Clinical Trials

 

Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Sign In to gain access to subscriptions and/or personal tools.
This Article
Immediate free access via SAGE Open
Right arrow Free Full Text (Free PDF) Free
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Google Scholar
Right arrow Articles by Higgins, J. P.
Right arrow Articles by Wood, A. M
PubMed
Right arrow PubMed Citation
Right arrow Articles by Higgins, J. P.
Right arrow Articles by Wood, A. M
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?
Clinical Trials, Vol. 5, No. 3, 225-239 (2008)
DOI: 10.1177/1740774508091600
SAGE Open article

Imputation methods for missing outcome data in meta-analysis of clinical trials

Julian PT Higgins

MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, UK, julian.higgins{at}mrc-bsu.cam.ac.uk

Ian R White

MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, UK

Angela M Wood

MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, UK

Background: Missing outcome data from randomized trials lead to greater uncertainty and possible bias in estimating the effect of an experimental treatment. An intention-to-treat analysis should take account of all randomized participants even if they have missing observations.

Purpose: To review and develop imputation methods for missing outcome data in meta-analysis of clinical trials with binary outcomes.

Methods: We review some common strategies, such as simple imputation of positive or negative outcomes, and develop a general approach involving `informative missingness odds ratios' (IMORs). We describe several choices for weighting studies in the meta-analysis, and illustrate methods using a meta-analysis of trials of haloperidol for schizophrenia.

Results: IMORs describe the relationship between the unknown risk among missing participants and the known risk among observed participants. They are allowed to differ between treatment groups and across trials. Application of IMORs and other methods to the haloperidol trials reveals the overall conclusion to be robust to different assumptions about the missing data.

Limitations: The methods are based on summary data from each trial (number of observed positive outcomes, number of observed negative outcomes and number of missing outcomes) for each intervention group. This limits the options for analysis, and greater flexibility would be available with individual participant data.

Conclusions: We propose that available reasons for missingness be used to determine appropriate IMORs. We also recommend a strategy for undertaking sensitivity analyses, in which the IMORs are varied over plausible ranges. Clinical Trials 2008; 5: 225—239. http://ctj.sagepub.com


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?