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A novel complete-case analysis to determine statistical significance between treatments in an intention-to-treat population of randomized clinical trials involving missing data.

Authors
  • Liu, Wei1
  • Ding, Jinhui2
  • 1 1 Rockville, MD, USA.
  • 2 2 Bioinformatics Core, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
Type
Published Article
Journal
Statistical Methods in Medical Research
Publisher
SAGE Publications
Publication Date
Apr 01, 2018
Volume
27
Issue
4
Pages
1067–1075
Identifiers
DOI: 10.1177/0962280216651307
PMID: 27230128
Source
Medline
Keywords
License
Unknown

Abstract

The application of the principle of the intention-to-treat (ITT) to the analysis of clinical trials is challenged in the presence of missing outcome data. The consequences of stopping an assigned treatment in a withdrawn subject are unknown. It is difficult to make a single assumption about missing mechanisms for all clinical trials because there are complicated reactions in the human body to drugs due to the presence of complex biological networks, leading to data missing randomly or non-randomly. Currently there is no statistical method that can tell whether a difference between two treatments in the ITT population of a randomized clinical trial with missing data is significant at a pre-specified level. Making no assumptions about the missing mechanisms, we propose a generalized complete-case (GCC) analysis based on the data of completers. An evaluation of the impact of missing data on the ITT analysis reveals that a statistically significant GCC result implies a significant treatment effect in the ITT population at a pre-specified significance level unless, relative to the comparator, the test drug is poisonous to the non-completers as documented in their medical records. Applications of the GCC analysis are illustrated using literature data, and its properties and limits are discussed.

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