Analysis of Longitudinal Data with Missing Responses Adjusted by Inverse Probability Weights

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Title: Analysis of Longitudinal Data with Missing Responses Adjusted by Inverse Probability Weights
Authors: Jankovic, Dina
Date: 2018-07-11
Abstract: 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 [7] in which the incomplete responses are replaced by values adjusted using the inverse probability weights proposed in [14]. We show that the root estimator is consistent and asymptotically normal, essentially under some conditions on the marginal distribution and the surrogate correlation matrix as those presented in [12] in the case of complete data, and under minimal assumptions on the missingness probabilities. This method is applied to a real-life dataset taken from [10], 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.
URL: http://hdl.handle.net/10393/37838
http://dx.doi.org/10.20381/ruor-22096
CollectionThèses, 2011 - // Theses, 2011 -
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Jankovic_Dina_2018_thesis.pdfLongitudinal data are presented when following particular individuals over prolonged periods of time, often years or even decades. A dataset is longitudinal if it tracks the same type of information on the same subjects at multiple time points. For instance, a longitudinal dataset can contain information about speci fic students, their test results and other achievements in ten successive years. The primary advantage of longitudinal data over cross-sectional data is that they can measure change. However, the complexity of their analysis is a big challenge for statisticians.440.11 kBAdobe PDFOpen