Using Medical Malpractice Data to Predict the Frequency of Claims: A Study of Poisson Process Models With Random Effects
利用佛罗里达州1975-1987年医疗事故索赔数据，构建含随机效应和医生特征的泊松过程模型，预测医生个人索赔频率，并与实际数据对比验证。
Abstract I use the Florida state data base (1975–1987) of settled malpractice claims to develop Poisson process models for the frequency of claims filed against individual physicians. These models incorporate random effects and covariates that represent physician attributes and are natural generalizations of the negative binomial model that is typically used to study claims frequency. I predict claims frequencies during 1981–1982 using models that are selected and estimated from claims data during 1975–1980 and then compare these predictions to the actual frequencies.