A graphical sensitivity analysis for clinical trials with non‐ignorable missing binary outcome
- 2 December 2002
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 21 (24) , 3823-3834
- https://doi.org/10.1002/sim.1276
Abstract
Many clinical trials are analysed using an intention-to-treat (ITT) approach. A full application of the ITT approach is only possible when complete outcome data are available for all randomized subjects. In a recent survey of clinical trial reports including an ITT analysis, complete case analysis (excluding all patients with a missing response) was common. This does not comply with the basic principles of ITT since not all randomized subjects are included in the analysis. Analyses of data with missing values are based on untestable assumptions, and so sensitivity analysis presenting a range of estimates under alternative assumptions about the missing-data mechanism is recommended. For binary outcome, extreme case analysis has been suggested as a simple form of sensitivity analysis, but this is rarely conclusive. A graphical sensitivity analysis is proposed which displays the results of all possible allocations of cases with missing binary outcome. Extension to allow binomial variation in outcome is also considered. The display is based on easily interpretable parameters and allows informal examination of the effects of varying prior beliefs. Copyright © 2002 John Wiley & Sons, Ltd.Keywords
This publication has 28 references indexed in Scilit:
- Explanatory and pragmatic attitudes in therapeutical trialsPublished by Elsevier ,2004
- A method for exploring the effects of attrition in randomized experiments with dichotomous outcomes.Psychological Methods, 1998
- Simple Fitting Algorithms for Incomplete Categorical DataJournal of the Royal Statistical Society Series B: Statistical Methodology, 1997
- Methods for the analysis of binary outcome results in the presence of missing data.Journal of Consulting and Clinical Psychology, 1994
- Composite linear models for incomplete multinomial dataStatistics in Medicine, 1994
- Pattern-Mixture Models for Multivariate Incomplete DataJournal of the American Statistical Association, 1993
- Regression Analysis for Categorical Variables with Outcome Subject to Nonignorable NonresponseJournal of the American Statistical Association, 1988
- Inference from Nonrandomly Missing Categorical Data: An Example from a Genetic Study on Turner's SyndromeJournal of the American Statistical Association, 1984
- Maximum Likelihood Estimation and Model Selection in Contingency Tables with Missing DataJournal of the American Statistical Association, 1982