Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data

Abstract

Introduction. Patient-level simulation models facilitate extrapolation of clinical trial data while allowing for heterogeneity, prior history, and nonlinearity. However, combining different types of uncertainty around within-trial and extrapolated results remains challenging. Methods. We tested 4 methods to combine parameter uncertainty (around the regression coefficients used to predict future events) with sampling uncertainty (uncertainty around mean risk factors within the finite sample whose outcomes are being predicted and the effect of treatment on these risk factors). We compared these 4 methods using a simulation study based on an economic evaluation extrapolating the AFORRD randomized controlled trial using the UK Prospective Diabetes Study Outcomes Model version 2. This established type 2 diabetes model predicts patient-level health outcomes and costs. Results. The 95% confidence intervals around life years gained gave 25% coverage when sampling uncertainty was excluded (i.e., 25% of 95% confidence intervals contained the "true" value). Allowing for sampling uncertainty as well as parameter uncertainty widened confidence intervals by 6.3-fold and gave 96.3% coverage. Methods adjusting for baseline risk factors that combine sampling and parameter uncertainty overcame the bias that can result from between-group baseline imbalance and gave confidence intervals around 50% wider than those just considering parameter uncertainty, with 99.8% coverage. Conclusions. Analyses extrapolating data for individual trial participants should include both sampling uncertainty and parameter uncertainty and should adjust for any imbalance in baseline covariates.

Keywords: decision-analytical modeling; diabetes; patient-level simulation models; randomized controlled trial.

Figure 1

Results of each analysis with 2000 loops.