Logistic regression with incompletely observed categorical covariates--investigating the sensitivity against violation of the missing at random assumption.

Abstract:

:Missing values in the covariates are a widespread complication in the statistical inference of regression models. The maximum likelihood principle requires specification of the distribution of the covariates, at least in part. For categorical covariates, log-linear models can be used. Additionally, the missing at random assumption is necessary, which excludes a dependence of the occurrence of missing values on the unobserved covariate values. This assumption is often highly questionable. We present a framework to specify alternative missing value mechanisms such that maximum likelihood estimation of the regression parameters under a specified alternative is possible. This allows investigation of the sensitivity of a single estimate against violations of the missing at random assumption. The possible results of a sensitivity analysis are illustrated by artificial examples. The practical application is demonstrated by the analysis of two case-control studies.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Vach W,Blettner M

doi

10.1002/sim.4780141205

subject

Has Abstract

pub_date

1995-06-30 00:00:00

pages

1315-29

issue

12

eissn

0277-6715

issn

1097-0258

journal_volume

14

pub_type

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