OBJECTIVES

: The identification of characteristics that stratify patients according to the likelihood of treatment response is a key goal of precision medicine research. Within randomized trials, effect modification is typically examined through subgroup analyses or the inclusion of an interaction term(s) in a multivariable regression model. To date, systematic reviews of such findings have relied upon qualitative summaries or subgroup-specific meta-analyses. Such approaches are problematic because they do not quantify the magnitude or uncertainty of the difference in the treatment effects between subgroups. To address this gap, we propose a novel approach that can be used to quantitatively pool subgroup findings from multiple trials.

METHODS

: Our procedure is focused on the estimation of the pooled difference between the subgroup-specific treatment effects within a two-stage meta-analysis. In the first stage, the magnitude and standard error of the difference between the subgroup-specific treatment effects is estimated for each study. When studying relative effect measures, this difference can be estimated on the log-scale, e.g., study-specific difference = log(RR subgroup A) – log(RR subgroup B). The standard error of this difference can then be estimated using subgroup-specific 95% CIs. A meta-analysis of the study-specific differences is then conducted. When the differences are estimated on the log-scale, the pooled quantity can be re-expressed as the ratio of the subgroup specific ORs, RRs, or HRs whereby a ratio equal to 1.00 indicates that the subgroup-specific estimates are equal in magnitude.

RESULTS

: Case-studies and empirical simulations highlighting the application and properties of our approach will be presented. Possible extensions of this methodology will also be discussed. Such extensions include the use of dose-response models to address subgroup defined by a continuous variable and the inclusion of three or more treatments within a network meta-analysis.

CONCLUSIONS

: This approach can help to inform decisions surrounding the targeting of drugs and health technologies.