Bridging the gap: Introducing joint models for longitudinal and time-to-event data in the social sciences [Author Accepted Manuscript]

In time-to-event analyses in social sciences, there often exist endogenous time-varying variables, where the event status is correlated with the trajectory of the covariate itself. Ignoring this endogeneity will result in biased estimates. In the field of biostatistics this issue is tackled by estimating a joint model for longitudinal and time-to-event data as it handles endogenous covariates properly. This method is underused in the social sciences even though it is very useful to model longitudinal and time-to-event processes appropriately. Therefore, this paper provides a gentle introduction to the method of joint models and highlights its advantages for social science research questions. We demonstrate its usage on an example on marital satisfaction and marriage dissolution and compare the results with classical approaches such as a time-to-event model with a time-varying covariate. In addition to demonstrating the method, our results contribute to the understanding of the relationship between marriage satisfaction, marriage dissolution and other covariates.