Skip to main content
Article
Approximating the Baseline Hazard Function by Taylor Series for Interval-Censored Time-to-Event Data
Journal of Biopharmaceutical Statistics
  • Ding-Geng Chen, Georgia Southern University
  • Lili Yu, Georgia Southern University
  • Karl E. Peace, Georgia Southern University
  • Y. L. Lio, University of South Dakota
  • Yibin Wang, Daiichi Sankyo Pharma Development
Document Type
Article
Publication Date
1-1-2013
DOI
10.1080/10543406.2012.756497
Abstract

In many oncology clinical trials, time-to-event data are generated from scanning for cancer within a specific interval, resulting in interval censoring along with complete-time and right-left-censored time-to-event data. A common practice in analyzing data from this type of trial is to impute the interval-censored event time using the midpoint or right endpoint (i.e., the first observed time) of the interval so that well-known statistical methods developed for right-censored time-to-event data, such as Cox regression, may be used for the requisite analyses. This may introduce bias and lead to erroneous conclusions. In this paper, a Taylor series is proposed to approximate the log baseline hazard function in Cox proportional hazards regression to mitigate the bias arising from analyzing the imputed time-to-event data. With this formulation, the likelihood ratio test can be used to select an appropriate order for this Taylor series approximation and maximum likelihood techniques used to estimate model parameters and provide statistical inference, for example, on treatment effect. The application of this novel method is demonstrated by a simulation study and application to data from a breast cancer clinical trial.

Citation Information
Ding-Geng Chen, Lili Yu, Karl E. Peace, Y. L. Lio, et al.. "Approximating the Baseline Hazard Function by Taylor Series for Interval-Censored Time-to-Event Data" Journal of Biopharmaceutical Statistics Vol. 23 Iss. 3 (2013) p. 695 - 708 ISSN: 1520-5711
Available at: http://works.bepress.com/lili-yu/3/