Issues Related to Subgroup Analysis in Clinical Trials

Abstract

Interpretation of subgroup findings is a difficult task. The attempt of this article is to clarify confusions on subgroup analysis and to give some practical suggestions on how to avoid mistakes in interpreting subgroup outcome. We believe that the correct interpretation of subgroup findings is closely related to the intrinsic statistical property and validity of the subgroup analysis. A systematic discussion on subgroup analysis from a statistical point of view will be helpful to clinical trial practitioners.

Invasive candidiasis in cancer patients: observations from a randomized clinical trial

“…Since this exploratory subgroup analysis was not specified in the original protocol, only point estimations of response rates are presented. [13][14][15] Formal comparisons between groups were not performed.…”

Regional Treatment Effects in Studies of Cardiorenal Drugs

“…We read with interest the recent report by Lakkireddy et al (1) regarding periprocedural dabigatran in patients undergoing atrial fibrillation (AF) ablation. This multicenter study noted a significant increase in bleeding and thromboembolic complications with essentially uninterrupted dabigatran versus uninterrupted warfarin.…”

“…Dabigatran potentiates heparin's antithrombotic properties with quantitatively doubled anticoagulant effect. The increased bleeding complications noted by Lakkireddy et al (1) suggest that this in vitro interaction very likely occurs in vivo. This interaction is much less apparent with rivaroxaban and apixaban (2).…”

“…The literature on the topic of differences in treatment effects among subsets or subgroups of clinical trials is extensive (1,(3)(4)(5)(6), and the general consensus is to be cautious in interpreting any such variations because some statistical variability is to be expected and is unlikely to be real.…”

Identification of predicted individual treatment effects in randomized clinical trials

“…The ideal health care scenario would be one in which treatment recommendations are based on the individual patient's most likely treatment response, given their biological and environmental uniqueness. While the reliance on aggregate measures is partially justified by long-established concerns over the dangers of multiplicity (false positives) in subgroup analyses (5)(6)(7)(8)(9)(10)(11), the avoidance of false positives has come at the cost of understanding individual heterogeneity in treatment response. We propose that a principled statistical approach that allows for prediction of an individual patient's response to treatment is needed to advance the effectiveness of individualized medicine.…”