A semiparametric regression method for interval-censored data

Seungbong Han*, Adin-Cristian Andrei, Kam Wah Tsui

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In many medical studies, event times are recorded in an interval-censored (IC) format. For example, in numerous cancer trials, time to disease relapse is only known to have occurred between two consecutive clinic visits. Many existing modeling methods in the IC context are computationally intensive and usually require numerous assumptions that could be unrealistic or difficult to verify in practice. We propose a flexible and computationally efficient modeling strategy based on jackknife pseudo-observations (POs). The POs obtained based on nonparametric estimators of the survival function are employed as outcomes in an equivalent, yet simpler regression model that produces consistent covariate effect estimates. Hence, instead of operating in the IC context, the problem is translated into the realm of generalized linear models, where numerous options are available. Outcome transformations via appropriate link functions lead to familiar modeling contexts such as the proportional hazards and proportional odds. Moreover, the methods developed are not limited to these settings and have broader applicability. Simulations studies show that the proposed methods produce virtually unbiased covariate effect estimates, even for moderate sample sizes. An example from the International Breast Cancer Study Group (IBCSG) Trial VI further illustrates the practical advantages of this new approach.

Original languageEnglish (US)
Pages (from-to)18-30
Number of pages13
JournalCommunications in Statistics: Simulation and Computation
Volume43
Issue number1
DOIs
StatePublished - Jan 1 2014

Keywords

  • Interval-censoring
  • Pseudo-observations
  • Regression
  • Semiparametric
  • Survival analysis

ASJC Scopus subject areas

  • Modeling and Simulation
  • Statistics and Probability

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