***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Reports of cluster r and omised trials require additional information to allow readers to interpret them accurately The effective reporting of r and omised controlled trials has received useful attention in recent years . Many journals now require that reports conform to the guidelines in the Consoli date d St and ards of Reporting Trials ( CONSORT ) statement , first published in 1996 and revised in 2001 . The statement includes a checklist of items that should be included in the trial report . These items are evidence based whenever possible and are regularly review ed . The statement also recommends including a flow diagram to show the flow of participants from group assignment through to the final analysis . The CONSORT statement focused on reporting parallel group r and omised trials in which individual participants are r and omly assigned to study groups . However , in some situations it is preferable to r and omly assign groups of individuals ( such as families or medical practice s ) rather than individuals . Reasons include the threat of contamination of some interventions ( such as dietary interventions ) if individual r and omisation is used . 5 Also , in certain setting s r and omisation by group may be the only feasible method of conducting a trial . Trials with this design are variously known as field trials , community based trials , place based trials , or ( as in this paper ) cluster r and omised trials . In an earlier discussion paper we considered the implication s of the CONSORT statement for the reporting of cluster r and omised trials . Here we present up date d guidance , based on the 2001 revision of the CONSORT statement IMPORTANCE In sub-Saharan Africa , malaria is a leading cause of childhood morbidity and iron deficiency is among the most prevalent nutritional deficiencies . In 2006 , the World Health Organization and the United Nations Children 's Fund released a joint statement that recommended limiting use of iron supplements ( tablets or liquids ) among children in malaria-endemic areas because of concern about increased malaria risk . As a result , anemia control programs were either not initiated or stopped in these areas . OBJECTIVE To determine the effect of providing a micronutrient powder ( MNP ) with or without iron on the incidence of malaria among children living in a high malaria-burden area . DESIGN , SETTING , AND PARTICIPANTS Double-blind , cluster r and omized trial of children aged 6 to 35 months ( n = 1958 living in 1552 clusters ) conducted over 6 months in 2010 in a rural community setting in central Ghana , West Africa . A cluster was defined as a compound including 1 or more households . Children were excluded if iron supplement use occurred within the past 6 months , they had severe anemia ( hemoglobin level < 7 g/dL ) , or severe wasting ( weight-for-length z score < -3 ) . INTERVENTIONS Children were r and omized by cluster to receive a MNP with iron ( iron group ; 12.5 mg/d of iron ) or without iron ( no iron group ) . The MNP with and without iron were added to semiliquid home-prepared foods daily for 5 months followed by 1-month of further monitoring . Insecticide-treated bed nets were provided at enrollment , as well as malaria treatment when indicated . MAIN OUTCOMES AND MEASURES Malaria episodes in the iron  group compared with the no iron group during the 5-month intervention period . RESULTS In intention-to-treat analyses , malaria incidence overall was significantly lower in the iron group compared with the no iron group ( 76.1 and 86.1 episodes/100 child-years , respectively ; risk ratio ( RR ) , 0.87 [ 95 % CI , 0.79 - 0.97 ] ) , and during the intervention period ( 79.4 and 90.7 episodes/100 child-years , respectively ; RR , 0.87 [ 95 % CI , 0.78 - 0.96 ] ) . In secondary analyses , these differences were no longer statistically significant after adjusting for baseline iron deficiency and anemia status overall ( adjusted RR , 0.87 ; 95 % CI , 0.75 - 1.01 ) and during the intervention period ( adjusted RR , 0.86 ; 95 % CI , 0.74 - 1.00 ) . CONCLUSION AND RELEVANCE In a malaria-endemic setting in which insecticide-treated bed nets were provided and appropriate malaria treatment was available , daily use of a MNP with iron did not result in an increased incidence of malaria among young children . TRIAL REGISTRATION clinical trials.gov Identifier : NCT01001871 In a cluster r and omized controlled trial , Anthony Shakeshaft and colleagues measure the effectiveness of a multi-component community-based intervention  for reducing alcohol-related harm Loss to follow-up is often hard to avoid in r and omised trials . This article suggests a framework for intention to treat analysis that depends on making plausible assumptions about the missing data and including all participants in sensitivity Abstract s Background The objective of this simulation study is to compare the accuracy and efficiency of population -averaged ( i.e. generalized estimating equations ( GEE ) ) and cluster-specific ( i.e. r and om-effects logistic regression ( RELR ) ) models for analyzing data from cluster r and omized trials ( CRTs ) with missing binary responses . Methods In this simulation study , clustered responses were generated from a beta-binomial distribution . The number of clusters per trial arm , the number of subjects per cluster , intra-cluster correlation coefficient , and the percentage of missing data were allowed to vary . Under the assumption of covariate dependent missingness , missing outcomes were h and led by complete case analysis , st and ard multiple imputation ( MI ) and within-cluster MI strategies . Data were analyzed using GEE and RELR . Performance of the methods was assessed using st and ardized bias , empirical st and ard error , root mean squared error ( RMSE ) , and coverage probability . Results GEE performs well on all four measures — provided the downward bias of the st and ard error ( when the number of clusters per arm is small ) is adjusted appropriately — under the following scenarios : complete case analysis for CRTs with a small amount of missing data ; st and ard MI for CRTs with variance inflation factor ( VIF ) <3 ; within-cluster MI for CRTs with VIF≥3 and cluster size>50 . RELR performs well only when a small amount of data was missing , and complete case analysis was applied . Conclusion GEE performs well as long as appropriate missing data strategies are adopted based on the design of CRTs and the percentage of missing data . In contrast , RELR does not perform well when either st and ard or within-cluster MI strategy is applied prior to the analysis Background Intention-to-treat analysis is used in the analysis of r and omized controlled trials to preserve trial power in the presence of missing subject data as well as to control for both known and unknown confounding factors . One form of intention-to-treat analysis is last-observation-carried-forward ( LOCF ) . Concerns exist regarding whether it is appropriate to use LOCF in analyses involving progressive conditions or in situations where missing data are non-r and om ( e.g. , subjects drop out because of treatment side effects or differing disease severity ) . Objective To examine the use of intention-to-treat imputation of missing data techniques , and specifically LOCF , in r and omized controlled trials of the use of  cholinesterase inhibitors and memantine to treat Alzheimer ’s disease , vascular dementia , mixed dementia and mild cognitive impairment . Methods We conducted a systematic electronic search of MEDLINE and the Cochrane Central Register of Controlled Trials from 1984 to 2008 for double-blinded , r and omized controlled trials of cholinesterase inhibitors or memantine that examined progressive symptoms in Alzheimer ’s disease , vascular dementia , mixed dementia and mild cognitive impairment . We collected data on the use of intention-to-treat and non-intention-to-treat analyses and on contraindications to the use of LOCF analysis and we performed quality assessment s of included trials . Results Of the 57 studies that met the inclusion criteria , 12 did not report intention-to-treat analyses . Of the 34 studies that employed LOCF as the only form of intention-to-treat analysis , 24 reported conditions that could produce biased LOCF analyses favouring the drug under study . The latter finding was more common in cholinesterase inhibitor trials than in memantine studies . Conclusions The published results of some r and omized controlled trials of dementia drugs may be inaccurate ( i.e. , drug effectiveness may be exaggerated ) or invalid ( i.e. , there may be false-positive results ) because of bias introduced through the inappropriate use of LOCF analyses . This bias favours cholinesterase inhibitors , potentially preventing funding of and patient access to less toxic treatment options such as memantine . Licensing agencies should consider whether to accept LOCF analyses in research on dementias and other chronic progressive conditions BACKGROUND Cluster r and omized trials increasingly are being used in health services research and in primary care , yet the majority of these trials do not account appropriately for the clustering in their analysis . OBJECTIVES We review the main implication s of adopting a cluster r and omized design in primary care and highlight the practical application of appropriate analytical techniques . METHODS The application of different analytical techniques is demonstrated through the use of empirical data from a primary care-based case study . CONCLUSION Inappropriate analysis of cluster trials can lead to the presentation of inaccurate results and hence potentially misleading conclusions . We have demonstrated that adjustment for clustering can be applied to real-life data and we encourage more routine adoption of appropriate analytical techniques Background Attrition , which leads to missing data , is a common problem in cluster r and omized trials ( CRTs ) , where groups of patients rather than individuals are r and omized . St and ard multiple imputation ( MI ) strategies may not be appropriate to impute missing data from CRTs since they assume independent data . In this paper , under the assumption of missing completely at r and om and covariate dependent missing , we compared six MI strategies which account for the intra-cluster correlation for missing binary outcomes in CRTs with the st and ard imputation strategies and complete case analysis approach using a simulation study . Method We considered three within-cluster and three across-cluster MI strategies for missing binary outcomes in CRTs . The three within-cluster MI strategies are logistic regression method , propensity score method , and Markov chain Monte Carlo ( MCMC ) method , which apply st and ard MI strategies within each cluster . The three across-cluster MI strategies are propensity score method , r and om-effects ( RE ) logistic regression approach , and logistic regression with cluster as a fixed effect . Based on the community hypertension assessment trial ( CHAT ) which has complete data , we design ed a simulation study to investigate the performance of above MI strategies . Results The estimated treatment effect and its 95 % confidence interval ( CI ) from generalized estimating equations ( GEE ) model based on the CHAT complete data set are 1.14 ( 0.76 1.70 ) . When 30 % of binary outcome are missing completely at r and om , a simulation study shows that the estimated treatment effects and the corresponding 95 % CIs from GEE model are 1.15 ( 0.76 1.75 ) if complete case analysis is used , 1.12 ( 0.72 1.73 ) if within-cluster MCMC method is used , 1.21 ( 0.80 1.81 ) if across-cluster RE logistic regression is used , and 1.16 ( 0.82 1.64 ) if st and ard logistic regression which does not account for clustering is used . Conclusion When the percentage of missing data is low or intra-cluster correlation coefficient is small , different approaches for h and ling missing binary outcome data generate quite similar results . When the percentage of missing data is large , st and ard MI strategies , which do not take into account the intra-cluster correlation , underestimate the variance of the treatment effect . Within-cluster and across-cluster MI strategies ( except for r and om-effects logistic regression MI strategy ) , which take the intra-cluster correlation into account , seem to be more appropriate to h and le the missing outcome from CRTs . Under the same imputation strategy and percentage of missingness , the estimates of the treatment effect from GEE and RE logistic regression models are similar Sometimes interventions in r and omized clinical trials are not allocated to individual patients , but rather to patients in groups . This is called cluster allocation , or cluster r and omization , and is particularly common in health services research . Similarly , in some types of observational studies , patients ( or observations ) are found in naturally occurring groups , such as neighborhoods . In either situation , observations within a cluster tend to be more alike than observations selected entirely at r and om . This violates the assumption of independence that is at the heart of common methods of statistical estimation and hypothesis testing . Failure to account for the dependence between individual observations and the cluster to which they belong can have profound implication s on the design and analysis of such studies . Their p-values will be too small , confidence intervals too narrow , and sample size estimates too small , sometimes to a dramatic degree . This problem is similar to that caused by the more familiar " unit of analysis error " seen when observations are repeated on the same subjects , but are treated as independent . The purpose of this paper is to provide an introduction to the problem of  clustered data in clinical research . It provides guidance and examples of methods for analyzing clustered data and calculating sample sizes when planning studies . The article concludes with some general comments on statistical software for cluster data and principles for planning , analyzing , and presenting such studies Introduction  Cluster r  and  omised trials ( CRTs ) r and omise participants in groups , rather than as individuals , and are key tools used to assess interventions in health research where treatment contamination is likely or if individual r and omisation is not feasible . Missing outcome data can reduce power in trials , including in CRTs , and is a potential source of bias . The current review focuses on evaluating methods used in statistical analysis and h and ling of missing data with respect to the primary outcome in CRTs . Methods and analysis We will search for CRTs published between August 2013 and July 2014 using PubMed , Web of Science and Psyc 
    Output:
    
Conclusions High rates of missing outcome data are present in the majority of CRTs , yet h and ling missing data in practice remains suboptimal .