A two-stage joint model approach to handle incomplete time dependent markers in survival data through inverse probability weight and multiple imputation

Gajendra K. Vishwakarma; Atanu Bhattacharjee; Bhrigu Kumar Rajbongshi

doi:10.1038/s41598-025-11125-4

A two-stage joint model approach to handle incomplete time dependent markers in survival data through inverse probability weight and multiple imputation

Gajendra K. Vishwakarma
, Atanu Bhattacharjee (Lead / Corresponding author)
, Bhrigu Kumar Rajbongshi (Lead / Corresponding author)

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Article number	33949
Journal	Scientific Reports
Volume	15
Issue number	1
Early online date	30 Sept 2025
DOIs	https://doi.org/10.1038/s41598-025-11125-4
Publication status	Published - Dec 2025

Keywords

Inverse probability weight
Joint model
Missing data
Multiple imputation

ASJC Scopus subject areas

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Cite this

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title = "A two-stage joint model approach to handle incomplete time dependent markers in survival data through inverse probability weight and multiple imputation",

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{\textquoteright}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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A two-stage joint model approach to handle incomplete time dependent markers in survival data through inverse probability weight and multiple imputation. / Vishwakarma, Gajendra K.; Bhattacharjee, Atanu (Lead / Corresponding author); Rajbongshi, Bhrigu Kumar (Lead / Corresponding author).
In: Scientific Reports, Vol. 15, No. 1, 33949, 12.2025.

Research output: Contribution to journal › Article › peer-review

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AU - Vishwakarma, Gajendra K.

AU - Bhattacharjee, Atanu

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A two-stage joint model approach to handle incomplete time dependent markers in survival data through inverse probability weight and multiple imputation

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