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For designing, monitoring, and analyzing a longitudinal study with an event time as the outcome variable, the restricted mean event time (RMET) is an easily interpretable, clinically meaningful summary of the survival function in the presence of censoring. The RMET is the average of all potential event times measured up to a time point and can be estimated consistently by the area under the KaplanMeier curve over $[0, \tau ]$. In this paper, we study a class of regression models, which directly relates the RMET to its baseline covariates for predicting the future subjects RMETs. Since the standard Cox and the accelerated failure time models can also be used for estimating such RMETs, we utilize a cross-validation procedure to select the best among all the working models considered in the model building and evaluation process. Lastly, we draw inferences for the predicted RMETs to assess the performance of the final selected model using an independent data set or a hold-out sample from the original data set. All the proposals are illustrated with the data from the an HIV clinical trial conducted by the AIDS Clinical Trials Group and the primary biliary cirrhosis study conducted by the Mayo Clinic.
Biostatistics – Oxford University Press
Published: Apr 29, 2014
Keywords: Accelerated failure time model Cox model Cross-validation Hold-out sample Personalized medicine Perturbation-resampling method
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