Mounting evidence suggests that there is frequently considerable variation in the risk of the outcome PLX-4720 of interest in clinical trial populations. patient characteristics modify the effects of treatment are usually unable to detect even large variations in treatment benefit (and harm) across risk groups because they do not account for the fact that patients have multiple characteristics simultaneously that affect the likelihood of treatment benefit. Based upon recent evidence on optimal statistical approaches to assessing HTE we propose a framework that prioritizes the analysis and reporting of multivariate risk-based HTE and suggests that other subgroup analyses should be explicitly labeled either as primary subgroup analyses (well-motivated by prior evidence and intended to produce clinically actionable results) or secondary (exploratory) subgroup Rabbit Polyclonal to CDK5RAP2. analyses (performed to inform future research). A standardized and transparent approach to HTE assessment and reporting could substantially improve clinical trial utility and interpretability. Introduction When the Scottish epidemiologist Archie Cochrane suggested that clinical practice should principally be guided by rigorously designed evaluations in particular randomized clinical trials (RCTs) the reaction of the medical profession was largely unfavorable. Critics suggested that relying on impersonal statistically-derived “evidence” based on averages to determine clinical decision-making was antithetical to the practice of medicine which should rather be based on a physician’s expertise acumen and clinical experience and on knowing the individual patient and considering what is best for each person given their individual circumstances and needs [1-3]. Although “evidence-based medicine” has become the dominant paradigm for shaping clinical recommendations and guidelines recent work demonstrates that many clinicians’ initial concerns about “evidence-based medicine” come from the very real incongruence between the overall effects of a treatment in a study population (the summary result of a clinical trial) and deciding what treatment is best for an individual patient given their specific condition needs and desires (the task of the good clinician) [4-7]. The answer however is usually not to accept clinician or expert opinion as a replacement for scientific evidence for estimating a treatment’s efficacy and safety but to better understand how the effectiveness and safety of a PLX-4720 treatment varies across the patient population (referred to as heterogeneity of treatment effect [HTE]) so as to make optimal decisions for each patient. The conventional method of examining whether treatment effects vary in a trial population is to divide patients into subgroups based on potentially influential characteristics. The main problem with the conventional approach is that there are too many characteristics that can potentially influence treatment effect. This leads to myriad subgroup analyses which are typically both underpowered and vulnerable to spurious false positive results due to multiple comparisons. For these reasons subgroup analyses are usually “exploratory” and rarely actionable leaving the clinician to PLX-4720 assume that all patients meeting trial inclusion criteria should be similarly treated. Herein we propose a framework that directly addresses the problem PLX-4720 of multiplicity in two ways. First our framework prioritizes the analysis and reporting of multivariate risk-based HTE over conventional “one-variable-at-a-time” subgroup analysis. This recommendation is based on an understanding that HTE emerges from just a few fundamental risk dimensions. These dimensions–which include the risk of the primary study outcome (the main focus of our proposed approach) competing risk the risk of treatment-related harm and direct treatment-effect modification [5-8]–can often be summarized using multivariate prediction models greatly simplifying subgroup analyses and substantially improving statistical power. Second this framework proposes that other subgroup analyses should be explicitly labeled either as primary subgroup PLX-4720 analyses (well-motivated by prior evidence and intended to.