Joint Modeling of Longitudinal and Survival Data in Public Health and Biomedical Research: A Systematic Review
Article
Wang, W, Bursac, Z, Hu, N. (2026). Joint Modeling of Longitudinal and Survival Data in Public Health and Biomedical Research: A Systematic Review
. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 23(4), 10.3390/ijerph23040492
Wang, W, Bursac, Z, Hu, N. (2026). Joint Modeling of Longitudinal and Survival Data in Public Health and Biomedical Research: A Systematic Review
. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 23(4), 10.3390/ijerph23040492
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 1 January 2019 and 30 January 2025 was conducted using the keywords “joint model”, “joint modeling”, “longitudinal and survival”, “longitudinal and time-to-event”, and “public health”, resulting in 70 methodological studies from 793 records after screening. Original studies proposing methodological innovations in joint modeling were eligible, while clinical applications, reviews, comparative or predictive studies, and articles without full text were excluded. The reviewed methods introduced advances in both 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. In total, 49% of the studies focused on longitudinal sub-model adaptations. This review is subject to limitations, including potential omission of relevant studies due to database, search term, and language restrictions. These developments have enhanced the flexibility of joint models for analyzing complex data structures, particularly in cardiovascular and oncology research, as well as broader public health applications. Despite these advances, challenges remain, including handling high-dimensional sparse data, reducing computational burden, and the lack of standardized evaluation metrics. This research received no external funding.
