ArticleStatistics in medicine2016
Multiple imputation of missing covariates for the Cox proportional hazards cure model.
Article in Statistics in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Accommodating the Analysis Model in Multiple Imputation for the Weibull Mixture Cure Model: Performance Under Penalized Likelihood.Statistics in medicine · 2026Article
- Multiple Imputation of Missing Covariates When Using the Fine-Gray Model.Statistics in medicine · 2025Article
- Real-world efficacy and safety outcomes of imatinib treatment in patients with chronic myeloid leukemia: An Australian experience.Pharmacology research & perspectives · 2022Article
- Article
- Multiple imputation with missing data indicators.Statistical methods in medical research · 2021Article
- A stacked approach for chained equations multiple imputation incorporating the substantive model.Biometrics · 2021Article
- Clinical variables and magnetic resonance imaging-based radiomics predict human papillomavirus status of oropharyngeal cancer.Head & neck · 2021Article
- EM algorithms for fitting multistate cure models.Biostatistics (Oxford, England) · 2019Article
- Cox regression model with randomly censored covariates.Biometrical journal. Biometrische Zeitschrift · 2019Article
- Individual and Population Comparisons of Surgery and Radiotherapy Outcomes in Prostate Cancer Using Bayesian Multistate Models.JAMA network open · 2019Article
- Pretreatment Hemoglobin Adds Prognostic Information To The NCCN-IPI In Patients With Diffuse Large B-Cell Lymphoma Treated With Anthracycline-Containing Chemotherapy.Clinical epidemiology · 2019Article
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Authors and funding
4 authors.
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Abstract
We explore several approaches for imputing partially observed covariates when the outcome of interest is a censored event time and when there is an underlying subset of the population that will never experience the event of interest. We call these subjects 'cured', and we consider the case where the data are modeled using a Cox proportional hazards (CPH) mixture cure model. We study covariate imputation approaches using fully conditional specification. We derive the exact conditional distribution and suggest a sampling scheme for imputing partially observed covariates in the CPH cure model setting. We also propose several approximations to the exact distribution that are simpler and more convenient to use for imputation. A simulation study demonstrates that the proposed imputation approaches outperform existing imputation approaches for survival data without a cure fraction in terms of bias in estimating CPH cure model parameters. We apply our multiple imputation techniques to a study of patients with head and neck cancer. Copyright © 2016 John Wiley & Sons, Ltd.
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