ArticleScientific reports2025
A two-stage joint model approach to handle incomplete time dependent markers in survival data through inverse probability weight and multiple imputation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Trajectory-based weight modeling distinguishes pathologic weight loss and predicts adverse events in nonhuman primate allogeneic kidney transplantation.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2026Article
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3 authors.
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Abstract
Joint models for longitudinal and survival data are essential in biomedical research, enabling the simultaneous analysis of biomarker progression and clinical events. These models account for the interdependence between longitudinal and survival outcomes, improving insights into disease progression. However, missing data in longitudinal studies pose challenges, particularly when time dependent markers contain missing values, leading to biased estimates. This paper proposes a two-stage joint modeling framework integrating multiple imputation and inverse probability weighting. First, a linear mixed-effects model estimates biomarker trajectories, handling missing data using multiple imputation. Second, predicted biomarker values are incorporated into a Cox model, where inverse probability weight corrects for selection bias in survival estimation. A detailed simulation study has been conducted to study the performance of the proposed method compared to other common approaches. Results demonstrate the framework's effectiveness in handling incomplete time dependent covariates while providing precise estimates of the relationship between biomarker progression and survival outcomes.
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