Joint modeling of longitudinal and time-to-event data in public health and biomedical research: A systematic review

We conducted a PRISMA-guided systematic review to summarize recent methodological advances in joint modeling. A PubMed search for English-language, peer-reviewed, full-text available articles published between January 1, 2019, and January 31, 2025, using the keywords “joint modeling,” “longitudinal and survival,” and “public health”, result-ing 57 methodological studies from 456 records after screening. Original studies propos-ing methodological innovations in joint modeling were eligible, while clinical applica-tions, reviews, comparative or predictive studies, and articles without full text were ex-cluded. The reviewed methods introduced novel developments in longitudinal and/or survival sub-models, including generalized linear mixed models, functional or latent class approaches, and flexible survival models such as frailty, accelerated failure time, B-spline, and competing risks models. 42% of the studies focused on longitudinal sub-model adaptations. This review is subject to limitations, including potential omission of relevant studies due to databases, search term, and language restrictions. The advances in joint modeling methods have improved the flexibility of joint models for complex public health data, although challenges remain related to high-dimensional sparse data, computational burden, and the lack of standardized evaluation metrics. This research received no external funding.