Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis by DANIELS, M. J. and HOGAN, J. W
By Andrea Rotnitzky, Daniel F. Heitjan, M. Ivette Gomes. 2009
357 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/4da107f0fea4e956c89be005066e876eae564a56
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Iv'an D'iaz
- S. van Buuren
- Marco Carone
- Aidan McDermott
- Nicola Lunardon
- D. Scharfstein
- I. Turkoz
- Nathan Kallus
Citations in the corpus
- Global sensitivity analysis for repeated measures studies with informative drop‐out: A semi‐parametric approach (2018, methodology): “The techniques of Daniels and Hogan (2008) and Linero and Daniels (2015) © 2017, The International Biometric Society 207 provide Bayesian solutions to the same problem and also ensure the compatibility of the untestable and testable assumptions.”
- Flexible Imputation of Missing Data (2012, background): “There is an extensive literature which often concentrates on the longitudinal case (Verbeke and Molenberghs, 2000; Molenberghs and Verbeke, 2005; Daniels and Hogan, 2008).”