ArticleBiostatistics (Oxford, England)2019
EM algorithms for fitting multistate cure models.
Article in Biostatistics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Defining and modeling 'cure' in early-stage melanoma: perspectives from a systematic literature review.Frontiers in oncology · 2026Pooled it
- No cure for cure models: killed by competing risks.Lifetime data analysis · 2026Article
- Multiple imputation strategies for missing event times in a multi-state model analysis.Statistics in medicine · 2024Article
- Surrogacy validation for time-to-event outcomes with illness-death frailty models.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Estimating the cure proportion of stage IA lung adenocarcinoma: a population-based study.BMC pulmonary medicine · 2023Article
- Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach.Biostatistics (Oxford, England) · 2022Article
- The Log-Normal zero-inflated cure regression model for labor time in an African obstetric population.Journal of applied statistics · 2022Article
- Development and Assessment of a Model for Predicting Individualized Outcomes in Patients With Oropharyngeal Cancer.JAMA network open · 2021Article
- Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain).Epidemiology and infection · 2021Article
- Joint modelling of colorectal cancer recurrence and death after resection using multi-state model with cured fraction.Scientific reports · 2021Article
- Time-to-event analysis when the event is defined on a finite time interval.Statistical methods in medical research · 2020Article
- Individualized survival prediction for patients with oropharyngeal cancer in the human papillomavirus era.Cancer · 2019Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Multistate cure models are multistate models in which transitions into one or more of the states cannot occur for a fraction of the population. In the study of cancer, multistate cure models can be used to identify factors related to the rate of cancer recurrence, the rate of death before and after recurrence, and the probability of being cured by initial treatment. However, the previous method for fitting multistate cure models requires substantial custom programming, making these valuable models less accessible to analysts. In this article, we present an Expectation-Maximization (EM) algorithm for fitting the multistate cure model using maximum likelihood. The proposed algorithm makes use of a weighted likelihood representation allowing it to be easily implemented with standard software and can incorporate either parametric or non-parametric baseline hazards for the state transition rates. A common complicating feature in cancer studies is that the follow-up times for recurrence and death may differ. Additionally, we may have missingness in the covariates. We propose a Monte Carlo EM (MCEM) algorithm for fitting the multistate cure model in the presence of covariate missingness and/or unequal follow-up of the two outcomes, we describe a novel approach for obtaining standard errors, and we provide some software. Simulations demonstrate good algorithmic performance as long as the modeling assumptions are sufficiently restrictive. We apply the proposed algorithm to a study of recurrence and death in patients with head and neck cancer.
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