Evaluating Parameter Uncertainty in a Simulation Model of Cancer Using Emulators. (May 2019)

• Record Type: Journal Article
• Authors: de Carvalho, Tiago M.; Heijnsdijk, Eveline A. M.; Coffeng, Luc; de Koning, Harry J.
• Is Part Of: Medical decision making. Volume 39:Number 4(2019)
• Page Start: 405
• Page End: 413
• Publication Date: 2019-05
• Subjects: overdiagnosis -- prostate cancer -- probabilistic sensitivity analyses -- Gaussian process regression -- emulators; Medical policy -- Periodicals; Clinical medicine -- Decision making -- Periodicals; Medicine -- Periodicals; Médecine clinique -- Prise de décision -- Périodiques; 362.1
• DOI: 10.1177/0272989X19837631
• Languages: English
• ISSNs: 0272-989X

Abstract

Background . Microsimulation models have been extensively used in the field of cancer modeling. However, there is substantial uncertainty regarding estimates from these models, for example, overdiagnosis in prostate cancer. This is usually not thoroughly examined due to the high computational effort required.Objective . To quantify uncertainty in model outcomes due to uncertainty in model parameters, using a computationally efficient emulator (Gaussian process regression) instead of the model.Methods . We use a microsimulation model of prostate cancer (microsimulation screening analysis [MISCAN]) to simulate individual life histories. We analyze the effect of parametric uncertainty on overdiagnosis with probabilistic sensitivity analyses (ProbSAs). To minimize the number of MISCAN runs needed for ProbSAs, we emulate MISCAN, using data pairs of parameter values and outcomes to fit a Gaussian process regression model. We evaluate to what extent the emulator accurately reproduces MISCAN by computing its prediction error.Results . Using an emulator instead of MISCAN, we may reduce the computation time necessary to run a ProbSA by more than 85%. The average relative prediction error of the emulator for overdiagnosis equaled 1.7%. We predicted that 42% of screen-detected men are overdiagnosed, with an associated empirical confidence interval between 38% and 48%. Sensitivity analyses show that the accuracy of the emulator is sensitive to which model parameters are included in the training runs.Conclusions . For a computationally expensive simulation model with a large number of parameters, we show it is possible to conduct a ProbSA, within a reasonable computation time, by using a Gaussian process regression emulator instead of the original simulation model.