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stat.ME2026

Testing for sufficient follow-up in cure models with categorical covariates

Tsz Pang Yuen, Eni Musta, Ingrid Van Keilegom

In survival analysis, estimating the fraction of 'immune' or 'cured' subjects who will never experience the event of interest, requires a sufficiently long follow-up period. A few…

stat.ME2026

Testing for sufficient follow-up in survival data with a cure fraction

Tsz Pang Yuen, Eni Musta

In order to estimate the proportion of `immune' or `cured' subjects who will never experience failure, a sufficiently long follow-up period is required. Several statistical tests h…

stat.ME2025

Can we detect treatment effect waning from time-to-event data?

Eni Musta, Joris Mooij

Understanding how the causal effect of a treatment evolves over time, including the potential for waning, is important for informed decisions on treatment discontinuation or repeti…

stat.ME2025

A mover-stayer model with time-dependent stayer fraction

Eni Musta, Martina Vittorietti

Mover-stayer models are used in social sciences and economics to model heterogeneous population dynamics in which some individuals never experience the event of interest ("stayers"…

stat.ME2024

A multiple imputation approach to distinguish curative from life-prolonging effects in the presence of missing covariates

Marta Cipriani, Marta Fiocco, Marco Alfò +2

Medical advances have increased cancer survival rates and the possibility of finding a cure. Hence, it is crucial to evaluate the impact of treatments both in terms of cure and pro…