Subgroup analyses, long considered to be unreliable at best and misleading at worst, have come back to the forefront of statistical methodology. The underlying cause for this revived interest is the inadequacy of the assumption of a fairly homogeneous treatment effect across all patients for some classes of treatments whose effects are modulated by specific molecular mechanisms that differ widely between patients. In molecular medicine, heterogeneity of treatment effect is the norm rather than the exception, which naturally leads to the concept of “precision” or “individualized” medicine.
This talk will discuss recently proposed taxonomies of the purposes of, and methods for, subgroup analyses (1,2). New methodologies are available for the reliable identification of subgroups with truly different treatment effects, and – more importantly – for the prospective validation of these subgroup findings prospectively, using various novel trial designs. These will be illustrated using real-life examples.
While the methodology of subgroup analyses is rapidly evolving, reaping the full potential of the methods available will require a deep transformation of clinical research. The traditional analysis of randomized clinical trials focuses primarily on a few important clinical endpoints, and is typically done without regard to external relevant data. This focused approach is quite useful to control the type I error, but it may not offer the best opportunities for identifying and/or validating subgroup effects. Subgroup analyses are likely to be far more informative with datasets enriched in biomarkers from multiple studies of the same treatment class.
References
1. Tanniou J, van der Tweel I, Teerenstra S, Roes KCB. Subgroup analyses in confirmatory clinical trials: time to be specific about their purposes. BMC Med Res Meth 2016; 16: 20.
2. Ondra T, Dmitrienko A, Friede T, et al. Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review. J Biopharm Stats 2016; 26: 99-119.