Models with interactions overestimated heterogeneity of treatment effects and were prone to treatment mistargeting.

ABSTRACT:

Objectives

We aimed to compare the performance of different regression modeling approaches for the prediction of heterogeneous treatment effects.

Study design and setting

We simulated trial samples (n = 3,600; 80% power for a treatment odds ratio of 0.8) from a superpopulation (N = 1,000,000) with 12 binary risk predictors, both without and with six true treatment interactions. We assessed predictions of treatment benefit for four regression models: a "risk model" (with a constant effect of treatment assignment) and three "effect models" (including interactions of risk predictors with treatment assignment). Three novel performance measures were evaluated: calibration for benefit (i.e., observed vs. predicted risk difference in treated vs. untreated), discrimination for benef

SUBMITTER: van Klaveren D 

PROVIDER: S-EPMC7497896 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature