Combining randomized trial data to estimate heterogeneous treatment effects

Authors  Hatfield, Laura; Kramer, Daniel;  Normand, Sharon-Lise
Year Published 2015
Link to white paper


Heart failure arises, progresses, and responds to therapy differently in different people. Yet clinical trials often lack power to estimate treatment effects for subgroups, or enforce eligibility criteria that exclude some patients entirely. Combining information across trials increases power for subgroup estimates and expands generalizibility. However, naively pooling patient-level data sacrifices the benefits of randomization, and pooling study-level estimates must consider trial heterogeneity.We develop and illustrate approaches for combining information across trials to estimate effects in men and women with heart failure who are treated with implantable cardioverter-defibrilliator (ICD) alone or in combination with cardiac resynchronization therapy (CRT-D). We consider individual- and trial-level factors that may confound or mediate subgroup treatment effects. For example, ischemic disease is more common in men; could this explain why women appear to benefit more from CRT-D than men?Our Bayesian models estimate sex-specific treatment effects across trials, accounting for uncertainty, confounding, and mediation. We find that with a very small number of heterogeneous studies, hierarchical modeling offers few benefits over conventional effect pooling,producing wider credible intervals but little shrinkage. We also find little evidence for residual confounding within subgroups, but some evidence of interactions between left bundle branch blockage and ischemic etiology in the sex-specific treatment effects, suggesting further study.


LAH and SLN are supported by contract DHHS/FDA-223201110172C and grant 1U01FD004493-01 from the Center for Devices and Radiological Health, US Food and Drug Administration.DBK is supported by a Paul B. Beeson Career Development Award (NIA K23AG045963).