Targeted estimation of state occupation probabilities for the non‐Markov illness‐death model
利用半参数效率理论，为连续时间不可逆疾病死亡模型的状态占用概率推导出一类估计量，允许使用数据自适应方法，并通过惩罚泊松回归估计转移强度函数。
Abstract We use semi‐parametric efficiency theory to derive a class of estimators for the state occupation probabilities of the continuous‐time irreversible illness‐death model. We consider both the setting with and without additional baseline information available, where we impose no specific functional form on the intensity functions of the model. We show that any estimator in the class is asymptotically linear under suitable assumptions about the estimators of the intensity functions. In particular, the assumptions are weak enough to allow the use of data‐adaptive methods, which is important for making the identifying assumption of coarsening at random plausible in realistic settings. We suggest a flexible method for estimating the transition intensity functions of the illness‐death model based on penalized Poisson regression. We apply this method to estimate the nuisance parameters of an illness‐death model in a simulation study and a real‐world application.