Covariate Decomposition Methods for Longitudinal Missing-at-Random Data and Predictors Associated with Subject-Specific Effects

Fixed Effects Models for Longitudinal Binary Data with Drop-outs Missing at Random

We consider the problem of attrition under a logistic regression model for longitudinal binary data in which each subject has his own intercept parameter, and where parameters are eliminated via conditional logistic regression. This is a fixed-effects, subject-specific model which exploits the longitudinal data by allowing subjects to act as their own controls. By modeling and conditioning on t...

TECHNICAL REPORT Fixed effects models for longitudinal binary data with drop-outs missing at random

We consider the problem of attrition under a logistic regression model for longitudinal binary data in which each subject has his own intercept parameter, and those parameters are eliminated via conditional logistic regression. This is a fixed-effects, subject-specific model which exploits the longitudinal data by allowing subjects to act as their own controls. By modeling and conditioning on t...

Longitudinal Discriminant Analysis with Random Effects for Predicting Preeclampsia using Hematocrit Data

Background and Objectives: Preeclampsia is the third leading cause of death in pregnant women. This study was conducted to evaluate the ability of longitudinal hematocrit data to predict preeclampsia and to compare the accuracy in longitudinal and cross-sectional data. Materials and Methods: In a prospective cohort study from October 2010 to July 2011, 650 pregnant women referred to the prenata...

A Comparison Of Methods For Longitudinal Analysis With Missing Data

Comparison of different methods for longitudinal data with missing observations

COMPARISON OF DIFFERENT METHODS FOR LONGITUDINAL DATA WITH MISSING OBSERVATIONS Lin Sun July 27, 2010 Longitudinal studies occupy an important role in scientific researches and clinical trials. When taking the analysis of longitudinal data, investigators are often confronted with missing data which will produce potential biases, even in well-controlled condition. In the literature, missing data...