Addresses censoring in survival analysis using imputation in a multistate model framework, highlighting implications in treatment effects.
Censoring is a key issue in survival analysis, especially when participants are lost to follow-up, potentially biasing treatment effect estimates. Imputation methods can help update censored observations, reducing bias. Multistate Models (MSMs) offer a flexible framework for modeling transitions between states over time, capturing dependencies and improving time-to-event comparisons. Traditional Cox models struggle to handle both censoring and multistate dynamics, particularly in the presence of unobserved heterogeneity. By incorporating transition-specific frailty, MSMs better account for individual variability. In this study, we addressed censoring using survival proximity score matching within an MSM framework, incorporating frailty to improve accuracy. Results showed increasing frailty variance across transitions and significant variation in survival probabilities across frailty levels, highlighting the value of frailty-adjusted MSMs.
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Bhattacharjee et al. (2026) studied this question.
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