Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 4 2 2018 10 31 91 98 EN Alireza Abadi Department of Community Medicine, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran Bagher Pahlavanzade Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Farid Zayeri Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Taban Baghfalaki Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran. 2018 05 09 Background & Aim: In survival studies, incidence of competing risks causes that the time of event of interest to be unknown. Analysis of competing risk data, often implemented using hazard-based method under proportional hazard assumption. In this study, we interpreted covariate effect under accelerated failure time model and cause-specific survival function. Methods & Materials: We considered weibull hazard and survival function as cause-specific hazard and survival function and explored the relation between these function. Estimation of parameters performed using Bayesian methods with non-informative priors that implemented in R2WinBUGS package of R software. Results: Simulation study showed that, the relation between hazard and survival parameters for weibull distribution is also established between parameters of cause-specific hazard and cause-specific survival function. This relation also verified in PBC data set for logarithm of serum bilirubin and D-penicillamine effect. Conclusion: Although in competing risk studies, most of the analysis performed under PH assumption, analysis based on AFT models will also be applicable for these data. In these setting, coefficients can be interpreted as effects of covariate on time to each event. https://jbe.tums.ac.ir/index.php/jbe/article/view/184 https://jbe.tums.ac.ir/index.php/jbe/article/download/184/162