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337
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Wed, 8/5/2020,
10:00 AM -
2:00 PM
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Virtual
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Approaches for Modeling Clustered and Longitudinal Data — Contributed Papers
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Biometrics Section
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Chair(s): Savannah Bergquist, Haas School of Business, University of California, Berkeley
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Inference for the Area Under the Curve (AUC) in a Three-Level Clustered Data Setting
Camden Bay, Brigham and Women's Hospital; Bernard Rosner, Brigham and Women's Hospital; Robert Glynn, Brigham and Women's Hospital
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The Effect of Covariate-Dependent Correlations on the Efficiency of GEE Models for Clustered Binary Data
Lee Kennedy-Shaffer, Harvard University; Michael David Hughes, Harvard University
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Joint Models for Multiple Ratings of a Discrete Diagnostic Test and Associated Auxiliary Variables
Xianling Wang, University of Pittsburgh; Gong Tang, University of Pittsburgh
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Exploration of Misspecification in Latent Class Trajectory Analysis and Growth Mixture Modeling: Correlation Structure Matters
Megan Neely, Biostatistics & Bioinformatics Dept., Duke University; Jane Pendergast, Duke University Department of Biostatistics and Bioinformatics; Natasha Dmitreava, Duke University Medical Center ; Carl Pieper, Duke University Department of Biostatistics and Bioinformatics
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Statistical Methods for Dealing with Missing Data in Longitudinal Studies
Panpan Zhang, University of Pennsylvania; Sharon Xiangwen Xie, University of Pennsylvania
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An Intensive Longitudinal Functional Data with Multiple Time Scales
Mostafa Zahed, -
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Multivariate Generalized Linear Model for Intensive Longitudinal Data with Incorporation of Outcome Variability as a Predictor
Maryam Skafyan
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