LAUSR

Microsimulation model simulates individuals and estimates transition probabilities within the population using individual participant data. This approach could deal with the heterogeneous characteristics among the people or personal history of diseases and may be relevant in addressing cost-effectiveness problems of screening for complex conditions in epidemiology. This paper introduces the general principles, basic steps involved in implementation, analytic methods, and other related issues of the microsimulation model. Based on a practical research case of estimating the cost-effectiveness of microalbuminuria screening for chronic kidney disease in the United States, critical points in applications of the microsimulation model for cost-effectiveness analysis of screening were discussed in detail, including model development, model analysis, and the interpretation of the results. The microsimulation model considers the dynamic nature of complex diseases by estimating a broad range of individual characteristics and increasingly used to provide insights into complex problems that the Markov model does not efficiently address. For better supporting evidence-informed decision-making in public health, future studies should be aware of the accuracy of parameters in the decision-analytic model and the transparency of the models and results, as well as complying with the relevant reporting standards.