A profile likelihood approach for longitudinal data analysis

A profile likelihood approach for longitudinal data analysis IntroductionLongitudinal data arise frequently in biomedical and health studies in which repeated measurements from the same subject are correlated. Consistency and efficiency of estimators for the regression parameters are important for longitudinal data analysis. Liang and Zeger () developed the generalized estimating equation (GEE) for longitudinal data analysis. GEE approach takes advantage of the built‐in robustness since no specification of the full likelihood is required. It is well known that GEE estimators are efficient when the working correlation structure is correctly specified. However, misspecification of the working correlation structure may lead to a great loss of efficiency even though the consistency may remain valid (Wang and Carey, ). The quadratic inference function (QIF) method proposed by Qu, Lindsay, and Li () does not involve direct estimation of the correlation matrix and remains optimal even if the working correlation matrix is misspecified. Ye and Pan () proposed the simultaneous GEE equations to estimate both the mean regression coefficients and the covariance structure parameters. Leung, Wang, and Zhu () proposed a hybrid method that combines multiple GEEs based on different working correlation models and obtains parameter estimates by maximizing the empirical likelihood (Qin and Lawless, ). Nonetheless, all aforementioned articles require http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

A profile likelihood approach for longitudinal data analysis

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Publisher
Wiley
Copyright
© 2018, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
D.O.I.
10.1111/biom.12712
Publisher site
See Article on Publisher Site

Abstract

IntroductionLongitudinal data arise frequently in biomedical and health studies in which repeated measurements from the same subject are correlated. Consistency and efficiency of estimators for the regression parameters are important for longitudinal data analysis. Liang and Zeger () developed the generalized estimating equation (GEE) for longitudinal data analysis. GEE approach takes advantage of the built‐in robustness since no specification of the full likelihood is required. It is well known that GEE estimators are efficient when the working correlation structure is correctly specified. However, misspecification of the working correlation structure may lead to a great loss of efficiency even though the consistency may remain valid (Wang and Carey, ). The quadratic inference function (QIF) method proposed by Qu, Lindsay, and Li () does not involve direct estimation of the correlation matrix and remains optimal even if the working correlation matrix is misspecified. Ye and Pan () proposed the simultaneous GEE equations to estimate both the mean regression coefficients and the covariance structure parameters. Leung, Wang, and Zhu () proposed a hybrid method that combines multiple GEEs based on different working correlation models and obtains parameter estimates by maximizing the empirical likelihood (Qin and Lawless, ). Nonetheless, all aforementioned articles require

Journal

BiometricsWiley

Published: Jan 1, 2018

Keywords: ; ; ;

References

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