Article type
Year
Abstract
Background: When there are several interventions for a single condition, several Cochrane intervention reviews (CIR) may cover the plethora of pairwise comparisons. Nevertheless, consumers and policy-makers face challenges in judging which intervention is best. Network meta-analyses (NMA) allow comparison of multiple interventions. A NMA using data from CIRs may reduce research waste and avoid duplication. However, it can also present some unique methodological challenges.
Objectives: To describe the challenges faced in compiling data from CIRs for a NMA.
Methods: We used a NMA of randomised controlled trials (RCT) of nine distinct surgical interventions for women with stress urinary incontinence (SUI). Primary outcomes were cure and improvement of SUI. Data came from eight CIRs from a single Cochrane Review Group (CRG). The CIRs provided relevant studies, outcome data from individual RCTs, study characteristics and findings of risk of bias (RoB) assessment. Where the same trial was used in two or more CIRs, we compared data extraction. When data were inconsistent or insufficient, we assessed primary trial reports. We updated literature searches to a common date and reviewed newly identified studies.
Results: We included 147 RCTs from eight CIRs and 28 new studies. A challenge for the NMA was caused by poor reporting in the original trial reports (e.g. number of participants in each group). This led to CIR authors making assumptions in order to use data, but limited reporting in the CIRs meant that extracted data from the CIR could not be replicated or verified. Additionally, methods also varied between CIRs; for example, outcomes were grouped into categories using different criteria. Study characteristics were also inconsistently reported across reviews as were descriptions of adverse events. The different publication dates of the CIRs, meant different versions of the Cochrane RoB tool were used.
Conclusions: Considerable additional work was required to make data from the CIRs useable. The NMA also served as a methods audit and highlighted the need to ensure consistency across reviews addressing substantially the same participants, intervention, comparator and outcomes (PICO). CIRs and CRGs need to ensure assumptions for handling missing or ambiguous data are consistent and clearly described, and CRGs need to ensure consistency of methods.
Objectives: To describe the challenges faced in compiling data from CIRs for a NMA.
Methods: We used a NMA of randomised controlled trials (RCT) of nine distinct surgical interventions for women with stress urinary incontinence (SUI). Primary outcomes were cure and improvement of SUI. Data came from eight CIRs from a single Cochrane Review Group (CRG). The CIRs provided relevant studies, outcome data from individual RCTs, study characteristics and findings of risk of bias (RoB) assessment. Where the same trial was used in two or more CIRs, we compared data extraction. When data were inconsistent or insufficient, we assessed primary trial reports. We updated literature searches to a common date and reviewed newly identified studies.
Results: We included 147 RCTs from eight CIRs and 28 new studies. A challenge for the NMA was caused by poor reporting in the original trial reports (e.g. number of participants in each group). This led to CIR authors making assumptions in order to use data, but limited reporting in the CIRs meant that extracted data from the CIR could not be replicated or verified. Additionally, methods also varied between CIRs; for example, outcomes were grouped into categories using different criteria. Study characteristics were also inconsistently reported across reviews as were descriptions of adverse events. The different publication dates of the CIRs, meant different versions of the Cochrane RoB tool were used.
Conclusions: Considerable additional work was required to make data from the CIRs useable. The NMA also served as a methods audit and highlighted the need to ensure consistency across reviews addressing substantially the same participants, intervention, comparator and outcomes (PICO). CIRs and CRGs need to ensure assumptions for handling missing or ambiguous data are consistent and clearly described, and CRGs need to ensure consistency of methods.