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Assessment of Heterogeneity in Heart Failure-Related Meta-Analyses
Muhammad Shahzeb Khan1, Lin Li1, Farah Yasmin2
1Department of Medicine, Cook County Health and Hospital System, Chicago, IL (M.S.K., L.L.).
Insights
Heterogeneity in heart failure meta-analyses was inconsistently reported, with nearly 30% of outcomes lacking this crucial data. Standardizing the assessment and reporting of heterogeneity is vital for reliable heart failure research.
Area of Science:
- Cardiology
- Biostatistics
- Medical Research Methodology
Background:
- Assessing statistical heterogeneity is crucial for the reliability of pooled results in meta-analyses.
- Inconsistent reporting of heterogeneity can impact the interpretation and validity of meta-analysis findings.
Purpose of the Study:
- To evaluate the reporting and assessment of statistical heterogeneity in heart failure (HF) meta-analyses.
- To identify common methods used to quantify and explore heterogeneity in HF research.
Main Methods:
- A systematic review of HF meta-analyses published between January 2009 and July 2019 in high-impact journals.
- Tabulation of the proportion of meta-analyses reporting statistical heterogeneity and the specific metrics and methods used.
Main Results:
- Heterogeneity was reported for 68.9% of outcomes across 126 HF meta-analyses.
- A significant proportion of reported heterogeneity was high (40%).
- Sensitivity analysis was the most common method (n=68) for exploring heterogeneity, followed by subgroup analyses (n=59) and meta-regression (n=40).
Conclusions:
- Nearly 30% of outcomes in HF meta-analyses lacked reporting of statistical heterogeneity.
- The handling and interpretation of heterogeneity in HF meta-analyses are variable.
- Standardization of heterogeneity assessment and reporting is recommended for future HF meta-analyses.
Background:
Assessment of heterogeneity in meta-analyses is critical to ensure the consistency of pooled results. Therefore, we sought to assess the evaluation and reporting of heterogeneity in heart failure (HF) meta-analyses.
Methods:
Study level meta-analyses pertaining to HF were selected from January 2009 to July 2019, published in 11 high impact factor journals. We tabulated the overall proportion of the meta-analyses reporting statistical heterogeneity and specific metrics and methods employed to quantify and explore heterogeneity.
Results:
Of 126 HF meta-analyses (612 outcomes), heterogeneity was reported for 422 outcomes (68.9 %) in 108 meta-analyses. Out of the 422 outcomes reporting statistical heterogeneity, 137 outcomes (32.5%) had no observable heterogeneity: (I2=0%), 40 outcomes (9.5%) had low heterogeneity (I2<25%), 76 outcomes (18%) had moderate heterogeneity (I=25%-50%), and 169 outcomes (40%) had high heterogeneity (I2>50%). Reporting of statistical heterogeneity was not significantly associated with year of publication, funding source, disclosure information, or the type of studies pooled. Sensitivity analysis (n=68) was the most common statistical technique employed to evaluate the source of heterogeneity followed by subgroup analyses (n=59) and meta-regression (n=40).
Conclusions:
Despite being an essential component of meta-analyses, heterogeneity was not reported for nearly 30% of outcomes and variably handled in contemporary HF meta-analyses. As meta-analyses increase across HF science, interpreting and handling of heterogeneity should be standardized.
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