What is it about?
Missing outcome data are ubiquitous in systematic reviews of all research fields and pose challenges in their handling and interpretation. The present article reports challenges encountered during the extraction process from Cochrane reviews in mental health and Campbell reviews and furthermore, it indicates their implications on the empirical performance of different methods to handle missingness.
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Why is it important?
Our findings indicate that systematic reviewers tend not to distinguish between missing participants and completers in each included study. As a result, the researcher cannot achieve accurate extraction of meta-analysis data, which may negatively affect empirical performance of different methods used to handle missingness. Systematic reviewers of all research fields should explicitly report per arm the outcome of completers, number of dropouts and number randomized in each included study and acknowledge limitations in accuracy of provided meta‐analysis data where reporting quality of studies is poor.
Perspectives
By highlighting the challenges I encountered during data extraction from Cochrane and Campbell systematic reviews, I aim to make aware the researcher about the quality of the extracted meta-analysis data in the presence of missing participant outcome data - an important issue that has not been disclosed in the past.
PhD Loukia Spineli
Medizinische Hochschule Hannover
Read the Original
This page is a summary of: Missing binary data extraction challenges from Cochrane reviews in mental health and Campbell reviews with implications for empirical research, Research Synthesis Methods, October 2017, Wiley,
DOI: 10.1002/jrsm.1268.
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