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Cited by 1,595

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data

Robins, James M., Rotnitzky, Andrea, Zhao, Lue Ping·1995·Journal of the American Statistical Association
DOI 10.1080/01621459.1995.10476493
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Abstract

We propose a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on a vector of explanatory variables in the presence of missing response data. The proposed estimators do not require full specification of the likelihood. They can be viewed as an extension of generalized estimating equations estimators that allow for the data to be missing at random but not missing completely at random. These estimators can be used to correct for dependent censoring and nonrandom noncompliance in randomized clinical trials studying the effect of a treatment on the evolution over time of the mean of a response variable. The likelihood-based parametric G-computation algorithm estimator may also be used to attempt to correct for dependent censoring and nonrandom noncompliance. But because of possible model misspecification, the parametric G-computation algorithm estimator, in contrast with the proposed weighted estimators, may be inconsistent for the difference in treatment-arm-specific means, even when compliance is completely at random and censoring is independent. We illustrate our methods with the analysis of the effect of zidovudine (AZT) treatment on the evolution of mean CD4 count with data from an AIDS clinical trial.

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Cites

Show all 7 references

Research works printed but not linked: 45

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Cited by

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Metadata

Authors
Robins, J. M., Rotnitzky, A., & Zhao, L. P.
Venue
Journal of the American Statistical Association
Publisher
Informa UK Limited
Published
1995-03
Volume
90
Issue
429
Pages
106-121
ISSN
  • 0162-1459 (ISSN-L)
  • 1537-274X
DOI
10.1080/01621459.1995.10476493
OpenAlex
W2116739230
Cited by
1,595
References
7
FWCI
10.41
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data. https://app.cochat.ai/papers/10.1080/01621459.1995.10476493
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Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1995). Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1995.10476493

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