Hypotheses about exposures and disease may be developed from descriptive studies that should then be explored in analytic studies. Because a case series has no comparison group, statistical tests of association between the exposure and outcome cannot be performed. A case series usually does not yield any measure of association other than estimates of the frequency of a particular characteristic among members included in the case series.
Presenting and Understanding the Results of Analytic Studies
To present the results of clinical trials or observational studies, a variety of rates and measures may be derived, as summarized below. To judge the scientific validity of the results of clinical studies, an investigator needs to consider whether the finding could have occurred simply by chance, by performing appropriate statistical testing, or if there are other possible explanations for the reported association, including bias or confounding. Besides statistical significance and freedom from bias or confounding, there are several additional criteria that can be applied to judge whether the treatment truly did affect disease outcome or whether an exposure truly caused the disease, as outlined below.
Rates and Measures The terminology associated with rates and measures include (Fig. 4.5):
Incidence (IR)—frequency of newly identified disease or event (outcome).
Prevalence (PR)—frequency of an existing disease or outcome during a specified period or point in time.
Odds Ratios (OR)—ratio of the probability of an exposure in one group (cases) compared with probability of the exposure in another group (controls).
Relative Risk (RR)—ratio of risk in the exposed group compared with the risk in the unexposed group. If the RR = 1 (or not significantly different from 1) then the risk in the exposed group is equal to the risk in the nonexposed group. RR >1 may suggest a positive association with the exposed group having greater risk than the nonexposed group, whereas a RR <1 implies a negative association with the exposed group having less risk than the nonexposed group.
Absolute Risk Reduction (ARR)—the difference in risk between the unexposed (control) group and the exposed (treatment) group.
 Overall Incidence Rate (IR): (a + c) (a + b + c + d)  Odds Ratio (OR): (a/b) (c/d)  Relative Risk (RR): (a/(a + b)) (d/(c + d))  Relative Risk Reductions (RRR): 1-RR x 100%  Absolute Risk Reduction (ARR): (a/(a +b)) – (d/(c + d))  Number Needed to Treat (NNT): 1 ARR
Figure 4.5 Calculating rates and measures.
Relative Risk Reduction (RRR)—the percentage of reduction in the risk comparing the unexposed (control) group to the exposed (treatment) group.
Number Needed to Treat (NNT)—represents the number of people who would need treatment (or the intervention) to prevent one additional outcome (to calculate the NNT, take the inverse of the ARR, i.e., 1 ÷ ARR).
Sensitivity—among the people who have the outcome, this is the proportion who have a positive test.
Specificity—among the people who do not have the outcome, this is the proportion who have a negative test.
Negative Predictive Value (NPV)—among the people who have a negative test, this is the proportion who do not have the outcome.
Positive Predictive Value (PPV)—among the people who have a positive test, this is the proportion who have the outcome.
Statistical Testing Statistical testing is used in clinical research for hypothesis testing in which the investigator is evaluating the study results against the null hypothesis (that there is no difference between the groups). Results from statistical testing allow the investigator to evaluate how likely it is that the study result is caused by chance rather than an intervention or exposure (p value). In the case where a study failed to find a significant difference, it is equally important to describe the likelihood that the study conclusion was wrong and that a difference truly exists. Finally, it is important to provide as precise a measure of the treatment effect or association as possible and convey to the reader the plausible range that the “true” effect resides (or confidence interval).