Bayesian Joint Modeling of Skew-Positive Longitudinal-Survival Data Using Birnbaum-Saunders Distribution

2020 ◽  
Author(s):  
Tohid Jafari-Koshki ◽  
Sayed Mohsen Hosseini ◽  
Shahram Arsang-Jang
Keyword(s):  
Survival Data ◽  
Real Data ◽  
Information Criterion ◽  
Joint Modeling ◽  
Multivariate Modeling ◽  
Joint Models ◽  
Wide Range ◽  
Longitudinal Part ◽  
Research Questions ◽  
Longitudinal And Survival Data

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. 

scholarly journals Basic Concepts and Methods for Joint Models of Longitudinal and Survival Data

2010 ◽  
Vol 28 (16) ◽  
pp. 2796-2801 ◽  
Author(s):  
Joseph G. Ibrahim ◽  
Haitao Chu ◽  
Liddy M. Chen
Keyword(s):  
Clinical Trials ◽  
Survival Data ◽  
Eastern Cooperative Oncology Group ◽  
Joint Modeling ◽  
Operating Characteristics ◽  
Joint Models ◽  
Free Survival ◽  
Introductory Overview ◽  
Longitudinal And Survival Data

Joint models for longitudinal and survival data are particularly relevant to many cancer clinical trials and observational studies in which longitudinal biomarkers (eg, circulating tumor cells, immune response to a vaccine, and quality-of-life measurements) may be highly associated with time to event, such as relapse-free survival or overall survival. In this article, we give an introductory overview on joint modeling and present a general discussion of a broad range of issues that arise in the design and analysis of clinical trials using joint models. To demonstrate our points throughout, we present an analysis from the Eastern Cooperative Oncology Group trial E1193, as well as examine some operating characteristics of joint models through simulation studies.

scholarly journals Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Lang Wu ◽  
Wei Liu ◽  
Grace Y. Yi ◽  
Yangxin Huang
Keyword(s):  
Survival Data ◽  
Latent Variables ◽  
Measurement Errors ◽  
Joint Modeling ◽  
Likelihood Method ◽  
Survival Models ◽  
Joint Models ◽  
Longitudinal Models ◽  
Time Dependent Covariates ◽  
Longitudinal And Survival Data

In the past two decades, joint models of longitudinal and survival data have received much attention in the literature. These models are often desirable in the following situations: (i) survival models with measurement errors or missing data in time-dependent covariates, (ii) longitudinal models with informative dropouts, and (iii) a survival process and a longitudinal process are associated via latent variables. In these cases, separate inferences based on the longitudinal model and the survival model may lead to biased or inefficient results. In this paper, we provide a brief overview of joint models for longitudinal and survival data and commonly used methods, including the likelihood method and two-stage methods.

scholarly journals Sample size and power determination in joint modeling of longitudinal and survival data

2011 ◽  
Vol 30 (18) ◽  
pp. 2295-2309 ◽  
Author(s):  
Liddy M. Chen ◽  
Joseph G. Ibrahim ◽  
Haitao Chu
Keyword(s):  
Sample Size ◽  
Survival Data ◽  
Joint Modeling ◽  
Longitudinal And Survival Data

scholarly journals Improved dynamic predictions from joint models of longitudinal and survival data with time-varying effects using P-splines

Biometrics ◽  
2017 ◽  
Vol 74 (2) ◽  
pp. 685-693 ◽  
Author(s):  
Eleni-Rosalina Andrinopoulou ◽  
Paul H. C. Eilers ◽  
Johanna J. M. Takkenberg ◽  
Dimitris Rizopoulos
Keyword(s):  
Survival Data ◽  
Time Varying ◽  
Joint Models ◽  
Longitudinal And Survival Data

scholarly journals A Two-Stage Approach for Bayesian Joint Models of Longitudinal and Survival Data: Correcting Bias with Informative Prior

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 50
Author(s):  
Valeria Leiva-Yamaguchi ◽  
Danilo Alvares
Keyword(s):  
Survival Data ◽  
Cox Model ◽  
Sensitivity Analyses ◽  
Simulation Studies ◽  
Joint Models ◽  
Two Stage ◽  
Informative Prior ◽  
Common Term ◽  
Shared Information ◽  
Longitudinal And Survival Data

Joint models of longitudinal and survival outcomes have gained much popularity in recent years, both in applications and in methodological development. This type of modelling is usually characterised by two submodels, one longitudinal (e.g., mixed-effects model) and one survival (e.g., Cox model), which are connected by some common term. Naturally, sharing information makes the inferential process highly time-consuming. In particular, the Bayesian framework requires even more time for Markov chains to reach stationarity. Hence, in order to reduce the modelling complexity while maintaining the accuracy of the estimates, we propose a two-stage strategy that first fits the longitudinal submodel and then plug the shared information into the survival submodel. Unlike a standard two-stage approach, we apply a correction by incorporating an individual and multiplicative fixed-effect with informative prior into the survival submodel. Based on simulation studies and sensitivity analyses, we empirically compare our proposal with joint specification and standard two-stage approaches. The results show that our methodology is very promising, since it reduces the estimation bias compared to the other two-stage method and requires less processing time than the joint specification approach.

scholarly journals Complete Case versus Inverse Probability Weighting Methods of Fitting Incomplete Longitudinal and Survival Data Joint Models

2019 ◽  
pp. 1-10
Author(s):  
D. O. Nyaboga ◽  
A. Mwangi ◽  
D. Lusweti
Keyword(s):  
Missing Data ◽  
Survival Data ◽  
Joint Model ◽  
Information Criteria ◽  
Joint Models ◽  
Full Data ◽  
Complete Case ◽  
Inverse Probability ◽  
Longitudinal And Survival Data ◽  
The Impact

Missing data is a common problem in real word studies especially clinical studies. However, most people working with such data, often drop missing cases from individuals with incomplete observations that occur when patients do not complete the treatment or miss their scheduled visits. This may lead to misleading results and ultimately affect the decision of whether an intervention is good or bad for the patients under treatment. The comparison of Complete Case (CC) and Inverse Probability Weights (IPW) techniques of handling missing data in various models has been addressed, however little has been done to compare these methods when applied to joint models of longitudinal and time to event data. Therefore, this paper seeks to investigate the impact of assuming CC analysis on clinical data with missing cases, comparing it with IPW method when fitting joint models of longitudinal and survival data setting full data model as the baseline model. This paper made use of randomized aids clinical trial data. The model with Deviance Information Criteria (DIC) close to that of full data joint model is considered the best. From the results, joint models from full data, CC and IPW had DIC of 10603.94, 8410.33 and 10600.95 respectively. The joint model obtained from IPW data had a DIC too close to that of full data joint model as compared to model from CC data.

Sign in / Sign up

Export Citation Format

Share Document