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A general framework for parametric survival analysis

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journal contribution
posted on 2015-09-17, 11:52 authored by Michael J. Crowther, P. C. Lambert
Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients over time, obtaining absolute measures of risk. Commonly used parametric survival models, such as the Weibull, make restrictive assumptions of the baseline hazard function, such as monotonicity, which is often violated in clinical datasets. In this article, we extend the general framework of parametric survival models proposed by Crowther and Lambert (Journal of Statistical Software 53:12, 2013), to incorporate relative survival, and robust and cluster robust standard errors. We describe the general framework through three applications to clinical datasets, in particular, illustrating the use of restricted cubic splines, modelled on the log hazard scale, to provide a highly flexible survival modelling framework. Through the use of restricted cubic splines, we can derive the cumulative hazard function analytically beyond the boundary knots, resulting in a combined analytic/numerical approach, which substantially improves the estimation process compared with only using numerical integration. User-friendly Stata software is provided, which significantly extends parametric survival models available in standard software.

History

Citation

Statistics in Medicine, 2014, 33 (30), pp. 5280-5297

Author affiliation

/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGY/School of Medicine/Department of Health Sciences

Version

  • AM (Accepted Manuscript)

Published in

Statistics in Medicine

Publisher

Wiley

issn

0277-6715

eissn

1097-0258

Acceptance date

2014-08-20

Copyright date

2014

Available date

2015-09-18

Publisher version

http://onlinelibrary.wiley.com/doi/10.1002/sim.6300/abstract

Language

en