Abstract
HIGHLIGHTS: Public health relevance—How does this work relate to a public health issue? Cardiovascular disease and cancer are leading causes of morbidity and mortality worldwide, often characterized by longitudinal risk factors and survival outcomes such as disease onset, progression, or death. This review examines recent advances in joint modeling methods developed across major public health areas, with emphasis on cardiovascular and cancer research, as well as applications in other chronic and complex diseases. Public health significance—Why is this work of significance to public health? Joint modeling can reduce bias, improve estimation, and strengthen risk prediction when longitudinal and survival outcomes are related. This review summarizes recent methodological advances and shows different joint modeling sub-model approaches to address complex public health data structures. Public health implications—What are the key implications or messages for practitioners, policy makers and/or researchers in public health? The review provides practical guidance for choosing longitudinal and survival sub-models based on the research question and data characteristics, particularly for cardiovascular and cancer applications. Better use of joint models can support more accurate risk assessment, clearer interpretation of the relationships between longitudinal biomarkers and survival outcomes, and more informed public health research and decision-making. ABSTRACT: 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.