EVALUATING DIFFERENT APPROACHES TO ESTIMATING TREATMENT COSTS FROM A RANDOMIZED CLINICAL TRIAL WITH INCOMPLETE FOLLOW-UP

Purpose: Statistical analysis of cost data from a real world clinical trial study is challenging because of the censoring arising from incomplete follow-up data. Typically a generalized linear model (GLM) is used to predict costs for all patients in the study.  Bootstrapping is then used to obtain a confidence interval. However, this approach may underestimate the true variance. Our objective was to investigate alternative methods in their ability to predict costs and appropriately measure uncertainty.

Methods: We simulated a two-arm randomized clinical trial and compared the performance of four different bootstrap approaches to predict average treatment costs. In the first approach, we bootstrapped the sample and ran a GLM to estimate costs for all patients regardless of discontinuation status, as is often done in the literature. In the second method, we only used predicted costs from the same GLM for missing values. In the third method, we modified the second approach by including a random component to the predicted costs based on the fit of the GLM.  Finally, we used predicted costs with a random component for all of the patients. We repeated this exercise under varying scenarios to identify factors that may influence the predicted costs (e.g., proportion of missing data, mechanism of missingness, distribution of costs, sample size, GLM link function).

Results: We compared the observed costs with predicted costs from the 4 approaches on the basis of bias and coverage.  All four approaches were similar with respect to bias, but coverage was improved when a random component was included.

Conclusions: Our study enabled us to evaluate different bootstrap procedures for handling missing costs data.  Approaches that utilized observed data or incorporate a random component to the predicted cost perform better under certain scenarios.