scholarly journals Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random

2002 ◽  
Vol 21 (20) ◽  
pp. 3035-3054 ◽  
Author(s):  
John S. Preisser ◽  
Kurt K. Lohman ◽  
Paul J. Rathouz
Keyword(s):  
Binary Data ◽  
Estimating Equations ◽  
Missing At Random ◽  
Longitudinal Binary Data ◽  
Drop Outs

scholarly journals Sensitivity analysis for non-monotone missing binary data in longitudinal studies: Application to the NIDA collaborative cocaine treatment study

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3057-3073 ◽  
Author(s):  
Garrett M Fitzmaurice ◽  
Stuart R Lipsitz ◽  
Roger D Weiss
Keyword(s):  
Sensitivity Analysis ◽  
Missing Data ◽  
Drug Use ◽  
Longitudinal Studies ◽  
Binary Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Missing At Random ◽  
Selection Model ◽  
Longitudinal Binary Data

Conventional approaches for handling missingness in substance use disorder trials commonly rely upon a single deterministic “worst value” imputation that posits a perfect relationship between missingness and drug use (“missing value = presumed drug use”); this yields biased estimates of treatment effects and their standard errors. Instead, deterministic imputations should be replaced by probabilistic versions that encode researchers prior beliefs that those with missing data are more likely to be using drugs at those occasions. Motivated by this problem, we present a method for handling non-monotone missing binary data in longitudinal studies. Specifically, we consider a joint model that combines a not missing at random (NMAR) selection model with a generalized linear mixed model for longitudinal binary data. The selection model links the distribution of a missing outcome to the corresponding distribution of the outcome for those observed at that occasion via a fixed and known sensitivity parameter. The mixed model for longitudinal binary data assumes the random effects have bridge distributions; the latter yields regression parameters that have both subject-specific and marginal interpretations. This approach is completely transparent about what is being assumed about missing data and can be used as the basis for sensitivity analysis.

Two‐wave two‐phase outcome‐dependent sampling designs, with applications to longitudinal binary data

2021 ◽  
Author(s):  
Ran Tao ◽  
Nathaniel D. Mercaldo ◽  
Sebastien Haneuse ◽  
Jacob M. Maronge ◽  
Paul J. Rathouz ◽  
...  
Keyword(s):  
Binary Data ◽  
Two Phase ◽  
Longitudinal Binary Data

A Two-Part Joint Model for the Analysis of Survival and Longitudinal Binary Data with Excess Zeros

Biometrics ◽  
2008 ◽  
Vol 64 (2) ◽  
pp. 611-619 ◽  
Author(s):  
Dimitris Rizopoulos ◽  
Geert Verbeke ◽  
Emmanuel Lesaffre ◽  
Yves Vanrenterghem
Keyword(s):  
Binary Data ◽  
Joint Model ◽  
Excess Zeros ◽  
Longitudinal Binary Data
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