Reanalysis or redefinition of the hypothesis?

Though a seemingly simple notion, defining a clear research question or hypothesis at the conception of a clinical study can be a daunting task for investigators. Through the TRILOGY ACS study, Bakal and colleagues illustrate that subtle changes in the primary outcome definition foraclinical trialmayresult in substantial changes in theanalytical methods, altering the overall interpretation of the study’s result. Perhaps as an unintended consequence, their article also emphasizes a pitfall of post-hoc analyses. This editorial is intended to provide some insight into the statistical considerations in their article. In doing so, it aggregates their ideas to provide recommendations for appropriate analytical techniques driven by specific research questions. Most disease progressions are associated with the realization of multiple complications. Cardiovascular disease, for example, may have elevated blood pressure, atherosclerosis, altered perfusion, ischaemia, changes in activities of daily living, etc. From a clinical perspective, a myocardial infarction resulting in death may be more important than reduced cardiopulmonary function that only limits a patient’s activities of daily living. That said, these extreme examples are intrinsically linked in a common disease process and represent different aspects of disease progression. These inter-relationships, when simultaneously modelled, often introduce the need for more sophisticated statistical analyses. These analyses can sometimes obfuscate the findings of the study. Thus, investigators often seek to balance clinical relevance with interpretability of the primary outcome measure. Composite outcome measures help with this process. The TRILOGYACS study, like manycontemporary clinical trials, utilized a composite primary outcome measure of time-to(first)occurrence of any of the following: death, myocardial infarction, or stroke. The appropriate statistical analysis based on this outcome compares the time-to-first-event across intervention arms without differentiating event severities (e.g. a cardiovascular death from a mild, non-disabling stroke). This analytical method addresses a research question that assumes that the severity of any such event in relation to others is unimportant, and the true question pertains to ‘event-free survival’. Composite endpoints may be useful not only when the investigator is interested in event-free survival, but also when investigators cannot choose between equally important event definitions, the events or components all carry equal weight such that they may be exchangeable, and there is concern of type I error rate inflation due to multiple testing of multiple endpoints. By pooling the occurrence of any events, a trial typically gains power by increasing the number of events observed that may contribute to analyses. On the other hand, power may further increase if analyses account for any recurrent events (i.e. those after the first event where the first event was not death). For this scenario, the authors recognize the utility of the ‘Anderson–Gill’ method. Another challenge we face with the use of composite endpoints in the traditional manner lies in the difficulty in interpretation when components exhibit effects in different directions and/or magnitudes across intervention arms. Further, the researchquestionmay pertainnot only to time-to-event, but also to overall event counts across arms. Bakal and colleagues discuss the Anderson–Gill method as a potential alternative to the traditional survival analysis techniques since it uses all events (e.g. death, myocardial infarction, and stroke) from the TRILOGY ACS study to contribute to analyses (table 2 in Bakal et al.). Nevertheless, the authors caution that this method also carries drawbacks in that it treats all events as exchangeable with one another (i.e. death and a mild stroke may be given equal weight), and the methods may yield an overall biased result. In fact, the reanalysis of the TRILOGY ACS data using this method results in a statistically significant difference between intervention arms while the other methods did not. The reader is left to speculate on the possibility of a type I error. The authors have proposed an alternative approach in the weighted composite endpoints (WCEs) in order to combat the shortcomings noted with the Anderson–Gill and traditional methods. The WCE, as the name implies, does not adhere to the traditional composite definition and eachcomponent of the composite outcome carries a weight. Analyses for this type of outcome address the research question pertaining to time-to-event and