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

Behnaz Alafchi1, Leili Tapak1,*, Hassan Doosti2, Christophe Chesneau3, Ghodratollah Roshanaei1
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
* Corresponding Author: Leili Tapak. Email: l.tapak@umsha.ac.ir
(This article belongs to this Special Issue: New Trends in Statistical Computing and Data Science)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2022.022647

Received 18 March 2022; Accepted 07 July 2022; Published online 18 August 2022


Survival data with a multi-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 for multi-state data. Three different machine learning approaches (random forest, gradient boosting, and neural network) as the most widely used methods 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.


Multi-state data; deviance residual; martingale residual; gradient boosting; random forest; neural network; variable importance; variable selection
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