Multinomial logistic regression with missing outcome data: An application to cancer subtypes.

ABSTRACT: Many diseases such as cancer and heart diseases are heterogeneous and it is of great interest to study the disease risk specific to the subtypes in relation to genetic and environmental risk factors. However, due to logistic and cost reasons, the subtype information for the disease is missing for some subjects. In this article, we investigate methods for multinomial logistic regression with missing outcome data, including a bootstrap hot deck multiple imputation (BHMI), simple inverse probability weighted (SIPW), augmented inverse probability weighted (AIPW), and expected estimating equation (EEE) estimators. These methods are important approaches for missing data regression. The BHMI modifies the standard hot deck multiple imputation method such that it can provide valid confidence interv

SUBMITTER: Wang CY 

PROVIDER: S-EPMC7736568 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature