Tehran University of Medical Sciences Journal of Biostatistics and Epidemiology 2383-4196 6 1 2020 10 22 30 39 Tohid Jafari-Koshki Molecular Medicine Research Center, Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran. Sayed Mohsen Hosseini Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran. Shahram Arsang-Jang Department of Biostatistics and Epidemiology, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran. 2020 06 09 2020 07 08 Background: There has been a great interest in joint modeling of longitudinal and survival data in recent two decades. Joint models have less restrictive assumptions in multivariate modeling and could address various research questions. This has led to their wide applications in practice. However, earlier models had normality assumption on the distribution in longitudinal part that is usually violated in real data. Hence, recent research have focused on circumventing this issue. Using various skewed distributions has been proposed and applied in the literature. Nevertheless, the flexibility of the proposed methods is limited especially when the data are skew positive. Methods: In this paper, we introduce the use of Birnbaum-Saunders (BS) distribution in joint modeling context. BS distribution is more flexible and could cover a wide range of skew, kurtotic or bimodal data. Results: We analyzed publicly available ddI/ddC data both with normal and BS distributions in Bayesian setting and compared their fit by Widely Applicable Information Criterion (WAIC). The joint BS model showed a better fit to the data. Conclusion: We introduced and applied BS distribution in joint modeling of longitudinal-survival data. Using multi-parameter distributions such as BS in Bayesian setting could improve the fit of models without limitations that arise in transformation of data from original scale.  https://jbe.tums.ac.ir/index.php/jbe/article/view/341