Abstract
Objective: Using the impact of chronic graft-versus-host disease (cGVHD) on overall survival after hematopoietic stem cell transplantation as an example, this study aims to introduces and critically appraises five statistical approaches for handling time-dependent events in survival analysis. Methods: This study was based on data from the Center for International Blood and Marrow Transplant Research (CIBMTR) GV18-03 study. A total of 4361 patients with acute myeloid leukemia or myelodysplastic syndrome were included, who were aged ≥40 years and had received an HLA 8/8 matched sibling donor transplant between 2008 and 2017. Five analytical approaches were used, treating cGVHD as: ① a baseline fixed covariate, ② a time-dependent covariate, ③ a time-dependent event via landmarking analysis, ④ a transitional event in a multi-state model, and ⑤ an exposure in the parametric g-formula. For each approach, we presented its core concept and results, and elaborated on its rationale, strengths, limitations, and applicable scenarios. Results: The five approaches showed significant differences in bias control, modeling flexibility, and clinical interpretability. Method 1 was prone to immortal time bias. Method 2 partially corrected this bias but was vulnerable to informative censoring and estimation instability due to insufficient sample sizes in the early-onset cGVHD group. Method 3 was straightforward but dependent on a predefined landmark time point and unable to account for cGVHD occurring after this point. Method 4 allowed for a comprehensive description of the impact of time-dependent events on survival prognosis by modeling dynamic clinical transitions. Method 5 could simultaneously handle both time-dependent events and confounders, making it suitable for estimating population-level effects of specific exposure strategies. Conclusion: Researchers should select the appropriate method based on their specific study objectives to ensure that the estimation of the impact of time-dependent events on outcomes is both statistically valid and clinically meaningful.