Abstract
It is well known that in the complete longitudinal setup, the so-called working correlation-based generalized estimating equations (GEE) approach may yield less efficient regression estimates as compared to the independence assumption-based method of moments and quasi-likelihood (QL) estimates. In the incomplete longitudinal setup, there exist some studies indicating that the use of the same “working” correlation-based GEE approach may provide inconsistent regression estimates especially when the longitudinal responses are at risk of being missing at random (MAR). In this paper, we revisit this inconsistency issue under a longitudinal binary model and empirically examine the relative performance of the existing weighted (by inverse probability weights for the missing indicator) GEE (WGEE), a fully standardized GQL (FSGQL) and conditional GQL (CGQL) approaches. In the comparative study, we consider both stationary and non-stationary covariates, as well as various degrees of missingness and longitudinal correlation in the data.
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References
Birmingham, J., Rotnitzky, A., Fitzmaurice, G.M.: Pattern-mixture and selection models for analysing longitudinal data with monotone missing patterns. J. R. Stat. Soc. B 65, 275–297 (2003)
Fitzmaurice, G.M., Laird, N.M., Zahner, G.E.P.: Multivariate logistic models for incomplete binary responses. J. Am. Stat. Assoc. 91, 99–108 (1996)
Liang, K.-Y., Zeger, S.L.: Longitudinal data analysis using generalized linear models. Biometrika 73, 13–22 (1986)
Paik, M.C.: The generalized estimating equation approach when data are not missing completely at random. J. Am. Stat. Assoc. 92, 1320–1329 (1997)
Qaqish, B.F.: A family of multivariate binary distributions for simulating correlated binary variables with specified marginal means and correlations. Biometrika 90, 455–463 (2003)
Robins, J.M., Rotnitzky, A., Zhao, L.P.: Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. J. Am. Stat. Assoc. 90, 106–121 (1995)
Rotnitzky, A., Robins, M., Scharfstein, D.O.: Semi-parametric regression for repeated outcomes with nonignorable nonresponse. J. Am. Stat. Assoc. 93, 1321–1339 (1998)
Rubin, D.B.: Inference and missing data. Biometrika 63, 581–592 (1976)
Sutradhar, B.C.: An overview on regression models for discrete longitudinal responses. Stat. Sci. 18, 377–93 (2003)
Sutradhar, B.C.: Dynamic Mixed Models for Familial Longitudinal Data. Springer, New York (2011)
Sutradhar, B.C.: Inference progress in missing data analysis from independent to longitudinal setup. In: Sutradhar, B.C. (ed.) ISS-2012 Proceedings volume on longitudinal data analysis subject to measurement errors, missing values, and/or outliers, Springer Lecture Notes Series, pp. 101–123 (2013)
Sutradhar, B.C., Mallick, T.S.: Modified weights based generalized quasilikelihood inferences in incomplete longitudinal binary models. Can. J. Stat. 38, 217–231 (2010)
Zeger, S.L, Liang, K.-Y., Self, S.G.: The analysis of binary longitudinal data with time-independent covariates. Biometrika 72, 31–38 (1985)
Acknowledgements
The authors would like to thank the audience of the symposium for their comments and suggestions. The authors would also like to thank a referee for valuable comments on the original submission. The research program of Patrick J. Farrell is supported by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC).
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Mallick, T.S., Farrell, P.J., Sutradhar, B.C. (2013). Consistent Estimation in Incomplete Longitudinal Binary Models. In: Sutradhar, B. (eds) ISS-2012 Proceedings Volume On Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers. Lecture Notes in Statistics(), vol 211. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6871-4_6
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DOI: https://doi.org/10.1007/978-1-4614-6871-4_6
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