Review and Comparison of Computational Approaches for Joint Longitudinal and Time‐to‐Event Models

Abstract

Summary Joint models for longitudinal and time‐to‐event data are useful in situations where an association exists between a longitudinal marker and an event time. These models are typically complicated due to the presence of shared random effects and multiple submodels. As a consequence, software implementation is warranted that is not prohibitively time consuming. While methodological research in this area continues, several statistical software procedures exist to assist in the fitting of some joint models. We review the available implementation for frequentist and Bayesian models in the statistical programming languages R, SAS and Stata. A description of each procedure is given including estimation techniques, input and data requirements, available options for customisation and some available extensions, such as competing risks models. The software implementations are compared and contrasted through extensive simulation, highlighting their strengths and weaknesses. Data from an ongoing trial on adrenal cancer patients are used to study different nuances of software fitting on a practical example.

Age and life expectancy clocks based on machine learning analysis of mouse frailty

“…The predictive power of these models for both age and lifespan could be improved by the inclusion of larger n values (especially at the older ages), the assessment of frailty from ages younger than 21 months, and more complex modeling of the longitudinal aspects of our data. In the current study, we have used standard fixed-time predictive models treating each time point for each mouse as independent data, as there is currently no standard method for predicting outcomes at the level of the individual from data collected longitudinally 56,57 . Future studies could apply dynamic prediction approaches from the clinical biostatistics literature such as joint modeling 57,58 to develop models based on repeated measures of markers from the same mice.…”

“…In the current study, we have used standard fixed-time predictive models treating each time point for each mouse as independent data, as there is currently no standard method for predicting outcomes at the level of the individual from data collected longitudinally 56 , 57 . Future studies could apply dynamic prediction approaches from the clinical biostatistics literature such as joint modeling 57 , 58 to develop models based on repeated measures of markers from the same mice. The models discussed in this study could also benefit from the incorporation of additional input variables, especially from relatively non-invasive molecular and physiological biomarkers or biometrics.…”

Genetics of physiological dysregulation: findings from the long life family study using joint models

“…Joint models is an active area of research in statistics with numerous extensions of the basic model (analyzed in this paper) suggested in the literature that cover a wide range of research applications such as latent classes, competing risks, multivariate models, nonlinear models, dynamic predictions, stochastic processes, etc. (see books [15,16] and recent review papers and tutorials [25][26][27][28][29][30][31][32][33]). Such extended models can be applied to analyze dynamic characteristics of composite measures such as DM with various outcomes in more comprehensive ways.…”

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

“…Based on the simulation scenarios proposed by Furgal et al [ 8 ], we adopt the following hazard specification for individual i : where the baseline hazard function has an exponential specification, . Note that other options for this function could be preferred, such as Gamma, Weibull, Gompertz, log-normal, log-logistic, piecewise, splines, and so forth [ 42 , 43 ].…”

“…In this paper, we focus on general contexts in which longitudinal measurements are observed strictly before the survival time [ 6 ]. This framework has been analysed in several applications, see References [ 7 , 8 , 9 ] for a review on joint models up to date, and it has at least two drawbacks: (i) identifiability problems due to the large number of parameters [ 7 , 10 , 11 , 12 , 13 ] and (ii) requirement for numerical integrations that can make the inferential process time-consuming [ 14 , 15 , 16 , 17 , 18 ].…”