LOGISTIC-REGRESSION WITH INCOMPLETELY OBSERVED CATEGORICAL COVARIATES INVESTIGATING THE SENSITIVITY AGAINST VIOLATION OF THE MISSING AT RANDOM ASSUMPTION

被引：49

作者：

VACH, W ^{[1
]}

BLETTNER, M ^{[1
]}

机构：

[1] GERMAN CANC RES CTR,DEPT EPIDEMIOL & BIOMETRY,D-69120 HEIDELBERG,GERMANY

来源：

STATISTICS IN MEDICINE | 1995年 / 14卷 / 12期

关键词：

D O I：

10.1002/sim.4780141205

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页码：1315 / 1329

页数：15

共 22 条

[1]

Vach W., Blettner M., Biased estimation of the odds ratio in case‐control studies due to the use of ad‐hoc methods of correcting for missing values in confounding variables, American Journal of Epidemiology, 134, pp. 895-907, (1991)

[2]

Rubin D.B., Inference and missing data, Biometrika, 63, pp. 581-592, (1976)

[3]

Dempster A.P., Laird N.M., Rubin D.B., Maximum likelihood estimation from incomplete data via EM algorithm (with discussion), Journal of the Royal Statistical Society, Series B, 39, pp. 1-38, (1977)

[4]

Little R.J.A., Rubin D.B., Statistical Analysis with Missing Data, (1987)

[5]

Ibrahim J.G., Incomplete data in generalized linear models, Journal of the American Statistical Association, 85, pp. 765-769, (1990)

[6]

Bishop Y.M.M., Fienberg S.E., Holland P.W., Discrete Multivariate Analyses: Theory and Practice, (1975)

[7]

Pregibon D., Typical survey data: estimation and imputation, Survey Methodology, 2, pp. 79-102, (1977)

[8]

Little R.J.A., Models for nonresponse in sample surveys, Journal of the American Statistical Association, 77, pp. 237-250, (1982)

[9]

Little R.J.A., The nonignorable case, Incomplete Data in Sample Surveys, 2, pp. 383-413, (1983)

[10]

Nordheim E.V., Inference from nonrandomly missing categorical data: An example from a genetic study on Turner's syndrome, Journal of the American Statistical Association, 79, pp. 772-780, (1984)

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