Jinyi Zhu, a PhD student in Health Policy and Decision Sciences at CHDS, received the 2019 Stephen Pauker Award for Quantitative Methods and Theoretical Developments (QMTD) at the 41st Annual North American Meeting of the Society for Medical Decision Making (SMDM) in Portland, Oregon.
Her work, titled “Evaluating Bias and Variance across Methods for Microsimulation Model Validation,” aimed to compare the relative performance of four validation methods for simulation models: apparent performance, split sample, cross-validation and bootstrapping. In statistical modeling, the apparent performance (i.e., using the same sample for model development and validation) is known to result in an optimistic bias in estimating model performance, and more advanced methods have been developed to correct for this bias. However, advanced validation methods have been underused and understudied in simulation modeling. Using a stroke microsimulation model, Jinyi found that among the four methods, bootstrapping achieved the lowest bias and variance and should be considered good practice when developing and validating a simulation based on individual-level datasets.
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