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Direct likelihood inference on the cause-specific cumulative incidence function: A flexible parametric regression modelling approach.

journal contribution
posted on 2018-04-09, 15:44 authored by Sarwar Islam Mozumder, Mark Rutherford, Paul Lambert
In a competing risks analysis, interest lies in the cause-specific cumulative incidence function (CIF) that can be calculated by either (1) transforming on the cause-specific hazard or (2) through its direct relationship with the subdistribution hazard. We expand on current competing risks methodology from within the flexible parametric survival modelling framework (FPM) and focus on approach (2). This models all cause-specific CIFs simultaneously and is more useful when we look to questions on prognosis. We also extend cure models using a similar approach described by Andersson et al for flexible parametric relative survival models. Using SEER public use colorectal data, we compare and contrast our approach with standard methods such as the Fine & Gray model and show that many useful out-of-sample predictions can be made after modelling the cause-specific CIFs using an FPM approach. Alternative link functions may also be incorporated such as the logit link. Models can also be easily extended for time-dependent effects.

History

Citation

Statistics in Medicine, 2017, 37 (1)

Author affiliation

/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Department of Health Sciences

Version

  • AM (Accepted Manuscript)

Published in

Statistics in Medicine

Publisher

Wiley

issn

0277-6715

eissn

1097-0258

Copyright date

2017

Available date

2018-10-02

Publisher version

https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7498

Notes

The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.

Language

en

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