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Optimal designs for Cox regression

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  • J. López‐Fidalgo
  • M. J. Rivas‐López
  • R. Del Campo

Abstract

Optimal designs under a survival analysis framework have been rarely considered in the literature. In this paper, an optimal design theory is developed for the typical Cox regression problem. Failure time is modeled according to a probability distribution depending on some explanatory variables through a linear model. At the end of the study, some units will not have failed and thus their time records will be censored. In order to deal with this problem from an experimental design point of view it will be necessary to assume a probability distribution for the time an experimental unit enters the study. Then an optimal conditional design will be computed at the beginning of the study for any possible given time. Thus, every time a new unit enters the study, there is an experimental design to be determined. A particular and simple case is used throughout the paper in order to illustrate the procedure.

Suggested Citation

  • J. López‐Fidalgo & M. J. Rivas‐López & R. Del Campo, 2009. "Optimal designs for Cox regression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(2), pages 135-148, May.
    • Handle: RePEc:bla:stanee:v:63:y:2009:i:2:p:135-148
    DOI: 10.1111/j.1467-9574.2009.00415.x
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    Cited by:

    1. López-Fidalgo, J. & Rivas-López, M.J., 2014. "Optimal experimental designs for partial likelihood information," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 859-867.

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