, which is home to the Millennium Seed Bank.
I work in the Biodiversity Informatics and Spatial Analysis department, so my role is to be a scientist in everything geospatial. This ranges from discovering the impact of climate change on individual species, like arabica coffee, to identifying and mapping threatened habitats for Tropical Important Plant Areas.
What does a typical day-to-day working with drones look like for you at the Royal Botanic Gardens?
Most of our drone work takes places in the tropics, which is where Kew's research interests have been historically focused. However, we spend some time at the Wakehurst Place site to check and test equipment due to its large, open areas. These flight tests help us ensure the safety and success of drone missions for when we're in remote areas, such as Madagascar.
For our large-scale drone survey mapping, we always use the eBee fixed-wing drone. It's the lightest and most portable that we've found, which allows us to fly further and longer at the resolution we want.
Before going on a drone mission, we check our kit thoroughly and make sure we have plenty of charged batteries for the drone and laptop. Once we arrive on site, we go through our pre-flight survey checks and determine whether we may need to restrict access to the public during our aerial survey flights, launches and landings. We work in pairs so there is at least one drone pilot with an observer. We always need one pair of eyes on the drone and another looking out for low-flying air traffic, birds of prey and unfavorable weather systems—anything that may threaten the drone's flight.
How did you accomplish your work prior to using drones?
We predominantly used Landsat Thematic Mapper, which has 30-meter resolution, because it's free and has a long ranging archive of imagery. We've also used Sentinel data and, as projects would allow and where we had funding, we'd purchase a small area of WorldView, Quickbird or IKONOS imagery to do similar-level data collection. It's certainly not the same resolution, but you can't get the same resolution with anything other than a drone. Plus, a lot of the areas we work in are tropical, so trying to get satellite imagery in a cloud-free sky is almost always problematic.
A sunset view from camp after a successful day of drone missions in Ambohimahamasina Forest, Madagascar.
What challenges have you faced that caused you to consider drones?
I had a project in Sumatra where I needed precise imagery. Despite having the rare budget for the collection of WorldView2 imagery, plus a year to work on this project, we were never able to source the imagery from the archive or task the collection because of cloud coverage. I ended up using the most current, high-resolution imagery we could find, which was a three-year-old, 10-meter SPOT satellite image. Obviously, this was not ideal or timely.
With drone imagery, we can actually count the number of trees that have fallen or been cut down to potentially discover the density of a forest or estimate future losses.
About six years ago, we looked into getting a drone so we could survey specific areas at specific times and in the specific seasons that worked for us. For some projects, we wanted to know the health of a tree during its healthiest versus most-stressed time of year. For others, we wanted to look at vegetation seasonally to determine variations in species during times of leaf-on or leaf-off. We needed to have the best chance of getting survey imagery without clouds!
Our decision to purchase a drone had a lot to do with the high spatial resolution of drone imagery. We needed the ability to identify individual tree crowns, measure and count felled trees or stumps, precisely map burn scars, and monitor small-scale vegetation regrowth. We simply cannot get timely high-resolution, cloud-free imagery any other way. When it comes to drone surveying, there is no other option for us. To get the level of detail we require, where and when we want it, we need to go and collect the imagery ourselves. Drone footage provides something that nothing else can, there's no other way we can get that imagery.
Which drone models did you originally assess, and which do you fly now?
We first flew the eBee Classic with the RGB, Near-Infra-Red (NIR) and RedEdge sensors. We now fly two eBee Plus drones and mainly use them with senseFly's S.O.D.A. camera, but often perform dual flights with the Parrot Sequoia to get multispectral resolution. The spectral information provided at different wavelengths is necessary for extracting specific vegetation features. Since vegetation is distinctively high in the NIR, this is where we are able to extract the most interesting information and indicate differences in reflectance between species, health and structures.
The main benefit of the eBee, for us, is that we can survey large areas with different cameras. There's nothing else other than the eBee superlight fixed-wing drone that we've found that is able to do that.
I tried to extract felled tree trunks from drone imagery I obtained in Madagascar. There was a large shadow cast over the forest-cut area caused by intact trees. With the S.O.D.A. images, this area appeared dark grey and black because of the shadow. When I looked at the same images from the Sequoia camera's NIR and Red-Edge bands, I could clearly identify the felled tree trunks. This is exactly why the ability to use and switch between different cameras has been so important to us.
What made you decide to go with senseFly?
Most of the imagery we need to collect is in remote areas. In most of the developing countries we work in, we must take the drone kit and personnel in a single 4×4 SUV to the research area. Then, we either drive or carry that kit to the base site. When we looked at potential drones, we asked questions like, "which is the lightest?", "the most portable?", "gives the best value for money?", "can fly the longest?", "can take quality pictures?" and "enables us to put different cameras in it?".
Jenny plugs away on a laptop in senseFly's eMotion flight planning software for a drone mission with the eBee Classic above the remnant forest in Ankafobe, Madagascar.
The eBee is light, flies far and is easily portable. Even the spare parts we carry are light—wings, propellers, band attachments. Most of the weight comes from the extra batteries. We bring 10 with us to enable a full day of droning. However, these are also extremely portable, despite their special LiPo fire-retardant storage bags.
What does a typical workflow with your drone look like?
First, we work out the area we want to fly. For example, when I was in Madagascar, I was given priority zones by Feedback Madagascar, the non-profit organization we worked with, who had teams on the ground. Then, I went through senseFly's eMotion software to plan potential flights and figure out the flight height, resolution and distance needed to cover the area. I had to make sure we could fly with spatial resolution sufficient enough for tree and cut detail we needed while also flying high enough to cover large areas and maximize the short amount of available flying time. Once we figured that out, I created flight plans that covered the high-risk priority zones for deforestation.
We take on far fewer extensive ground-based surveys because we can get so much information from the drone.
Next, we determined where would be best for launches and landings. With the eBee, the launch and landing zones are the most critical part of any mission. We look for big, flat areas that are open enough to get us clearance for proper, safe landings. In Madagascar, these sites were ground-checked for us in advance and only slightly altered to ensure there was camping space.
We had four teams—one of which consisted of the drone pilot and observers. The other three were "roaming teams" to specific geographic locations. The roaming teams were responsible for maintaining consistent visuals of the drone, observing weather patterns and keeping an eye out for our greatest threat: birds of prey. As the pilot, I constantly monitored the drone's status, signal, battery as well as radio reports from the roaming teams to ensure the drone's safety.
We then examine the drone, change its battery and initiate a new flight plan. It's a quick turnaround process before we're able to do it all over again.
An eBee Plus gently glides above land in Ambohimahamasina, Madagascar as it utilizes the information Jenny gathered to ensure a safe landing after a successful mission.
Which post-processing software do you use?
We use eMotion to link photos to flight logs and for the initial post-processing, and then bring it into Pix4D. We've used other bits and bobs, like Agisoft, which one of my colleagues uses. We've also been experimenting with the Pix4D bridging product, Drone2Map, in ArcGIS, but I mostly use Pix4D. They have tons of good tutorial videos on specific processing options that have helped us with more complicated photo blocks. They're always good about answering our questions and getting back to us. Their team has always been helpful.
With drone imagery, we are looking at a completely different level of detail. It allows us to develop a new way of working so we can fully exploit the information held within these images.
What benefits are you getting from using drones?
For our large-scale drone survey mapping, we always use the eBee fixed-wing drone. It's the lightest and most portable that we've found, which allows us to fly further and longer at the resolution we want. We use quadcopters here and there, but they can't match the flight time. Their battery is only about half of the eBee's. Hardly any of our Madagascar surveying would have been possible if we used a quadcopter.
The eBee Classic soars above the drone base, campsite and first-ever drop toilet (bottom-left) in the community of Ambohimahamasina, Madagascar.
How accurate was your data collection before using drones?
We don't really have any comparison at this level of detail. Sometimes, we could purchase high-resolution satellite imagery, but pixel sizes were always greater than one meter. It was even more than that with multispectral imagery. To extract features from the satellite imagery, we would have to trace around the features or downgrade the imagery's resolution to something a bit more coarse so we could effectively use various automated extraction techniques, like image classification. With drone imagery, we are looking at a completely different level of detail. It allows us to develop a new way of working so we can fully explore the information held within these images.
You can't get the same resolution with anything other than a drone.
How have drones impacted your work for the Royal Botanic Gardens?
What we're doing with drones is completely different to our traditional satellite image interpretation. We now look for things at a much finer resolution over larger areas. For example, with drone imagery, we can actually count the number of trees that have fallen or been cut down to potentially discover the density of a forest or estimate future losses. We can identify the health of a specific tree or species, whether they are stressed or diseased. We've never been able to do this before we had drones. We could only extract small amounts of this information from ground-based surveys or plot work in combination with an analysis of satellite imagery.
Recent deforestation of the primary forest in Ambohimahamasina, Madagascar. The detailed imagery shows individually felled tree trunks.
A few years ago, I spent time in Sumatra doing extensive ground-based plot work. With 10-meter resolution SPOT satellite data and three teams, we completed 330 plots with a radius of 12.6 m (500 m2) in three weeks. The plots were rapid, so they weren't full surveys, and we weren't able to identify every species. We were mainly looking at the numbers and quantity of tree species and seedlings to determine the regeneration potential of the forest after years of selective logging. Based on the field data, I was able to classify the satellite image of the Harapan Rainforest into different forest types and estimate carbon storage as a base for future regrowth or illegal deforestation to be monitored and assessed.
Now, we take on far fewer extensive ground-based surveys because we can get so much information from the drone. Field-based teams can also ground-truth the aerial photos with far fewer surveys.
What are some challenges you face when using drones?
Since we mostly fly over dense forests with only sporadically cleared areas, the main challenge we have with the eBee is finding suitable launch and landing areas. Thankfully, because it can fly far, we can often position ourselves outside the forest and fly in over the portions of it we want to map.
What we're doing with drones is completely different to our traditional satellite image interpretation. We now look for things at a much finer resolution over larger areas.
A bigger challenge is getting the country's Civil Aviation Authority clearance to fly our drones. There are many countries with strict regulations that restrict where you can fly, if you can fly and if you can even bring a drone into the country. Some require that you get a permit to fly, which is what I had to do for Madagascar. I submitted my drone pilot certification, all the flight plan and route information, the drone details and our operations manual. All of that had to be signed off before we could fly in the country.
Jenny's colleague Tim Wilkinson shows the village children where the safe launch and landing zones are during their mission in Ambohimahamasina, Madagascar.
Are there any other unique challenges you've encountered?
When my colleagues worked in Peru, they had to provide a large down payment as collateral to provide assurance that they wouldn't sell the drone they'd brought in. They had to do this upon arrival and go back to retrieve that money before they left. The process was very time-consuming and extremely stressful.
In Madagascar, we found the fascination of the drone to be a slight challenge, too. During a drone flight demonstration for local village elders, a large group of school children bombarded the site. There were quite possibly 100 or more children. They were released from their classes after hearing the drone takeoff—all at once. All the Malagasy staff from the Kew Madagascar Conservation Centre who were working on-site with us were suddenly on crowd-control duty to make sure the drone landed safely!
To get the level of detail we require, where and when we want it, we need to go and collect the imagery ourselves. Drone footage provides something that nothing else can, there's no other way we can get that imagery
Was it difficult to implement drone technology at Kew?
Even though we had the buy-in on the research side, getting the funding for it wasn't so easy. Working for a non-profit organization, anything that's a financial outlay is quite complicated and must be fully justified. We had to bid separately for the acquisition of this research kit as we can't purchase it through normal funding sources, such as research grants.
What's next for you? Are you working on any interesting projects?
I still have 38 flights of the Madagascar forest multi-spectral image data to work with – the forest's mosaic map was just the tip of the iceberg!
I'm also applying to get my UK CAA pilots license, which is not currently a legal requirement. It now seems it's harder in most places to fly drones due to necessary safety regulations. I think almost everyone has experienced at least one dangerous or annoying drone flight these days.
We are starting to rely on our drone images for much of the innovative research with our international partners and collaborators. There is so much scientific knowledge to be gained from all this new information collected in these unique and threatened habitats. Time to get processing!
Sam Cameron from Feedback Madagascar (center-left) and Jenny Williams (center-right) brief the village communities about the upcoming drone flights in Ambohimahamasina, Madagascar.
Thanks so much for your time, Jenny.
Good talking with you. Cheers!


text:
Women's 4 x 400m Relay at Athletics World Cup 2018
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Full race report from Women's 4 x 400m Relay at Athletics World Cup in London, July 14, 2018
Athletics World CupAthletics World Cup 2018Athletics World Cup LondonAthletics World Cup Relay4 x 400m RelayWomen's 4 x 400m
Women's 100 metres FINAL IAAF World Athletics Championships Berlin 2009
American Women win 4x400m at Worlds - Universal Sports
http://www.UniversalSports.com 2015, Nassau, Bahamas, 2nd IAAF/BTC World Relays, In the Women's 4x400m, Team USA, Francis, Hastings, Richard-Ross and McCorory r
Men's 4 x 400m Relay at Athletics World Cup 2018
Full race report from Men's 4 x 400m Relay at Athletics World Cup in London, July 15, 2018
Les Françaises championnes du monde du 4x100m (Paris 2003)
Patricia Girard, Muriel Hurtis, Sylviane Félix et Christine Arron championnes du monde du relais 4x100 lors des championnats du monde de Paris 2003. Elles éta
Women's 800m at Ostrava Golden Spike 2018
Full race report from Women's 800m at IAAF World Challenge Meetings 57th Ostrava Golden Spike in Ostrava (Czech Republic), June 13, 2018
American women repeat as 4x400 champs - from Universal Sports
http://www.UniversalSports.com 2011, Daegu, South Korea, IAAF Track and Field World Championship, just as it did in 2009, USA wins, followed by Jamaica and Russ
4 x 800m M Australian National Record 7:11:48
Final 4x100 women Mundial de Londres 2017.
Final 4x100 femenino del mundial de Londres.
American men win again in 4x400 relay - from Universal Sports
http://www.UniversalSports.com 2011, Daegu, South Korea, IAAF Track and Field World Championship, for the 9th time, Team USA has reigned supreme at the champion
USA Win 4x400m Relay Gold - London 2012 Olympics
Allyson Felix, Sanya Richards-Ross, DeeDee Trotter and Francena McCorory win the gold medal for USA as they set a time of 3:16.87 in the women's 4x400m relay.
Rio Replay: Women's 1500m Final
Faith Chepngetich Kipyegon wins gold for Kenya in the women's 1500m final race in Rio 2016. Watch the FULL SESSION here: http://bit.do/Rio2016-Women1500m Subs
Womens 4x400m Relay Final - Sydney 2000 Olympic Games
Womens 4x400m Relay Final - Sydney 2000 Olympic Games featuring Tamsyn Lewis, Nova Peris, Melinda Gainsford-Taylor and Cathy Freeman for Australia
Indian women win Gold! Women's 4x400m relay race in India: Commonwealth Games
Indian wins women's 4x400m relay race during Common Wealth Games in India. The 2010 Commonwealth Games, officially known as the XIX Commonwealth Games, were he
Women's 100m at Athletics World Cup 2018
Full race report from Women's 100m at Athletics World Cup in London, July 14, 2018
RIO Olympics Men's 4 x 400 Relay
RIO Olympics USA men's 4 x 400 meters Relay USA won
4x400 metres relay women final world athletics championships 2013 in Moscow
world athletics championships 2013 in Moscow 4x400 metres relay women final world athletics championships 2013 in Moscow, 2013 IAAF World Championships in Mosco
Women's 100m - Athletics World Cup 2018 London
Women's 4 x 400m Relay Round 1 - London 2012 Olympics
Full highlights from Round 1 in the Women's 4 x 400m relay during the London 2012 Olympic Games. Since 1896, athletics has been on the programme of each editi
TOP 5 | Fastest 4x100m women teams | All time
TOP 5 Fastest 4x100m women teams relays of all time! ------------------------------------------------------------------------------ 1 40.82 United States US
Women's 1500m at Athletics World Cup 2018
Full race report from Women's 1500m at Athletics World Cup in London, July 14, 2018
USA vs Canada | World Relays 2017 Finals
IAAF World Relays Bahamas 2017 Justin Gatlin & Mike Rodgers and Team USA defeats Andre Degrasse and Team Canada and once again bring home the GOLD medal during
Women's 3000m Steeplechase - London 2012 Olympics
Relive the final of the Women's 3000m Steeplechase in the Olympic Stadium at the London 2012 Olympic Games (6 August). Russia's Yuliya Zaripova later had the g
India Beats Vietnam and Wins WOMEN'S 4X400m RELAY FINAL. 22nd ASIAN ATHLETICS CHAMPIONSHIPS-2017
Indian Women's Team beat Vietnam and wins 4X400m RELAY FINAL 22nd ASIAN ATHLETICS CHAMPIONSHIPS-2017 BHUBANESWAR,ODISHA., INDIA Follow us On: Youtube.com ht
FINAL MASCULINA 4X100 LONDON/17
Usain Bolt da la sorpresa en sus últimos mundiales.
Kenya wins 4x4M Relay Mixed Heat 1
Kenya win 4x4M Relay Mixed Heat 1 on day 4 of the IAAF World U18 Championships.
Women's 4x100 Relay Finals - 2000 Sydney Olympics Track & Field
2000 Sydney Olympics Track & Field - Women's 4x100 Relay Finals September 30, 2000 Sydney, Australia 1 BAHAMAS BAH BAH 41.95 SB 2 JAMAICA JAM JAM 42.13 SB 3 U
USA Break Women's 4 x 100m Relay World Record - London 2012 Olympics
Athletics Women's 4 x 100m Relay Final Full Replay from the Olympic Stadium at the London 2012 Olympic Games. -- 10 August 2012 Since 1896, athletics has been


Today we reach yet another milestone on our Dumbarton Harbour project. The final part of the red brick tower is demolished and our project shifts from being a demolition operation to a New Build/Regeneration project. It is a day that we have all been working towards for some time and, as a team, we are delighted to have successfully and safely reached this stage.
There has been a lot of public interest in the tower's demolition. Some are happy to see it go, others no so much. By the time we acquired the tower it was beyond repair and the only thing to do was demolish it. We've had several requests for bricks from the tower so that people can retain a piece of the history of Dumbarton, we were only too happy to oblige.
The Demolition itself was not straight forward and it required lots of investigation and planning. Our contractors, Caskie Construction and Demolition, have executed the demolition project flawlessly. They will now focus on clearing the demolition debris and making the site ready for the construction phase.
Obtaining Planning Approval is the next significant milestone for us. The application will go before West Dunbartonshire Council's Planning Committee in March. We are hopeful that a successful determination will give us the green light to progress with the development.


This apartments home located at 160 Moberly Road, Grande Cache is currently for sale and has been available on Zolo.ca for 200 days. This property is listed at $153,000 with an estimated mortgage of $563* per month. It has 2 beds, 2 bathrooms, and is 1,050 square feet. The property was built in 2007. 160 Moberly Road, Grande Cache is in Grande Cache.
Property Details Home facts for 401 - 160 Moberly Road: utilities, exterior, parking etc.
Rooms Room details for 401 - 160 Moberly Road: size, flooring, features etc.
Want to compare this condo to other units in the condiminium? Take a look at the other active 160 Moberly Road condo for sale. If this property isn't quite what you're looking for, view the other 3 condos for sale in Grande Cache.
160 Moberly Road is a property located in Grande Cache, Alberta. Rural Lac Ste. Anne County is a nearby city. Moberly Road has 5 properties currently for sale, while the city of Grande Cache has 11 properties for sale. The average listed price of a property in the community near moberly-road is $237,741, with an approximate mortgage of $868 per month*. The average community listed home is 1,133 sq ft, has 3 beds, and 2 baths. The majority of active real estate postings in the area around 160 Moberly Roadare houses.


text:
//  (C) Copyright Dave Abrahams, Steve Cleary, Beman Dawes,
//      Howard Hinnant and John Maddock 2000.
//  (C) Copyright Mat Marcus, Jesse Jones and Adobe Systems Inc 2001

//  Use, modification and distribution are subject to the Boost Software License,
//  Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
//  http://www.boost.org/LICENSE_1_0.txt).
//
//  See http://www.boost.org/libs/type_traits for most recent version including documentation.

//    Fixed is_pointer, is_reference, is_const, is_volatile, is_same,
//    is_member_pointer based on the Simulated Partial Specialization work
//    of Mat Marcus and Jesse Jones. See  http://opensource.adobe.com or
//    http://groups.yahoo.com/group/boost/message/5441
//    Some workarounds in here use ideas suggested from "Generic<Programming>:
//    Mappings between Types and Values"
//    by Andrei Alexandrescu (see http://www.cuj.com/experts/1810/alexandr.html).

#ifndef BOOST_TT_IS_SAME_HPP_INCLUDED
#define BOOST_TT_IS_SAME_HPP_INCLUDED

#include <boost_1_56_0/type_traits/config.hpp>
// should be the last #include
#include <boost_1_56_0/type_traits/detail/bool_trait_def.hpp>

namespace boost_1_56_0 {

BOOST_TT_AUX_BOOL_TRAIT_DEF2(is_same,T,U,false)
BOOST_TT_AUX_BOOL_TRAIT_PARTIAL_SPEC2_1(typename T,is_same,T,T,true)
#if BOOST_WORKAROUND(__BORLANDC__, < 0x600)
// without this, Borland's compiler gives the wrong answer for
// references to arrays:
BOOST_TT_AUX_BOOL_TRAIT_PARTIAL_SPEC2_1(typename T,is_same,T&,T&,true)
#endif

} // namespace boost_1_56_0

#include <boost_1_56_0/type_traits/detail/bool_trait_undef.hpp>

#endif  // BOOST_TT_IS_SAME_HPP_INCLUDED

redpajama_set_name: RedPajamaGithub


text:
Champions Speakers
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Ian Bell MBE
PLAYED OVER 100 TEST MATCHES FOR ENGLAND SCORING A REMARKABLE 22 CENTURIES AND 42 HALF CENTURIES. PLAYING A VITAL ROLE IN THE UNFORGETTABLE 2005 ASHES SERIES VICTORY
British cricket speaker and England batsman Ian Bell MBE is one of few players to score a century against all current test playing nations and whose success garnered a place on the 2006 New Year's Honours List where he was given an MBE for his services to British sport and work as a cricket speaker.
Born in Coventry Ian Bell MBE has played for his local domestic team, Warwickshire Cricket Club since he was 16 years' old making his first-class debut in 1999, Bell finally made it into the Warwickshire first team in 2001 and has continued to work as a batsman and cricket speaker for the team ever since. Beyond his commitments as a player Bell has also worked as a sports speaker on Warwick's Cricket Board since 1999 and in 2005 embarked on a four-year stint with Marylebone Cricket Club.
Bell currently holds the record for Warwickshire's youngest player to score a test century, a score of 130 against Oxford UCCE at 19 years and 56 days old, and spent much of his youth career known as the greatest under 17 player of his generation and became the county's youngest ever capped player; Bell frequently appears as an after dinner speaker for his local club and is beloved by fans and Warwickshire locals.
Bell's success has continued since graduating into the Senior League with a number of impressive appearances as part of the English International Team. Ian Bell MBE made his first appearance in the England Test team in 2004 when he was brought in to replace an injured Mark Butcher on the New Zealand tour. Bell has since appeared in over 100 Test Matches and scored 42 Test 50's, thanks to his part in the 2005 Ashes Tour Bell received an MBE and in 2006 was named the International Cricket Council's Emerging Player of the Year .
Bell holds a Test batting average of 45.00 and is frequently called upon to appear at after dinner speaker events for the International cricket team.
As well as demonstrating success as a Test cricket player Ian Bell MBE also boasts three ODI centuries and almost 150 ODI appearances; his work as a sports speaker and cricket player has taken him around the world and Bell has competed against some of the most acclaimed international cricketers of the last ten years.
To book Ian Bell MBE as the cricket speaker for your corporate event, function or conference, simply contact the Champions Speakers agency using the contact details below.
Enquiry Form0207 1010 553agent@championsukplc.com
When at corporate events, Ian is known to cover the following topics:
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In saying that, I would highly recommend you and Champions too. All your dealings in the process of this booking have been highly professional and extremely helpful to us as we have sought to make a b
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text:
WEBB-FOSTER SIGNS HARROGATE LOAN DEAL
Bantams striker Reece Webb-Foster has joined Vanarama National League North side Harrogate Town on loan.
Webb-Foster has penned a loan deal at The CNG Stadium which will keep him with Harrogate until 10 December.
Having completing the necassary paperwork for the deal, Webb-Foster could now be in line to make his debut for Harrogate in their Emirates FA Cup Fourth Qualifying Round tie with FC Halifax Town this Saturday.
Harrogate are currently second in the Vanarama National League North table, four points behind unbeaten league leaders AFC Fylde.
Webb-Foster has already enjoyed one loan spell away from the Northern Commercials Stadium this season, playing five times and scoring once for National League side Guiseley.
The 18 year-old has also featured three times for City so far this season, starting the Checkatrade Trophy win over Stoke City and coming off the bench against Accrington Stanley in the EFL Cup and in the league against Coventry.


We currently work exclusively with several property management companies to help them prepare their commercial and residential properties for lease. Inevitably there are always tenants who leave some junk behind, and that's where we come in. We provide same day or next day service so that you can have the property ready for immediate lease.
To schedule an appointment, call 1-800-586-5735 or Book Online and our professional crew will schedule a time that works for you.


text:
MTV Entertainment Elevates Nina L. Diaz to Head Unscripted, Adult Animation for Paramount Plus
Nina L. Diaz (Courtesy of VH1)
Nina L. Diaz will now head up unscripted and adult animation for Paramount Plus, Variety has learned exclusively.
The promotion was announced by Chris McCarthy in a memo to MTV Entertainment Group staffers on Thursday, which can be read in full below.
Diaz will now serve as chief creative officer for unscripted entertainment and adult animation at Paramount Plus, while remaining CCO and president of content for MTV Entertainment Group. She will continue to report to McCarthy. In addition, Grant Gish's role as head of adult animation is expanding to include Paramount Plus across series and movies, with Gish still reporting to Diaz.
Furthermore, Lily Neumeyer will now be head of development for unscripted entertainment for Paramount Plus and MTV Entertainment, while Tolani Holmes is being promoted to the new role of senior vice president for development of unscripted entertainment for Paramount Plus and MTV Entertainment.
Unscripted executives Ben Hurvitz, Sitarah Pendelton, Todd Radnitz have all been given expanded roles as well, overseeing various key unscripted franchises for the company.
Per McCarthy's memo, this reorganization means that the company will no longer have "stand-alone Movie or Alternative teams." Given that, both Meghan Hooper White and Justin Rosenblatt are exiting ViacomCBS.
"We would like to thank both Meghan and Justin for their contributions and wish them the very best in their endeavors," McCarthy wrote. "Thankfully, both have strong teams who will be integrated into our content group as we align series and movies by genre."
Hooper White served as executive vice president and head of original movies and limited series for ViacomCBS' Entertainment and Youth Group. She first joined ViacomCBS just over a year ago in May 2020 from A+E Networks with a mandate to expand the company's production of made-for-TV movies. She was set to create 100 films annually for the conglomerate's various network brands, including MTV, Comedy Central, Paramount Network, VH1, CMT and the Smithsonian Channel.
In particular, Paramount Network was seen as a central hub for the aforementioned films. Variety exclusively reported in September 2020 that ViacomCBS was rebranding the network, shifting its focus from long form scripted shows to original movies. The media conglomerate had originally said that it would program one movie a week, or 52 a year, on the cable network. Yet one year later, few of the films have materialized as ViacomCBS has reorganized its ranks to funnel content and resources to streaming service Paramount Plus.
Rosenblatt was the senior vice president of alternative and comedy content for the Entertainment and Youth Group, having been promoted to that role in May 2020. He previously worked on shows like "Schitt's Creek" at Pop and brought the "One Day at a Time" reboot to that network after it was canceled at Netflix. He started out at MTV2, overseeing shows like "Celebrity Deathmatch."
Following up on the Paramount+ content leadership announcement from Bob, I am thrilled to share with you our first set of key appointments to support our group's leadership role in driving Unscripted Entertainment and Adult Animation for the streaming service. This note will focus on the leadership level of the Development and Originals teams in those respective areas with others to follow.
• Nina L. Diaz will serve as Chief Creative Officer for Unscripted Entertainment and Adult Animation at Paramount+, expanding her current role as CCO/President of Content for MTV Entertainment Group. Nina will continue to report to me.
• Grant Gish will expand his role as Head of Adult Animation for MTVE to now include Paramount+ across Series and Movies. Grant will continue to report to Nina.
• Lily Neumeyer will expand her role to now include Head of Development for Unscripted Entertainment, Paramount+ and MTVE, continuing to report to Nina.
• Tolani Holmes is being promoted to the new role of SVP for Development, Unscripted Entertainment, Paramount+ and MTVE, reporting to Lily.
Also, we are expanding the role of several key executives to oversee our growing number of key franchises that are fueling our success across Paramount+ and MTV Entertainment worldwide and will continue to report to Nina. These leaders include: • Ben Hurvitz will expand his role to SVP, Unscripted Entertainment Franchises, Paramount+ and MTVE, overseeing Teen Mom, Basketball Wives, and 16 & franchises, as well as Ink Master.
• Sitarah Pendelton will expand her role to SVP, Unscripted Entertainment Franchises, Paramount+ and MTVE, overseeing Love & Hip Hop, Catfish and Are You the One? franchises as well as Behind the Music and The Real World Homecoming.
• Todd Radnitz will expand his role to SVP, Unscripted Entertainment Franchises, Paramount+ and MTVE, overseeing The Hills, Double Shot at Love and True Crime, RuPaul's Drag Race, and the upcoming Queen of the Universe.
With this realignment around Paramount+ Unscripted Entertainment and Adult Animation, we will no longer have stand-alone Movie or Alternative teams. As a result, Meghan Hooper White and Justin Rosenblatt are exiting the organization. We would like to thank both Meghan and Justin for their contributions and wish them the very best in their endeavors.
Thankfully, both have strong teams who will be integrated into our content group as we align series and movies by genre.
Please join me in congratulating these creative leaders on their well-deserved appointments and thank you all for your continued passion and hustle.
Variety's Joe Otterson contributed to this post.
https://variety.com/2021/tv/news/meghan-hooper-white-viacomcbs-1235037581/#recipient_hashed=27d3e1decbdd57f8473998652dd3259302a49d011cd4c3c98101f80952412574
MTV Entertainment
MTV Entertainment Group
Grant Gish
Lily Neumeyer
Tolani Holmes
Ben Hurvitz
Sitarah Pendelton
Todd Radnitz
Meghan Hooper White
Justin Rosenblatt


Since I got banned from DSHack.org, I decided to go here instead to find a website which was all about hacking with accounts. I was here before I even logged on to this website!
gridatttack And the point is?
gridatttack Youtube fails when telling when they are active. I just literally finished seeing a video.
NSMB But I saw it.
Arisotura So uh, you hack yourself I guess?
Dutch train dude Happy birthday!!