Methods: Using patient-level data from 7 RCTs representing 4455 patients, we developed a Bayesian hierarchical Weibull regression model to combine data while allowing for trial-specific baseline hazard functions and treatment effects. The final model, derived from backwards elimination, included the main effects of treatment, covariates and the interaction between age and treatment. We performed frequentist evaluation of our prediction model using calibration and discrimination statistics. We performed internal validation using bootstrap samples of the combined data set and external validation using registry data. The model explored patients in 192 subgroups stratified by treatment, age, ejection fraction (EF), New York Heart Association (NYHA) class, QRS, and presence of ischemic disease.
Results: With the borrowing of strength between covariate categories and across trials, our Bayesian hierarchical model allows predictions even for subgroups with small sizes (subgroup sample size ranged from 0 to 200) though with increased uncertainty in such cases. The prediction model had a C-statistic of 0.72 (se=0.01) at year 1 indicating good discrimination and was well calibrated (p=0.99). The C-statistic was slightly smaller at years 2-5 (range: 0.67,0.70), but the model predictions were also calibrated. The same general conclusions were obtained using either internal or external validation data sets. At 5 years, the model predicts the ICD to be more effective in all subgroups. Predicted 5-yr survival with an ICD varied from 29.6% (75+y, NYHAIII, EF<30, QRS>=120, ischemia) to 90% (<65y, NYHAI, EF>=30, QRS>=120, no ischemia), while survival in the control group varied from 20% (75+y, NYHAIII, EF<30, QRS>=120, ischemia) to 84.2% (<65y, NYHAII, EF>=30, QRS<120, no ischemia). The absolute survival benefit ranged from 0.6% to 21.6% across subgroups.
Conclusion: Our findings suggest that over time ICD treatment is more effective in most subgroups relative to non-ICD. Incorporation of this prediction model into a decision-analytic framework will allow exploration of harms/benefits of ICD use in specific subgroups of interest, while also exploring the uncertainty of these findings and the value of additional data acquisition.