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...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.展开更多
Analysis of microarray data is associated with the methodological problems of high dimension and small sample size. Various methods have been used for variable selection in high- dimension and small sample size cases ...Analysis of microarray data is associated with the methodological problems of high dimension and small sample size. Various methods have been used for variable selection in high- dimension and small sample size cases with a single survival endpoint. However, little effort has been directed toward addressing competing risks where there is more than one failure risks. This study compared three typical variable selection techniques including Lasso, elastic net, and likelihood-based boosting for high-dimensional time-to-event data with competing risks. The per- formance of these methods was evaluated via a simulation study by analyzing a real dataset related to bladder cancer patients using time-dependent receiver operator characteristic （ROC） curve and bootstrap .632 ＋ prediction error curves. The elastic net penalization method was shown to outper- form Lasso and boosting. Based on the elastic net, 33 genes out of 1381 genes related to bladder cancer were selected. By fitting to the Fine and Gray model, eight genes were highly significant（P 〈 0.001）. Among them, expression of RTN4, SON, IGF1R, SNRPE, PTGR1, PLEK, and ETFDHwas associated with a decrease in survival time, whereas SMARCAD1 expression was asso- ciated with an increase in survival time. This study indicates that the elastic net has a higher capacity than the Lasso and boosting ＇for the prediction of survival time in bladder cancer patients. Moreover, genes selected by all methods improved the predictive power of the model based on only clinical variables, indicating the value of information contained in the mieroarray features.展开更多
文摘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.
基金funded by the Vice Chancellor for Research and Technology of Hamadan University of Medical Sciences (grant No.9210173382)
文摘Analysis of microarray data is associated with the methodological problems of high dimension and small sample size. Various methods have been used for variable selection in high- dimension and small sample size cases with a single survival endpoint. However, little effort has been directed toward addressing competing risks where there is more than one failure risks. This study compared three typical variable selection techniques including Lasso, elastic net, and likelihood-based boosting for high-dimensional time-to-event data with competing risks. The per- formance of these methods was evaluated via a simulation study by analyzing a real dataset related to bladder cancer patients using time-dependent receiver operator characteristic （ROC） curve and bootstrap .632 ＋ prediction error curves. The elastic net penalization method was shown to outper- form Lasso and boosting. Based on the elastic net, 33 genes out of 1381 genes related to bladder cancer were selected. By fitting to the Fine and Gray model, eight genes were highly significant（P 〈 0.001）. Among them, expression of RTN4, SON, IGF1R, SNRPE, PTGR1, PLEK, and ETFDHwas associated with a decrease in survival time, whereas SMARCAD1 expression was asso- ciated with an increase in survival time. This study indicates that the elastic net has a higher capacity than the Lasso and boosting ＇for the prediction of survival time in bladder cancer patients. Moreover, genes selected by all methods improved the predictive power of the model based on only clinical variables, indicating the value of information contained in the mieroarray features.
