Linked Sensitivity Analysis, Calibration, and Uncertainty Analysis Using a System Dynamics Model for Stroke Comparative Effectiveness Research.
As health services researchers and decision makers tackle more difficult problems using simulation models, the number of parameters and the corresponding degree of uncertainty have increased. This often results in reduced confidence in such complex models to guide decision making.



To demonstrate a systematic approach of linked sensitivity analysis, calibration, and uncertainty analysis to improve confidence in complex models.



Four techniques were integrated and applied to a System Dynamics stroke model of US veterans, which was developed to inform systemwide intervention and research planning: Morris method (sensitivity analysis), multistart Powell hill-climbing algorithm and generalized likelihood uncertainty estimation (calibration), and Monte Carlo simulation (uncertainty analysis).



Of 60 uncertain parameters, sensitivity analysis identified 29 needing calibration, 7 that did not need calibration but significantly influenced key stroke outcomes, and 24 not influential to calibration or stroke outcomes that were fixed at their best guess values. One thousand alternative well-calibrated baselines were obtained to reflect calibration uncertainty and brought into uncertainty analysis. The initial stroke incidence rate among veterans was identified as the most influential uncertain parameter, for which further data should be collected. That said, accounting for current uncertainty, the analysis of 15 distinct prevention and treatment interventions provided a robust conclusion that hypertension control for all veterans would yield the largest gain in quality-adjusted life years.



For complex health care models, a mixed approach was applied to examine the uncertainty surrounding key stroke outcomes and the robustness of conclusions. We demonstrate that this rigorous approach can be practical and advocate for such analysis to promote understanding of the limits of certainty in applying models to current decisions and to guide future data collection.
Program in Health Services & Systems Research, Duke-NUS Graduate Medical School Singapore, Singapore (YT, KE, DBM)
Yuan
Y
Tian
Department of Health Policy & Management, University of North Carolina at Chapel Hill, NC, USA (KHL)
Kristen
K
Hassmiller Lich
Department of Computer Science, University of Saskatchewan, SK, Canada (NDO)
Nathaniel D
ND
Osgood
Program in Health Services & Systems Research, Duke-NUS Graduate Medical School Singapore, Singapore (YT, KE, DBM)
Kirsten
K
Eom
Program in Health Services & Systems Research, Duke-NUS Graduate Medical School Singapore, Singapore (YT, KE, DBM)|Department of Internal Medicine, Duke University Medical Center, Durham, NC, USA (DBM)
David B
DB
Matchar
D000066491:Clinical Decision-Making / Q000379:methods*; D057186:Comparative Effectiveness Research; D003198:Computer Simulation; D003657:Decision Making; D006801:Humans; D008962:Models, Theoretical; D009010:Monte Carlo Method; D011336:Probability; D019057:Quality-Adjusted Life Years; D020521:Stroke / Q000191:economics / Q000453:epidemiology / Q000517:prevention & control* / Q000628:therapy*; D035501:Uncertainty; D014481:United States; D014493:United States Department of Veterans Affairs
System Dynamics; calibration; sensitivity analysis; simulation model; stroke; uncertainty analysis