Further Development of Flexible Parametric Models for Survival Analysis

Author:

Lambert Paul C.1,Royston Patrick2

Affiliation:

1. Centre for Biostatistics and Genetic Epidemiology Department of Health Sciences University of Leicester, UK

2. Clinical Trials Unit Medical Research Council London, UK

Abstract

Royston and Parmar (2002, Statistics in Medicine 21: 2175–2197) developed a class of flexible parametric survival models that were programmed in Stata with the stpm command (Royston, 2001, Stata Journal 1: 1–28). In this article, we introduce a new command, stpm2, that extends the methodology. New features for stpm2 include improvement in the way time-dependent covariates are modeled, with these effects far less likely to be over parameterized; the ability to incorporate expected mortality and thus fit relative survival models; and a superior predict command that enables simple quantification of differences between any two covariate patterns through calculation of time-dependent hazard ratios, hazard differences, and survival differences. The ideas are illustrated through a study of breast cancer survival and incidence of hip fracture in prostate cancer patients.

Publisher

SAGE Publications

Subject

Mathematics (miscellaneous)

Reference13 articles.

1. On two families of transformations to additivity for binary response data

2. Deriving more up-todate estimates of long-term patient survival

3. CarstensenB. 2004. Who needs the Cox model anyway? Statistical report, Steno Diabetes Center, Denmark. http://staff.pubhealth.ku.dk/∼bxc/Talks/WntCma-xrp.pdf.

4. Age at diagnosis and the choice of survival analysis methods in cancer epidemiology

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