Welcome to the SIAM Student Chapter at Tufts!
The Chapter promotes research in applied mathematics for the graduate and undergraduate community at Tufts, provides exposure to different areas of applied mathematics, and encourages an environment where students can share scientific interests.
All meetings are held in either the Clarkson Conference Room or Classroom 101 in Bromfield-Pearson, unless otherwise noted.
Upcoming EventsOctober 23th, 2013, 12:00PM
Dr. David Kent, Associate Professor of Medicine, Tufts University
Heterogeneity of Treatment Effect: A General Framework
The conventional method of examining whether treatment effects vary in a trial population is to serially divide patients into subgroups based on potentially influential characteristics. The main problem with the conventional approach is that there are too many potentially influential characteristics. This leads to myriad “one-variable-at-a-time” 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 assume that all patients meeting trial inclusion criteria should be similarly treated. Evidence-based medicine (EBM) is thus methodologically canalized to “one-size-fits-all” recommendations, a problem increasingly recognized even as EBM has become the dominant paradigm.
At the same time, mounting evidence suggests that there is frequently considerable variation in the risk of the outcome of interest in clinical trial populations. These differences in risk will often cause clinically important heterogeneity in treatment effects (HTE) across the trial population, such that the balance between treatment risks and benefits may differ substantially between large identifiable patient subgroups; the “average” benefit observed in the summary result may even be non-representative of the treatment effect for a typical patient in the trial.
In this session, I will discuss the issues above from both a theoretical and empirical perspective and present a recently proposed framework for assessing heterogeneity of treatment effect (HTE) that addresses these issues. The framework 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 analyses (performed to inform future research).
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