Causal estimation of time-varying treatments in observational studies: A scoping review of methods, applications, and missing data practices.

Background: Estimating causal effects of time-varying treatments or exposures in observational studies is challenging due to time-dependent confounding and missing data, necessitating advanced statistical approaches for accurate inference. Previous reviews indicate that singly robust methods are prevalent in epidemiological studies despite the availability of more robust alternatives that better handle time-varying confounding. Although common in longitudinal studies, missing data are often inadequately reported and addressed, potentially compromising the validity of estimates. Whether this dependence on less robust methods and inadequate handling of missing data persists in time-varying treatment settings remains unclear. This review aimed to identify current practices, methodological trends, and gaps in the causal estimation of time-varying treatments. Methods: We conducted a scoping review to map causal methodologies for time-varying treatments in epidemiological studies and identify trends and gaps. To capture the most recent developments, we searched PubMed, Scopus, and Web of Science for articles published between 2023 and 2024. A structured questionnaire was used to extract key methodological aspects, and findings were summarized using descriptive statistics. Results: Of the 424 articles, 63 met the eligibility criteria, with five added from citations and references, totalling 68 for analysis. Among these, 78% addressed epidemiological questions, 13% included methodological illustrations, and 9% focused solely on methods. Singly robust methods dominated, with inverse probability of treatment weighting (IPTW) being the most common (64.3%), followed by targeted maximum likelihood estimation (TMLE) (14.3%). The emergence of new estimation approaches was also noted. Missing data handling remained inadequate; 33% did not report the extent of missingness, 95.2% lacked assumptions, and sensitivity analysis was performed in only 14.5% of the articles. Multiple imputation (MI) was more prevalent (29%), while complete case analysis (11.3%) was likely underreported, given 33.9% omitted strategy details. Conclusion: Persistent reliance on singly robust methods, underutilization of doubly robust approaches, and inadequate missing data handling highlight ongoing gaps in evaluating time-varying treatments. While newer estimation approaches are emerging, their adoption remains limited. These trends, alongside the growing complexity of real-world data and the demand for evidence-driven care, call for greater methodological rigor, wider adoption of robust approaches, and enhanced reporting transparency.