Article type
Year
Abstract
Background: We previously developed an approach to address the impact of missing participant data for continuous outcomes in meta-analyses of trials that used the same measurement instrument.
Objectives: To extend our approach to meta-analyses including trials that use different instruments to measure the same construct.
Methods: We reviewed the available literature, and conducted an iterative consultative process with nine methodologists. We applied our approach to an example systematic review of respiratory rehabilitation for chronic obstructive pulmonary disease.
Results: Our approach involves first choosing a reference instrument, typically the one that is most familiar to the target audience and/or has the best measurement properties. Second, we convert scores from different instruments to the units of the reference instrument. Third, we impute the means for participants with missing data using five sources of data that reflect observed outcomes from the trials in the systematic review. These range from the best mean score among the intervention arms of included trials to the worst mean score among the control arms of included trials. Fourth, we apply four increasingly stringent imputation strategies for addressing missing participant data (Table 1). To impute standard deviation (SD), we used the median SD from the control arms of all included trials. Finally, we calculate a pooled mean difference for the complete case analysis and each of the four imputation strategies. In the example review, pooled effect estimates diminished but lost significance only with the most stringent strategy (Strategy 4, Fig. 1). When judging the risk of bias as a result of missing participant data, one should consider both the plausibility of the more stringent strategies, and the importance of the apparent intervention effects.
Conclusions: Our extended approach provides guidance for addressing missing participant data in systematic reviews of trials using different instruments to measure the same construct.
Objectives: To extend our approach to meta-analyses including trials that use different instruments to measure the same construct.
Methods: We reviewed the available literature, and conducted an iterative consultative process with nine methodologists. We applied our approach to an example systematic review of respiratory rehabilitation for chronic obstructive pulmonary disease.
Results: Our approach involves first choosing a reference instrument, typically the one that is most familiar to the target audience and/or has the best measurement properties. Second, we convert scores from different instruments to the units of the reference instrument. Third, we impute the means for participants with missing data using five sources of data that reflect observed outcomes from the trials in the systematic review. These range from the best mean score among the intervention arms of included trials to the worst mean score among the control arms of included trials. Fourth, we apply four increasingly stringent imputation strategies for addressing missing participant data (Table 1). To impute standard deviation (SD), we used the median SD from the control arms of all included trials. Finally, we calculate a pooled mean difference for the complete case analysis and each of the four imputation strategies. In the example review, pooled effect estimates diminished but lost significance only with the most stringent strategy (Strategy 4, Fig. 1). When judging the risk of bias as a result of missing participant data, one should consider both the plausibility of the more stringent strategies, and the importance of the apparent intervention effects.
Conclusions: Our extended approach provides guidance for addressing missing participant data in systematic reviews of trials using different instruments to measure the same construct.