Abstract
In this article, we propose a new method for analyzing longitudinal data which contain responses that are missing at random. This method consists in solving the generalized estimating equation (GEE) of [8] in which the incomplete responses are replaced by values adjusted using the inverse probability weights proposed in [17]. We show that the root estimator is consistent and asymptotically normal, essentially under the some conditions on the marginal distribution and the surrogate correlation matrix as those presented in [15] in the case of complete data, and under minimal assumptions on the missingness probabilities. This method is applied to a real-life data set taken from [13], which examines the incidence of respiratory disease in a sample of 250 pre-school age Indonesian children which were examined every 3 months for 18 months, using as covariates the age, gender, and vitamin A deficiency.
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Research supported by a grant from the Natural Sciences and Engineering Research Council of Canada.
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Balan, R.M., Jankovic, D. Asymptotic Theory for Longitudinal Data with Missing Responses Adjusted by Inverse Probability Weights. Math. Meth. Stat. 28, 83–103 (2019). https://doi.org/10.3103/S1066530719020017
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DOI: https://doi.org/10.3103/S1066530719020017
Keywords
- longitudinal data
- generalized estimating equations
- asymptotic properties
- missing at random
- inverse probability weights