The previous installments in this series have discussed how to ask answerable clinical questions and then search for the best evidence addressing those questions. Not all evidence is of high enough quality to provide meaningful information for patient care, however, and it is important to evaluate all studies with a critical eye toward study design and analysis.
A study can be flawed in many ways, and while many flaws still allow us to apply study results to patients, we need to understand these limitations. It is also insufficient to trust factors such as a medical journal’s impact factor or prestige: Many examples of suboptimal evidence come from higher-tier journals, and it has been estimated that even in the top internal medicine journals up to 50% of papers contain significant design and analysis errors.
While the growth of EBM has directed increasing attention to these issues, the onus remains on the literature consumer to critically appraise the evidence in order to make treatment decisions in as informed a manner as is possible.
Results from a valid study can be expected to be unbiased. In other words, these results should portray the true underlying effect of interest. There are many threats to a study’s validity. Such factors must be evaluated to ensure that they do not systematically affect results and therefore alter the correct interpretation of study findings.
The primary goal of any unbiased study design is to make the comparison groups as similar as possible for all factors potentially affecting the outcome of interest—except for the intervention or exposure of interest. If the only difference between groups’ histories, comorbidities, study experiences, and so on is the intervention or exposure, we can be more confident that any observed outcome differences are due to the exposure rather than other confounding variables.
For example, consider a trial of treatment options for esophageal cancer in which twice as many control group patients smoked as in the intervention group. If the intervention group had better outcomes, we would not know whether this was due to the intervention or to the lower smoking rates in the treatment arm of the study. A well-designed, valid study will make every effort to minimize such problems. This principle applies to all study designs, including observational designs such as case-control and cohort studies, and experimental designs such as the classic randomized controlled trial. We will briefly present a few of the key threats to study validity in this segment of the series. We will focus on clinical trial designs, but the same principles apply to observational designs as well.
Minimize Bias and Protect Study Validity
Randomization: If we wish to make study groups similar on all variables other than the exposure of interest, and we can assign interventions such as in a clinical trial, we can maximize validity by appropriately randomizing patients to intervention groups. Randomization has the effect of balancing comparison groups with respect to both recognized and unrecognized factors that may affect outcomes.
A key feature to look for in a randomization procedure is that the randomization algorithm is in fact completely random. It should be impossible to predict for any study subject to which group they will be randomized. Therefore, for example, procedures systematically alternating subject assignments among groups (A-B-A-B- … ) are not truly random and do not confer the validity benefits of true randomization. It is also important that the randomization process be separate from all other aspects of the study, so that no other factors may influence group assignment. This is closely related to the concept of blinding.