Matching Microsimulation Risk Factor Correlations to Cross-sectional Data: The Shortest Distance Method

Abstract

Background. Microsimulation models often compute the distribution of a simulated cohort’s risk factors and medical outcomes over time using repeated waves of cross-sectional data. We sought to develop a strategy to simulate how risk factor values remain correlated over time within individuals, and compare it to available alternative methods. Methods. We developed a method using shortest-distance matching for modeling changes in risk factors in individuals over time, which preserves both the cohort distribution of each risk factor as well as the cross-sectional correlation between risk factors observed in repeated cross-sectional data. We compared the performance of the method with rank stability and regression methods, using both synthetic data and data from the Framingham Offspring Heart Study (FOHS) to simulate a cohort’s atherosclerotic cardiovascular disease (ASCVD) risk. Results. The correlation between risk factors was better preserved using the shortest distance method than with rank stability or regression (root mean squared difference = 0.077 with shortest distance, v. 0.126 with rank stability and 0.146 with regression in FOHS, and 0.052, 0.426 and 0.352, respectively, in the synthetic data). The shortest distance method generated population ASCVD risk estimate distributions indistinguishable from the true distribution in over 99.8% of cases (Kolmogorov-Smirnov, P > 0.05), outperforming some existing regression methods, which produced ASCVD distributions statistically distinguishable from the true one at the 5% level around 15% of the time. Limitations. None of the methods considered could predict individual longitudinal trends without error. The shortest-distance method was not statistically inferior to rank stability or regression methods for predicting individual risk factor values over time in the FOHS. Conclusions. A shortest distance method may assist in preserving risk factor correlations in microsimulations informed by cross-sectional data.