Article type
Year
Abstract
Background: Missing outcome data for participants in clinical trials could lead to biased effect estimates. Analytical approaches for handling missing participant data (MPD) are important for both estimating the effect estimates (main analysis), and for assessing risk of bias associated with those estimates (sensitivity analyses).
Objectives: To review the literature systematically for simulation studies testing different analytical approaches for dealing with dichotomous MPD in individual randomized clinical trials (RCTs).
Methods: We considered dichotomous outcome data only. We searched MEDLINE, the Cochrane Library, Web of Science, and Journal Storage (JSTOR) up to January 2015. Pairs of reviewers conducted screening and data abstraction independently and in duplicate. We are abstracting information about the identified methods and their performance in the simulation studies (in terms of bias, precision, coverage, accuracy, power, and type I error).
Results: Of 15,205 citations retrieved by the electronic search strategy, 28 studies proved eligible. Twenty-one studies were designed based one or more patterns of missingness: missing completely at random (MCAR: n = 5), missing at random (MAR: n = 16); and not missing at random (NMAR; n = 8). Seven studies did not account for missingness. The data abstracted thus far suggests that multiple imputation, Maximum Likelihood method, or a combination of the two will be the most appropriate methods under the MCAR and MAR assumptions. We will present the full results at the Colloquium.
Conclusion: The results of this study will inform trialists as well as systematic reviewers conducting individual participant data meta-analysis on how to handle MPD for categorical outcomes in both the main and sensitivity analyses.
Objectives: To review the literature systematically for simulation studies testing different analytical approaches for dealing with dichotomous MPD in individual randomized clinical trials (RCTs).
Methods: We considered dichotomous outcome data only. We searched MEDLINE, the Cochrane Library, Web of Science, and Journal Storage (JSTOR) up to January 2015. Pairs of reviewers conducted screening and data abstraction independently and in duplicate. We are abstracting information about the identified methods and their performance in the simulation studies (in terms of bias, precision, coverage, accuracy, power, and type I error).
Results: Of 15,205 citations retrieved by the electronic search strategy, 28 studies proved eligible. Twenty-one studies were designed based one or more patterns of missingness: missing completely at random (MCAR: n = 5), missing at random (MAR: n = 16); and not missing at random (NMAR; n = 8). Seven studies did not account for missingness. The data abstracted thus far suggests that multiple imputation, Maximum Likelihood method, or a combination of the two will be the most appropriate methods under the MCAR and MAR assumptions. We will present the full results at the Colloquium.
Conclusion: The results of this study will inform trialists as well as systematic reviewers conducting individual participant data meta-analysis on how to handle MPD for categorical outcomes in both the main and sensitivity analyses.