TY - JOUR
T1 - Assessment of Heterogeneity in Heart Failure-Related Meta-Analyses
AU - Khan, Muhammad Shahzeb
AU - Li, Lin
AU - Yasmin, Farah
AU - Khan, Safi U.
AU - Bajaj, Navkaranbir S.
AU - Pandey, Ambarish
AU - Murad, M. Hassan
AU - Fonarow, Gregg C.
AU - Butler, Javed
AU - Vaduganathan, Muthiah
N1 - Publisher Copyright:
© 2020 Lippincott Williams and Wilkins. All rights reserved.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - 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 (I2=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.
AB - 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 (I2=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.
KW - evidence-based medicine
KW - heart failure
KW - patients
KW - population
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U2 - 10.1161/CIRCHEARTFAILURE.120.007070
DO - 10.1161/CIRCHEARTFAILURE.120.007070
M3 - Article
C2 - 33131285
AN - SCOPUS:85096354352
SN - 1941-3289
VL - 13
SP - E007070
JO - Circulation: Heart Failure
JF - Circulation: Heart Failure
IS - 11
ER -