Global Sensitivity Analysis for Repeated Measures Studies With Informative Dropout: A Fully Parametric Approach

Daniel Scharfstein; Aidan McDermott; William Olson; Frank Wiegand

doi:10.1080/19466315.2014.966920

Global Sensitivity Analysis for Repeated Measures Studies With Informative Dropout: A Fully Parametric Approach

Daniel Scharfstein, Aidan McDermott, William Olson, Frank Wiegand

Bloomberg School of Public Health

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

We present a global sensitivity analysis methodology for drawing inference about the mean at the final scheduled visit in a repeated measures study with informative dropout. We review and critique the sensitivity frameworks developed by Rotnitzky et al. and Daniels and Hogan. We identify strengths and weaknesses of these approaches and propose an alternative. We illustrate our approach via a comprehensive analysis of the RIS-INT-3 trial.

Original language	English (US)
Pages (from-to)	338-348
Number of pages	11
Journal	Statistics in Biopharmaceutical Research
Volume	6
Issue number	4
DOIs	https://doi.org/10.1080/19466315.2014.966920
State	Published - Oct 2 2014

Keywords

Curse of dimensionality
Explainable dropout
Exponential tilting
G-computation
Identification
Missing at random
Pattern-mixture model
Selection model

ASJC Scopus subject areas

Statistics and Probability
Pharmaceutical Science

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10.1080/19466315.2014.966920

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title = "Global Sensitivity Analysis for Repeated Measures Studies With Informative Dropout: A Fully Parametric Approach",

abstract = "We present a global sensitivity analysis methodology for drawing inference about the mean at the final scheduled visit in a repeated measures study with informative dropout. We review and critique the sensitivity frameworks developed by Rotnitzky et al. and Daniels and Hogan. We identify strengths and weaknesses of these approaches and propose an alternative. We illustrate our approach via a comprehensive analysis of the RIS-INT-3 trial.",

keywords = "Curse of dimensionality, Explainable dropout, Exponential tilting, G-computation, Identification, Missing at random, Pattern-mixture model, Selection model",

author = "Daniel Scharfstein and Aidan McDermott and William Olson and Frank Wiegand",

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AU - Olson, William

AU - Wiegand, Frank

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AB - We present a global sensitivity analysis methodology for drawing inference about the mean at the final scheduled visit in a repeated measures study with informative dropout. We review and critique the sensitivity frameworks developed by Rotnitzky et al. and Daniels and Hogan. We identify strengths and weaknesses of these approaches and propose an alternative. We illustrate our approach via a comprehensive analysis of the RIS-INT-3 trial.

KW - Curse of dimensionality

KW - Explainable dropout

KW - Exponential tilting

KW - G-computation

KW - Identification

KW - Missing at random

KW - Pattern-mixture model

KW - Selection model

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Global Sensitivity Analysis for Repeated Measures Studies With Informative Dropout: A Fully Parametric Approach

Abstract

Keywords

ASJC Scopus subject areas

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