Predicting the restricted mean event time with the subject's baseline covariates in survival analysis

Lu Tian, Lihui Zhao, L. J. Wei*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

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 τ and can be estimated consistently by the area under the Kaplan-Meier curve over. 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.

Original languageEnglish (US)
Pages (from-to)222-233
Number of pages12
JournalBiostatistics
Volume15
Issue number2
DOIs
StatePublished - Apr 1 2014

Keywords

  • Accelerated failure time model
  • Cox model
  • Cross-validation
  • Hold-out sample
  • Personalized medicine
  • Perturbation-resampling method

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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