Purpose: As Bayesian statistical approaches have gained broader acceptance within the clinical-trial community, the Centers for Medicare and Medicaid (CMS) sought to assess the impact of such techniques on policy-level decision making. We performed a case-study of Bayesian approaches in the clinical domain of implantable cardioverter defibrillator (ICD) therapy for the prevention of sudden cardiac death.
Method: We considered patient-level data from eight ICD trials representing 6,286 patients and two decades of evidence. We considered two treatment groups (ICD versus control) and four baseline prognostic variables (age, ejection fraction, NYHA class, and ischemia) to capture some of the differences in trial designs. We explored the use of frequentist or Bayesian techniques in combining data from trials (1) without adjustments for (potential) trial effects, (2) adjusting for trial effects using fixed or random effect, and (3) assuming trial-specific baseline hazard functions. We performed sensitivity analyses on priors used in our Bayesian analyses.
Result: Under all model formulations considered, there is evidence of a treatment effect on overall survival. Estimates from Bayesian models are generally similar to those obtained under frequentist models. Under the full Bayesian hierarchical model that accounts for trial variation in the baseline-hazard, main and interaction effects, we found differential ICD effect across trials. This variation could be due to differences in the devices, in the underlying medical care, or in patient characteristics that are currently not included in our analysis. When considering only data from a single trial, our results are more sensitive to prior choices. Increasing the sample size (by combining data from trials) reduces the sensitivity to prior choices. We found no evidence of interactions between treatment and any of the prognostic variables.
Conclusion: Bayesian models flexibly allow for borrowing of information while also allowing for different treatment and subgroup effects across trials. They provide more precise estimates of the treatment effects and may reconcile what could be an unexpected result from a single trial. When considering Bayesian estimation the role of priors should be examined through a sensitivity analysis. Incorporation of Bayesian techniques into CMS decision-making process may enable policymakers to harness the power of available evidence, explore subgroup effects within a trial and across trials in a methodologically rigorous manner, and assess the uncertainty in clinical trial findings.
Candidate for the Lee B. Lusted Student Prize Competition