Modeling Incomplete Longitudinal Data
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چکیده
منابع مشابه
Analysis of Dynamic Longitudinal Categorical Data in Incomplete Contingency Tables Using Capture-Recapture Sampling: A case Study of Semi-Concentrated Doctoral Exam
Abstract. In this paper, dynamic longitudinal categorical data and estimation of their parameters in incomplete contingency tables are evaluated. To apply the proposed method, a study has been conducted on the data of the semi-concentrated doctoral exam of the National Organization for Educational Testing (NOET). The results of studies such as the obtained confidence intervals and calculating t...
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Longitudinal data analysis in the field of substance abuse studies commonly has two related problems: large numbers of repeated measures and incomplete data matrices. These problems are critical because the structures and mechanisms of the missing data are usually more complicated than other longitudinal studies with fewer repeated measures. For an incomplete longitudinal data set, different lo...
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A longitudinal study refers to collection of a response variable and possibly some explanatory variables at multiple follow-up times. In many clinical studies with longitudinal measurements, the response variable, for each patient is collected as long as an event of interest, which considered as clinical end point, occurs. Joint modeling of continuous longitudinal measurements and survival time...
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Pattern mixture modeling is a popular approach for handling incomplete longitudinal data. Such models are not identifiable by construction. Identifying restrictions is one approach to mixture model identification (Little, 1995, Journal of the American Statistical Association 90, 1112-1121; Little and Wang, 1996, Biometrics 52, 98-111; Thijs et al., 2002, Biostatistics 3, 245-265; Kenward, Molen...
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