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

Helen A. Dakin, José Leal, Andrew Briggs, Philip Clarke, Rury R. Holman and Alastair Gray
Additional contact information
Helen A. Dakin: Nuffield Department of Population, Health Economics Research Centre, University of Oxford, Oxford, Oxfordshire, UK
José Leal: Nuffield Department of Population, Health Economics Research Centre, University of Oxford, Oxford, Oxfordshire, UK
Andrew Briggs: Department of Health Services Research & Policy, London School of Hygiene and Tropical Medicine, London, UK
Philip Clarke: Nuffield Department of Population, Health Economics Research Centre, University of Oxford, Oxford, Oxfordshire, UK
Rury R. Holman: Diabetes Trials Unit, University of Oxford, Oxford, Oxfordshire, UK

Medical Decision Making, 2020, vol. 40, issue 4, 460-473

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: diabetes; patient-level simulation models; randomized controlled trial; decision-analytical modeling (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0272989X20916442 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:40:y:2020:i:4:p:460-473

DOI: 10.1177/0272989X20916442

Access Statistics for this article

More articles in Medical Decision Making
Bibliographic data for series maintained by SAGE Publications ().