A Two-Step Algorithm to Estimate Variable Importance for Multi-State Data:An Application to COVID-19  

作　　者：Behnaz Alafchi Leili Tapak Hassan Doosti Christophe Chesneau Ghodratollah Roshanaei 

机构地区：[1]Department of Biostatistics,School of Public Health,Modeling of Noncommunicable Diseases Research Center,Hamadan University of Medical Sciences,Hamadan,Iran [2]School of Mathematical and Physical Sciences,Macquarie University,Sydney,Australia [3]Department of Mathematics,LMNO,University of Caen-Normandie,Caen,France

出　　处：《Computer Modeling in Engineering & Sciences》2023年第6期2047-2064,共18页工程与科学中的计算机建模（英文）

摘　　要：Survival data with amulti-state structure are frequently observed in follow-up studies.An analytic approach based on a multi-state model(MSM)should be used in longitudinal health studies in which a patient experiences a sequence of clinical progression events.One main objective in the MSM framework is variable selection,where attempts are made to identify the risk factors associated with the transition hazard rates or probabilities of disease progression.The usual variable selection methods,including stepwise and penalized methods,do not provide information about the importance of variables.In this context,we present a two-step algorithm to evaluate the importance of variables formulti-state data.Three differentmachine learning approaches(randomforest,gradient boosting,and neural network)as themost widely usedmethods are considered to estimate the variable importance in order to identify the factors affecting disease progression and rank these factors according to their importance.The performance of our proposed methods is validated by simulation and applied to the COVID-19 data set.The results revealed that the proposed two-stage method has promising performance for estimating variable importance.

关 键 词：Multi-state data deviance residual martingale residual gradient boosting randomforest neural network variable importance variable selection 

分 类 号：R563.1[医药卫生—呼吸系统]