This monograph extends previous work on the evaluation of GEE performance in parameter estimation with correlation structure misspecification. It examines a new structure to model the correlation between binary responses observed over unequal time intervals. The proposed unequal-time correlation structure accounts for unequal intervals by fixing the correlation coefficient and interval length between the first 2 observations and by adjusting the correlations for subsequent pairs by their relative interval length. To evaluate this structure, correlated binary responses under the logistic model were generated with the unequal time correlation structure for a full range of positive correlations and strength of association under different scenarios of number of measurements, sample size and prevalence of exposure. The model was evaluated when the working correlation structure was misspecified as exchangeable, 1st order autoregressive, unstructured, or when alternating logistic regression was employed.
Longitudinal Data Observed Over Unequal Time Intervals Modeling Correlations In The Analysis Of Binary Outcomes Data
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This monograph extends previous work on the evaluation of GEE performance in parameter estimation with correlation structure misspecification. It examines a new structure to model the correlation between binary responses observed over unequal time intervals. The proposed unequal-time correlation structure accounts for unequal intervals by fixing the correlation coefficient and interval length between the first 2 observations and by adjusting the correlations for subsequent pairs by their relative interval length. To evaluate this structure, correlated binary responses under the logistic model were generated with the unequal time correlation structure for a full range of positive correlations and strength of association under different scenarios of number of measurements, sample size and prevalence of exposure. The model was evaluated when the working correlation structure was misspecified as exchangeable, 1st order autoregressive, unstructured, or when alternating logistic regression was employed.
This monograph is written for students at the graduate level in biostatistics, statistics or other disciplines that collect longitudinal data. It concentrates on the state space approach that...
Longitudinal studies often incur several problems that challenge standard statistical methods for data analysis. These problems include non-ignorable missing data in longitudinal measurements of one...
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