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An overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies
Author(s) -
Remontet L.,
Bossard N.,
Belot A.,
Estève J.
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2656
Subject(s) - akaike information criterion , covariate , statistics , proportional hazards model , poisson regression , regression , regression analysis , hazard , econometrics , mathematics , hazard ratio , accelerated failure time model , survival analysis , relative survival , parametric statistics , standard error , confidence interval , medicine , cancer , cancer registry , population , chemistry , environmental health , organic chemistry
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. Copyright © 2006 John Wiley & Sons, Ltd.