Large sample results for frequentist multiple imputation for Cox regression with missing covariate data

DOI10.1007/s10463-019-00716-4zbMath1476.62231OpenAlexW2934914099MaRDI QIDQ778878

Frank Eriksson, Søren Feodor Nielsen, Torben Martinussen

Publication date: 20 July 2020

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://research.cbs.dk/en/publications/306d1ba7-afee-47a4-a6ee-426fa8399d59

zbMATH Keywords

survivalasymptotic distributionvariance estimatorsemiparametriccoarsened data

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Applications of statistics to biology and medical sciences; meta analysis (62P10) General nonlinear regression (62J02) Estimation in survival analysis and censored data (62N02) Missing data (62D10)

Related Items (1)

Gene-environment interaction analysis under the Cox model

Cites Work

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• Asymptotic Statistics
• Large-sample theory for parametric multiple imputation procedures
• Double-Semiparametric Method for Missing Covariates in Cox Regression Models
• Proportional Hazards Regression with Missing Covariates
• Likelihood-Based Methods for Missing Covariates in the Cox Proportional Hazards Model
• Proper and Improper Multiple Imputation
• Inference for imputation estimators
• Cox Regression with Incomplete Covariate Measurements using the EM‐algorithm
• Weighted Estimators for Proportional Hazards Regression With Missing Covariates

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