Commentary on Murray and Hernán

Randomization constructs groups of participants with nearly the same distribution of measured and unmeasured baseline variables. To exploit the benefit of this similarity, the analysis of the data must respect the random allocation. The strict intention-to-treat (ITT) population comprises all participants randomized into the study; a valid ITT analysis is one that includes them all. This strict ITT analysis is not, however, the only valid analysis; subgroups defined by pre-randomization variables (as long as the analyst doesn’t choose a promising subgroup after looking at the data) can yield rigorous answers to other questions of interest. We must resist, however, basing analyses on ‘‘improper subgroups’’— those defined by post-baseline variables. Among the most egregious of such improper subgroups are the socalled ‘‘per protocol’’ (PP) analyses defined by adherence to the study intervention. Because adherence leads to selection bias, simple PP analyses cannot disentangle the reason for nonadherence from the effect of the intervention. Nonadherence may be related to toxicity or to the perception of lack of benefit; both are effects of intervention. In a book he was preparing during the last months of his life, David Sackett wrote, ‘‘‘[P]er-protocol’ analyses confined to compliant patients are inherently invalid, and I consider them nefarious when carried out by folks who know better. ’’ Many of us ‘‘folks who know better’’ would like, however, to learn how a treatment affects an adherent patient. A drug cannot benefit patients who never take it; moreover, a drug’s effectiveness is probably related to how religiously patients adhere to the assigned regimen. In recent decades, several methods have emerged to correct for selection bias. Heckman’s 1979 Nobel prize in economics cited methods dealing with selection bias in observational studies. Robins and co-authors have proposed inverse probability weighting as one approach to solving the problem in clinical trials. In this issue, Murray and Hernán (henceforth, M&H) argue that although naı̈ve PP analysis is biased, the limitation of a naı̈ve approach should not preclude addressing non-adherence; rather, clinical trialists should use modern statistical methods to adjust for post-randomization variables. M&H ask whether the reason for the reluctance of trialists to embrace modern methods of adjusting for selection bias is an unfortunate fall-out of a seminal paper that demonstrated a very large difference between adherers and non-adherers to placebo in the Coronary Drug Project (CDP). M&H report that in training new clinical trialists, many courses point to this article to warn against the analysis by adherence. An important contribution of the 1980 paper was the insight of Paul Canner, the Principal Statistician of the CDP, to look at the placebo patients in assessing the value of adherence. His finding that adherers and nonadherers to placebo had different event rates demonstrated that adherence itself is a marker, not a predictor, of outcome because clearly placebo causes neither benefit nor harm. M&H reanalyze the CDP data using different statistical methods, some of which were not available when the CDP paper was written. These new analyses show a much smaller difference between adherers and nonadherers to placebo than reported in the 1980 paper; in fact, after applying all the new methods, the authors find the difference no longer statistically significant. M&H imply that if the reluctance of clinical trialists to consider PP analysis was due to the very large effect of adherence to placebo seen in the 1980 paper, their new analyses should convince trialists that modern methods can perform the necessary disentangling. Thus, if analyses are done correctly, M&H contend that one can perform PP analyses and, because of the importance of knowing how a drug affects those who take it, a valid PP analysis should accompany the reporting of the results of clinical trials. Many of us, even prior to the 1980 publication, avoided analyses restricted to adherers because, if one believes in the importance of comparing groups that are similar, one cannot measure the effect of treatment in post-randomization subgroups. Response to treatment itself could affect adherence, a post-randomization variable. Thus, wistful as we were about ignoring adherence, we were interested in how populations would