A correlated random-effects model for normal longitudinal data with nonignorable missingness

Statistics in Medicine
Huazhen LinXiao-Hua Zhou

Abstract

The missing data problem is common in longitudinal or hierarchical structure studies. In this paper, we propose a correlated random-effects model to fit normal longitudinal or cluster data when the missingness mechanism is nonignorable. Computational challenges arise in the model fitting due to intractable numerical integrations. We obtain the estimates of the parameters based on an accurate approximation of the log likelihood, which has higher-order accuracy but with less computational burden than the existing approximation. We apply the proposed method it to a real data set arising from an autism study.

References

Oct 1, 1992·Statistics in Medicine·M D Schluchter
Jan 15, 1997·Statistics in Medicine·J W Hogan, N M Laird
Sep 14, 2000·Biometrics·P S Albert, D A Follmann
Oct 12, 2004·Biostatistics·Pascal Minini, Michel Chavance

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Citations

May 29, 2012·Statistical Methods in Medical Research·Chi-hong TsengGang Li
May 1, 2018·Statistical Science : a Review Journal of the Institute of Mathematical Statistics·Antonio R Linero, Michael J Daniels
Sep 3, 2011·Biometrical Journal. Biometrische Zeitschrift·Antonello Maruotti
Jun 27, 2012·Statistics in Medicine·Baojiang Chen, Xiao-Hua Zhou

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