An overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies.

Abstract:

:Relative survival provides a measure of the proportion of patients dying from the disease under study without requiring the knowledge of the cause of death. We propose an overall strategy based on regression models to estimate the relative survival and model the effects of potential prognostic factors. The baseline hazard was modelled until 10 years follow-up using parametric continuous functions. Six models including cubic regression splines were considered and the Akaike Information Criterion was used to select the final model. This approach yielded smooth and reliable estimates of mortality hazard and allowed us to deal with sparse data taking into account all the available information. Splines were also used to model simultaneously non-linear effects of continuous covariates and time-dependent hazard ratios. This led to a graphical representation of the hazard ratio that can be useful for clinical interpretation. Estimates of these models were obtained by likelihood maximization. We showed that these estimates could be also obtained using standard algorithms for Poisson regression.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Remontet L,Bossard N,Belot A,Estève J,French network of cancer registries FRANCIM.

doi

10.1002/sim.2656

subject

Has Abstract

pub_date

2007-05-10 00:00:00

pages

2214-28

issue

10

eissn

0277-6715

issn

1097-0258

journal_volume

26

pub_type

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