Comput Methods Programs Biomed - SAS macros for estimation of direct adjusted cumulative incidence curves under proportional subdistribution hazards models.

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Resumo

The cumulative incidence function is commonly reported in studies with competing risks. The aim of this paper is to compute the treatment-specific cumulative incidence functions, adjusting for potentially imbalanced prognostic factors among treatment groups. The underlying regression model considered in this study is the proportional hazards model for a subdistribution function [1]. We propose estimating the direct adjusted cumulative incidences for each treatment using the pooled samples as the reference population. We develop two SAS macros for estimating the direct adjusted cumulative incidence function for each treatment based on two regression models. One model assumes the constant subdistribution hazard ratios between the treatments and the alternative model allows each treatment to have its own baseline subdistribution hazard function. The macros compute the standard errors for the direct adjusted cumulative incidence estimates, as well as the standard errors for the differences of adjusted cumulative incidence functions between any two treatments. Based on the macros' output, one can assess treatment effects at predetermined time points. A real bone marrow transplant data example illustrates the practical utility of the SAS macros.

Resumo Limpo

cumul incid function common report studi compet risk aim paper comput treatmentspecif cumul incid function adjust potenti imbalanc prognost factor among treatment group under regress model consid studi proport hazard model subdistribut function propos estim direct adjust cumul incid treatment use pool sampl refer popul develop two sas macro estim direct adjust cumul incid function treatment base two regress model one model assum constant subdistribut hazard ratio treatment altern model allow treatment baselin subdistribut hazard function macro comput standard error direct adjust cumul incid estim well standard error differ adjust cumul incid function two treatment base macro output one can assess treatment effect predetermin time point real bone marrow transplant data exampl illustr practic util sas macro

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