• Shanil Ebrahim, Elie A Akl, Reem A Mustafa, Xin Sun, Stephen D Walter, Diane Heels-Ansdell, Pablo Alonso-Coello, Bradley C Johnston, and Gordon H Guyatt.
• Department of Clinical Epidemiology & Biostatistics, McMaster University, 1200 Main St. West, Hamilton, Canada, L8S 4K1.
• J Clin Epidemiol. 2013 Sep 1;66(9):1014-1021.e1.

BackgroundNo methods directly address the impact of missing participant data for continuous outcomes in systematic reviews on risk of bias.MethodsWe conducted a consultative, iterative process to develop a framework for handling missing participant data for continuous outcomes. We considered sources reflecting real observed outcomes in participants followed-up in individual trials included in the systematic review, and developed a range of plausible strategies. We applied our approach to two systematic reviews.ResultsWe used five sources of data for imputing the means for participants with missing data. To impute standard deviation (SD), we used the median SD from the control arms of all included trials. Using these sources, we developed four progressively more stringent imputation strategies. In the first example review, effect estimates diminished and lost significance as strategies became more stringent, suggesting rating down confidence in estimates of effect for risk of bias. In the second, effect estimates maintained statistical significance using even the most stringent strategy, suggesting missing data does not undermine confidence in results.ConclusionsOur approach provides a useful, reasonable, and relatively simple, quantitative guidance for judging the impact of risk of bias as a result of missing participant data in systematic reviews of continuous outcomes.Copyright © 2013 Elsevier Inc. All rights reserved.

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