Article type
Year
Abstract
Background: Missing participant data (MPD) are a common source of bias in randomized controlled trials (RCTs). Sensitivity analyses can address whether results are robust under different assumptions regarding MPD. It is likely that some methods of conducting such sensitivity analysis are superior to others.
Objective: To identify analytical approaches for dealing with MPD in continuous variables in RCTs for proposed and tested simulations of use.
Method: We searched MEDLINE, the Cochrane Library, Web of Science, and Journal Storage for methodological articles up to January 2015. Reviewers conducted screening and data abstraction independently in duplicate. Data abstraction is ongoing. We will describe methods identified and their performance regarding bias, precision, coverage, accuracy, power, and type I error, and general ranking considering all the above aspects both in tabular and narrative formats.
Results: From 15205 citations, we have identified 94 eligible papers reporting more than 60 methods. Simulated data were derived from real trial data from the following clinical areas: respirology, gastroenterology, psychology, dermatology, infectious diseases, cardiology, and rheumatology. The 25 studies we have abstracted to date, explored performance of methods under different missing mechanisms, proportions of missing data, effect size, sample size, and strength of associations between certain variables with missing data. Twenty of these specified general performance of the methods for ignorable and non-ignorable missing data, mixed-effect models (MEM; 4/20), multiple imputation (MI; 5/20), maximum likelihood (ML) method (4/20), or combination of MI and MEM (2/20) seem to be the most frequently mentioned best methods. Expectation maximization, weighted least squares estimator, and empirical bayes are also identified as possible optimal methods.
Conclusions: The data abstracted so far suggest that using MEM or MI alone or as a combination, or ML may minimize the impact of ignorable and/or non-ignorable MPD in RCTs. The completion of data abstraction and synthesis will allow us to confirm or refute these preliminary conclusions.
Objective: To identify analytical approaches for dealing with MPD in continuous variables in RCTs for proposed and tested simulations of use.
Method: We searched MEDLINE, the Cochrane Library, Web of Science, and Journal Storage for methodological articles up to January 2015. Reviewers conducted screening and data abstraction independently in duplicate. Data abstraction is ongoing. We will describe methods identified and their performance regarding bias, precision, coverage, accuracy, power, and type I error, and general ranking considering all the above aspects both in tabular and narrative formats.
Results: From 15205 citations, we have identified 94 eligible papers reporting more than 60 methods. Simulated data were derived from real trial data from the following clinical areas: respirology, gastroenterology, psychology, dermatology, infectious diseases, cardiology, and rheumatology. The 25 studies we have abstracted to date, explored performance of methods under different missing mechanisms, proportions of missing data, effect size, sample size, and strength of associations between certain variables with missing data. Twenty of these specified general performance of the methods for ignorable and non-ignorable missing data, mixed-effect models (MEM; 4/20), multiple imputation (MI; 5/20), maximum likelihood (ML) method (4/20), or combination of MI and MEM (2/20) seem to be the most frequently mentioned best methods. Expectation maximization, weighted least squares estimator, and empirical bayes are also identified as possible optimal methods.
Conclusions: The data abstracted so far suggest that using MEM or MI alone or as a combination, or ML may minimize the impact of ignorable and/or non-ignorable MPD in RCTs. The completion of data abstraction and synthesis will allow us to confirm or refute these preliminary conclusions.