Testing Categorical Moderators in Mixed-Effects Meta-analysis in the Presence of Heteroscedasticity

Rubio‐Aparicio, María; López-López, José A; Viechtbauer, Wolfgang; Martínez, Faustino Cavas; Botella, Juan; Meca, Julio Sánchez

doi:10.1080/00220973.2018.1561404

Cited by 52 publications

(49 citation statements)

References 30 publications

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“…In subgroup analyses, we examined differences by study design (cross-sectional versus case-control), geographic location (European versus other studies), assays (Roche versus other for NT-proBNP and FEIA versus RIA for BNP) and decade of publication (2000–2009 versus 2010–2019). Subgroup effect statistic was calculated by means of a Wald test to determine differences between respective subgroups, if both subgroups included a minimum of two studies [ 27 ]. In sensitivity analyses, we further determined heterogeneity by geographic location by including European studies only, or by study population by excluding studies with hospitalized patients.…”

Section: Methodsmentioning

confidence: 99%

Natriuretic peptides for the detection of diastolic dysfunction and heart failure with preserved ejection fraction—a systematic review and meta-analysis

Remmelzwaal

Ballegooijen

Schoonmade

et al. 2020

BMC Med

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Background An overview of the diagnostic performance of natriuretic peptides (NPs) for the detection of diastolic dysfunction (DD) and heart failure with preserved ejection fraction (HFpEF), in a non-acute setting, is currently lacking. Methods We performed a systematic literature search in PubMed and Embase.com (May 13, 2019). Studies were included when they (1) reported diagnostic performance measures, (2) are for the detection of DD or HFpEF in a non-acute setting, (3) are compared with a control group without DD or HFpEF or with patients with heart failure with reduced ejection fraction, (4) are in a cross-sectional design. Two investigators independently assessed risk of bias of the included studies according to the QUADAS-2 checklist. Results were meta-analysed when three or more studies reported a similar diagnostic measure. Results From 11,728 titles/abstracts, we included 51 studies. The meta-analysis indicated a reasonable diagnostic performance for both NPs for the detection of DD and HFpEF based on AUC values of approximately 0.80 (0.73–0.87; I2 = 86%). For both NPs, sensitivity was lower than specificity for the detection of DD and HFpEF: approximately 65% (51–85%; I2 = 95%) versus 80% (70–90%; I2 = 97%), respectively. Both NPs have adequate ability to rule out DD: negative predictive value of approximately 85% (78–93%; I2 = 95%). The ability of both NPs to prove DD is lower: positive predictive value of approximately 60% (30–90%; I2 = 99%). Conclusion The diagnostic performance of NPs for the detection of DD and HFpEF is reasonable. However, they may be used to rule out DD or HFpEF, and not for the diagnosis of DD or HFpEF.

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Section: Methodsmentioning

confidence: 99%

Natriuretic peptides for the detection of diastolic dysfunction and heart failure with preserved ejection fraction—a systematic review and meta-analysis

Remmelzwaal

Ballegooijen

Schoonmade

et al. 2020

BMC Med

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“…For this analyses, a meta regression model in the R package called metaphor (26) was applied. We used a fixed effects model because the (residual) heterogeneity within each subset has already been accounted for by fitting random effects models (27). The fixed effects model did not substantially change the results (P > 0.05) (25), and therefore data from the meta-analyses including data from all 5 cohorts are presented, unless stated otherwise.…”

Section: Statistical Analysesmentioning

confidence: 99%

Plasma Metabolomics Identifies Markers of Impaired Renal Function: A Meta-analysis of 3089 Persons with Type 2 Diabetes

Tofte

Vogelzangs

Mook‐Kanamori

et al. 2020

The Journal of Clinical Endocrinology &Amp; Metabolism

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Context There is a need for novel biomarkers and better understanding of the pathophysiology of diabetic kidney disease. Objective To investigate associations between plasma metabolites and kidney function in people with type 2 diabetes (T2D). Design 3089 samples from individuals with T2D, collected between 1999 and 2015, from 5 independent Dutch cohort studies were included. Up to 7 years follow-up was available in 1100 individuals from 2 of the cohorts. Main outcome measures Plasma metabolites (n = 149) were measured by nuclear magnetic resonance spectroscopy. Associations between metabolites and estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), and eGFR slopes were investigated in each study followed by random effect meta-analysis. Adjustments included traditional cardiovascular risk factors and correction for multiple testing. Results In total, 125 metabolites were significantly associated (PFDR = 1.5×10–32 − 0.046; β = −11.98-2.17) with eGFR. Inverse associations with eGFR were demonstrated for branched-chain and aromatic amino acids (AAAs), glycoprotein acetyls, triglycerides (TGs), lipids in very low-density lipoproteins (VLDL) subclasses, and fatty acids (PFDR < 0.03). We observed positive associations with cholesterol and phospholipids in high-density lipoproteins (HDL) and apolipoprotein A1 (PFDR < 0.05). Albeit some metabolites were associated with UACR levels (P < 0.05), significance was lost after correction for multiple testing. Tyrosine and HDL-related metabolites were positively associated with eGFR slopes before adjustment for multiple testing (PTyr = 0.003; PHDLrelated < 0.05), but not after. Conclusions This study identified metabolites associated with impaired kidney function in T2D, implying involvement of lipid and amino acid metabolism in the pathogenesis. Whether these processes precede or are consequences of renal impairment needs further investigation.

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“…This is because heterogeneity has a direct bearing upon var(y i ), as seen in Equation (3). To our knowledge, however, there has been little work considering the implications of variation in τ 2 i (but see Rubio-Aparicio et al, 2020). In what follows, we open Pandora's jar on this important topic.…”

Section: Inference On the Overall Effectmentioning

confidence: 86%

Putting Variation into Variance: Modeling Between-Study Heterogeneity in Meta-Analysis

Dr¹,

Rodriguez²,

Bürkner³

2021

Preprint

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We shed much needed light upon a critical assumption that is oft-overlooked---or not considered at all---in random-effects meta-analysis.Namely, that between-study variance is constant across \emph{all} studies which implies they are from the \emph{same} population. Yet it is not hard to imagine a situation where there are several and not merely one population of studies, perhaps differing in their between-study variance (i.e., heteroskedasticity). The objective is to then make inference, given that there are variations in heterogeneity. There is an immediate problem, however, in that modeling heterogeneous variance components is not straightforward to do in a general way. To this end, we propose novel methodology, termed Bayesian location-scale meta-analysis, that can accommodate moderators for both the overall effect (location) and the between-study variance (scale). After introducing the model, we then extend heterogeneity statistics, prediction intervals, and hierarchical shrinkage, all of which customarily assume constant heterogeneity, to include variations therein. With these new tools in hand, we go to work demonstrating that quite literally \emph{everything} changes when between-study variance is not constant across studies. The changes were not small and easily passed the interocular trauma test---the importance hits right between the eyes. Such examples include (but are not limited to) inference on the overall effect, a compromised predictive distribution, and improper shrinkage of the study-specific effects. Further, we provide an illustrative example where heterogeneity was not considered a mere nuisance to show that modeling variance for its own sake can provide unique inferences, in this case into discrimination across nine countries. The discussion includes several ideas for future research. We have implemented the proposed methodology in the {\tt R} package \textbf{blsmeta}.

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Testing Categorical Moderators in Mixed-Effects Meta-analysis in the Presence of Heteroscedasticity

Cited by 52 publications

References 30 publications

Natriuretic peptides for the detection of diastolic dysfunction and heart failure with preserved ejection fraction—a systematic review and meta-analysis

Natriuretic peptides for the detection of diastolic dysfunction and heart failure with preserved ejection fraction—a systematic review and meta-analysis

Plasma Metabolomics Identifies Markers of Impaired Renal Function: A Meta-analysis of 3089 Persons with Type 2 Diabetes

Putting Variation into Variance: Modeling Between-Study Heterogeneity in Meta-Analysis

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