Sensitivity Analysis for Nonrandom Dropout: A Local Influence Approach
- 1 March 2001
- journal article
- Published by Oxford University Press (OUP) in Biometrics
- Vol. 57 (1) , 7-14
- https://doi.org/10.1111/j.0006-341x.2001.00007.x
Abstract
Summary.Diggle and Kenward (1994,Applied Statistics43, 49–93) proposed a selection model for continuous longitudinal data subject to nonrandom dropout. It has provoked a large debate about the role for such models. The original enthusiasm was followed by skepticism about the strong but untestable assumptions on which this type of model invariably rests. Since then, the view has emerged that these models should ideally be made part of a sensitivity analysis. This paper presents a formal and flexible approach to such a sensitivity assessment based on local influence (Cook, 1986,Journal of the Royal Statistical Society, Series B48, 133–169). The influence of perturbing a missing‐at‐random dropout model in the direction of nonrandom dropout is explored. The method is applied to data from a randomized experiment on the inhibition of testosterone production in rats.Keywords
This publication has 19 references indexed in Scilit:
- Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse ModelsJournal of the American Statistical Association, 1999
- Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse ModelsJournal of the American Statistical Association, 1999
- Nonrandom Missingness in Categorical Data: Strengths and LimitationsThe American Statistician, 1999
- Parametric models for incomplete continuous and categorical longitudinal dataStatistical Methods in Medical Research, 1999
- Analysis of Longitudinal Data with Non-Ignorable Non-Monotone Missing ValuesJournal of the Royal Statistical Society Series C: Applied Statistics, 1998
- Modeling the Drop-Out Mechanism in Repeated-Measures StudiesJournal of the American Statistical Association, 1995
- Modeling the Drop-Out Mechanism in Repeated-Measures StudiesJournal of the American Statistical Association, 1995
- Informative Drop-Out in Longitudinal Data AnalysisJournal of the Royal Statistical Society Series C: Applied Statistics, 1994