Multinomial logistic regression with missing outcome data: an application to cancer subtypes
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Publication:6627618
DOI10.1002/sim.8666zbMath1546.62785MaRDI QIDQ6627618
Publication date: 29 October 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
Cites Work
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- Numerical equivalence of imputing scores and weighted estimators in regression analysis with missing covariates
- A note on kernel assisted estimators in missing covariate regression
- Inference and missing data
- Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
- Multiple Imputation for Interval Estimation From Simple Random Samples With Ignorable Nonresponse
- Expected Estimating Equations for Missing Data, Measurement Error, and Misclassification, with Application to Longitudinal Nonignorable Missing Data
- Weighted Estimators for Proportional Hazards Regression With Missing Covariates
- A review of hot deck imputation for survey non-response
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