The EM Algorithm for Cox's Regression Model Using Glim

David Clayton and Jack Cuzick

Journal of the Royal Statistical Society Series C, 1985, vol. 34, issue 2, 148-156

Abstract: The methods described by Aitkin and Clayton (1980) for fitting parametric regression models to survival data consist of a two‐step recursive algorithm. In the first step a transformation of the observed failure times is found such that the transformed times obey a model which may be simply fitted, i.e. the exponential model. The second step updates the estimates of the parameters by fitting the simple model to the transformed observations. The steps are repeated until convergence. We have suggested elsewhere (Clayton and Cuzick, 1985) that estimation in a very general class of semi‐parametric models may be carried out using a similar algorithm in which the transformation is non‐parametric. Here we apply this idea to the proportional hazards model and show that in this case the iteration is an EM algorithm and leads to maximum partial likelihood estimates. It is shown how this algorithm allows the Cox model to be fitted using the computer program GLIM (Baker and Nelder, 1975).

Date: 1985
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.2307/2347367

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:34:y:1985:i:2:p:148-156

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().