ArticleBiostatistics (Oxford, England)2016
Sieve estimation in a Markov illness-death process under dual censoring.
Article in Biostatistics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- An Overview and Recent Developments in the Analysis of Multistate Processes.Statistics in medicine · 2026Review
- Specification of Estimands for Complex Disease Processes Using Multistate Models and Utility Functions.Statistics in medicine · 2025Article
- Statistical approaches for component-wise censored composite endpoints.Clinical trials (London, England) · 2024Article
- The heterogeneity effect of surveillance intervals on progression free survival.Journal of applied statistics · 2024Article
- Performance of Cox regression models for composite time-to-event endpoints with component-wise censoring in randomized trials.Clinical trials (London, England) · 2023Article
- Analysis of composite endpoints with component-wise censoring in the presence of differential visit schedules.Statistics in medicine · 2022Article
- Joint modelling of colorectal cancer recurrence and death after resection using multi-state model with cured fraction.Scientific reports · 2021Article
- Current recommendations on the estimation of transition probabilities in Markov cohort models for use in health care decision-making: a targeted literature review.ClinicoEconomics and outcomes research : CEOR · 2017Review
- Bias in progression-free survival analysis due to intermittent assessment of progression.Statistics in medicine · 2015Article
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Authors and funding
2 authors.
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Abstract
Semiparametric methods are well established for the analysis of a progressive Markov illness-death process observed up to a noninformative right censoring time. However, often the intermediate and terminal events are censored in different ways, leading to a dual censoring scheme. In such settings, unbiased estimation of the cumulative transition intensity functions cannot be achieved without some degree of smoothing. To overcome this problem, we develop a sieve maximum likelihood approach for inference on the hazard ratio. A simulation study shows that the sieve estimator offers improved finite-sample performance over common imputation-based alternatives and is robust to some forms of dependent censoring. The proposed method is illustrated using data from cancer trials.
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