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A flexible parametric competing-risks model using a direct likelihood approach for the cause-specific cumulative incidence function

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journal contribution
posted on 07.03.2018, 17:08 by Sarwar Islam Mozumder, Mark J. Rutherford, Paul C. Lambert
In competing-risks analysis, the cause-specific cumulative incidence function (CIF) is usually obtained in a modeling framework by either 1) transforming on all cause-specific hazards or 2) transforming by using a direct relationship with the subdistribution hazard function. We expand on current competing-risks methodology from within the flexible parametric survival modeling framework and focus on the second approach. This approach models all cause-specific CIFs simultaneously and is more useful for answering prognostic-related questions. We propose the direct flexible parametric survival modeling approach for the cause-specific CIF. This approach models the (log cumulative) baseline hazard without requiring numerical integration, which leads to benefits in computational time. It is also easy to make out-of-sample predictions to estimate more useful measures and incorporate alternative link functions, for example, logit links. To implement these methods, we introduce a new estimation command, stpm2cr, and demonstrate useful predictions from the model through an illustrative melanoma dataset.

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Citation

Stata Journal, 17 (2), pp. 462-489

Author affiliation

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

Version

AM (Accepted Manuscript)

Published in

Stata Journal

Publisher

StataCorp

issn

1536-867X

eissn

1536-8734

Copyright date

2017

Publisher version

http://www.stata-journal.com/article.html?article=st0482

Notes

The file associated with this record is under embargo while permission to archive is sought from the publisher. The full text may be available through the publisher links provided above.

Language

en

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