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Multiple imputation of missing covariates for the Cox proportional hazards cure model. 2016

Lauren J Beesley, and Jonathan W Bartlett, and Gregory T Wolf, and Jeremy M G Taylor
Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A.. lbeesley@umich.edu.

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.

UI MeSH Term Description Entries
D003627 Data Interpretation, Statistical Application of statistical procedures to analyze specific observed or assumed facts from a particular study. Data Analysis, Statistical,Data Interpretations, Statistical,Interpretation, Statistical Data,Statistical Data Analysis,Statistical Data Interpretation,Analyses, Statistical Data,Analysis, Statistical Data,Data Analyses, Statistical,Interpretations, Statistical Data,Statistical Data Analyses,Statistical Data Interpretations
D006258 Head and Neck Neoplasms Soft tissue tumors or cancer arising from the mucosal surfaces of the LIP; oral cavity; PHARYNX; LARYNX; and cervical esophagus. Other sites included are the NOSE and PARANASAL SINUSES; SALIVARY GLANDS; THYROID GLAND and PARATHYROID GLANDS; and MELANOMA and non-melanoma skin cancers of the head and neck. (from Holland et al., Cancer Medicine, 4th ed, p1651) Cancer of Head and Neck,Head Cancer,Head Neoplasm,Head and Neck Cancer,Head and Neck Neoplasm,Neck Cancer,Neck Neoplasm,Neck Neoplasms,Neoplasms, Upper Aerodigestive Tract,UADT Neoplasm,Upper Aerodigestive Tract Neoplasm,Upper Aerodigestive Tract Neoplasms,Cancer of Head,Cancer of Neck,Cancer of the Head,Cancer of the Head and Neck,Cancer of the Neck,Head Neoplasms,Head, Neck Neoplasms,Neoplasms, Head,Neoplasms, Head and Neck,Neoplasms, Neck,UADT Neoplasms,Cancer, Head,Cancer, Neck,Cancers, Head,Cancers, Neck,Head Cancers,Neck Cancers,Neoplasm, Head,Neoplasm, Neck,Neoplasm, UADT,Neoplasms, UADT
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D015982 Bias Any deviation of results or inferences from the truth, or processes leading to such deviation. Bias can result from several sources: one-sided or systematic variations in measurement from the true value (systematic error); flaws in study design; deviation of inferences, interpretations, or analyses based on flawed data or data collection; etc. There is no sense of prejudice or subjectivity implied in the assessment of bias under these conditions. Aggregation Bias,Bias, Aggregation,Bias, Ecological,Bias, Statistical,Bias, Systematic,Ecological Bias,Outcome Measurement Errors,Statistical Bias,Systematic Bias,Bias, Epidemiologic,Biases,Biases, Ecological,Biases, Statistical,Ecological Biases,Ecological Fallacies,Ecological Fallacy,Epidemiologic Biases,Experimental Bias,Fallacies, Ecological,Fallacy, Ecological,Scientific Bias,Statistical Biases,Truncation Bias,Truncation Biases,Bias, Experimental,Bias, Scientific,Bias, Truncation,Biase, Epidemiologic,Biases, Epidemiologic,Biases, Truncation,Epidemiologic Biase,Error, Outcome Measurement,Errors, Outcome Measurement,Outcome Measurement Error
D016016 Proportional Hazards Models Statistical models used in survival analysis that assert that the effect of the study factors on the hazard rate in the study population is multiplicative and does not change over time. Cox Model,Cox Proportional Hazards Model,Hazard Model,Hazards Model,Hazards Models,Models, Proportional Hazards,Proportional Hazard Model,Proportional Hazards Model,Cox Models,Cox Proportional Hazards Models,Hazard Models,Proportional Hazard Models,Hazard Model, Proportional,Hazard Models, Proportional,Hazards Model, Proportional,Hazards Models, Proportional,Model, Cox,Model, Hazard,Model, Hazards,Model, Proportional Hazard,Model, Proportional Hazards,Models, Cox,Models, Hazard,Models, Hazards,Models, Proportional Hazard

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