Scientific Data Services

"Breaking up is hard (to model): Joint models for longitudinal and time-to-event data on divorce data and rare events" is the title of Sophie Potts talk, which is being organised by the "Zentrum of Statistik" at Bielefeld University. It will take place on Tuesday, 18 November 2025 from 12:00 - 13:00 in W9-109. 

In time-to-event analyses in social sciences, endogenous time-varying variables often occur, where the event status is correlated with the covariate’s trajectory. Ignoring this causes biased estimates. In biostatistics, this is addressed with joint models for longitudinal and time-to-event data, which handle endogeneity correctly. Despite their usefulness, joint models remain rare in social sciences. We give an introduction to this method, highlight its advantages for social sciences, and demonstrate it with an example on marital satisfaction and dissolution, comparing results with classical time-to-event models. In a second part, we address rare events in joint models. Standard estimation struggles with few events and imbalanced designs, leading to monotone likelihood. Firth’s correction, which adjusts the score function, provides stable estimates even with very small event counts. We extend this correction to joint models, enabling their use in rare event studies, particularly in observational data where event numbers cannot be controlled.