August 7, 2025

Joint Modeling of Zero-Inflated Longitudinal Count Data and Survival Outcomes with Cure Fraction

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Joint modeling consistently reduces bias in parameter estimates compared to separate models.
ZINB joint models handle high zero-inflation better than ZIP models, showing less bias.
Increased sample size of 800 helps to further reduce bias compared to a sample of 500.
Lower cure rates enhance estimation accuracy in joint modeling scenarios.

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Simulation analysis demonstrates joint modeling improves parameter estimates in zero-inflated longitudinal and survival data with cure fraction.

Pilangorgi et al. (2025) studied this question.

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Simulation analysis demonstrates joint modeling improves parameter estimates in zero-inflated longitudinal and survival data with cure fraction.