Neither fixed nor random: weighted least squares meta‐regression

Stanley, T. D.; Doucouliagos, Hristos

doi:10.1002/jrsm.1211

Cited by 189 publications

(171 citation statements)

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“…Recently, a new approach has been proposed for meta-analysis, which differs from the standard fixed or random effect models [47, 48]. The standard fixed effect meta-analysis for pairwise comparisons is just weight least squares regression and can be written as:where Δ j may be the log odds ratio or difference in means between two treatments, v j is the standard error of Δ j and , where is the variance of Δ j .…”

Section: Methodsmentioning

confidence: 99%

Using structural equation modeling for network meta-analysis

2017

BMC Med Res Methodol

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BackgroundNetwork meta-analysis overcomes the limitations of traditional pair-wise meta-analysis by incorporating all available evidence into a general statistical framework for simultaneous comparisons of several treatments. Currently, network meta-analyses are undertaken either within the Bayesian hierarchical linear models or frequentist generalized linear mixed models. Structural equation modeling (SEM) is a statistical method originally developed for modeling causal relations among observed and latent variables. As random effect is explicitly modeled as a latent variable in SEM, it is very flexible for analysts to specify complex random effect structure and to make linear and nonlinear constraints on parameters. The aim of this article is to show how to undertake a network meta-analysis within the statistical framework of SEM.MethodsWe used an example dataset to demonstrate the standard fixed and random effect network meta-analysis models can be easily implemented in SEM. It contains results of 26 studies that directly compared three treatment groups A, B and C for prevention of first bleeding in patients with liver cirrhosis. We also showed that a new approach to network meta-analysis based on the technique of unrestricted weighted least squares (UWLS) method can also be undertaken using SEM.ResultsFor both the fixed and random effect network meta-analysis, SEM yielded similar coefficients and confidence intervals to those reported in the previous literature. The point estimates of two UWLS models were identical to those in the fixed effect model but the confidence intervals were greater. This is consistent with results from the traditional pairwise meta-analyses. Comparing to UWLS model with common variance adjusted factor, UWLS model with unique variance adjusted factor has greater confidence intervals when the heterogeneity was larger in the pairwise comparison. The UWLS model with unique variance adjusted factor reflects the difference in heterogeneity within each comparison.ConclusionSEM provides a very flexible framework for univariate and multivariate meta-analysis, and its potential as a powerful tool for advanced meta-analysis is still to be explored.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-017-0390-9) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Using structural equation modeling for network meta-analysis

2017

BMC Med Res Methodol

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“…In the presence of publication selection bias, WLS meta-regression has been shown to outperform random-effects meta-regression, especially when large heterogeneity was present (Stanley and Doucouliagos, 2013). Specifically, simulation results indicated that WLS meta-regression yielded smaller bias and mean squared error than random-effects meta-regression, regardless of publication bias (Stanley and Doucouliagos, 2013). Meta-analysts in medical research have long recognized the important role of weighted regression in adjusting for publication bias and heterogeneity (Moreno et al, 2009; Thompson and Sharp, 1999).…”

Section: Methodsmentioning

confidence: 99%

Smoking cessation interventions for adults aged 50 or older: A systematic review and meta-analysis

Chen

2015

Drug and Alcohol Dependence

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Background The older population size has increased substantially, and a considerable proportion of older adults are cigarette smokers. Quitting smoking is associated with reduced health risk. This review is among the first to quantitatively assess the relative efficacy of types of cessation interventions for smokers aged ≥ 50 years. Methods We conducted searches of the Cochrane Library, Embase, MEDLINE, and PsycINFO to identify smoking cessation studies on adults aged ≥ 50 years. Twenty-nine randomized clinical trials met the inclusion criteria. Three main types of interventions were identified. We analyzed relative cessation rates or Risk Ratios (RRs) between the type of intervention groups and the control group by fixed- and random-effects meta-analyses at the study level. We conducted a weighted least squares meta-regression of cessation rates on trial and sample characteristics to determine sources of outcome heterogeneity. Results Fixed-effects analysis showed significant treatment effects for pharmacological (RR=3.18, 95% CI: 1.89–5.36), non-pharmacological (RR=1.80, 95% CI: 1.67–1.94), and multimodal interventions (RR=1.61, 95% CI: 1.41–1.84) compared with control group. Estimations based on meta-regression suggested that pharmacological intervention (mean point prevalence abstinence rate (PPA) = 26.10%, CI: 15.20–37.00) resembled non-pharmacological (27.97%, CI: 24.00–31.94), and multimodal interventions (36.64%, CI: 31.66–41.62); and non-pharmacological and multimodal interventions had higher PPAs than the control group (18.80%, CI: 14.48–23.12), after adjusting for a number of trial and sample characteristics. Conclusions A small number of smoking cessation studies examined smokers aged ≥ 50 years. Additional research is recommended to determine smoking cessation efficacy for diverse older population groups (e.g., ethnic minorities).

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“…In particular, weighted ordinary least square (OLS) is superior to conventional random effects estimator when meta-analysis refers to a small sample [64], such as in our paper.…”

Section: The Meta-analysismentioning

confidence: 96%

Assessing the Determinants of Renewable Electricity Acceptance Integrating Meta-Analysis Regression and a Local Comprehensive Survey

Bigerna

Polinori

2015

Sustainability

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Abstract:In dealing with renewable electricity (RE), individuals are involved both as end-consumers on the demand side and as stakeholders (citizens) in the local production process on the supply side. Empirical evidence shows that in many countries, consumers are willing to pay a significant amount to facilitate adoption of RE. By contrast, environmental externalities are often the cause of strong opposition to RE adoption if local communities are involved as stakeholders in wind, solar or biomass investment projects. Looking at the literature on willingness to pay and on willingness to accept, we have investigated RE acceptance mechanisms. First, we have used the meta-analysis to assess the major determinants of RE acceptance on both demand and supply sides. Meta-analysis has provided some insights useful for managing field research on an onshore wind farm enlargement project located in the Umbria region. Meta-analysis and survey results confirm that the local community plays a central role in local RE acceptance. Furthermore, people who have previous experience with windmills require less compensation, or are willing to pay more, for RE development.Results suggest that these attributes should be included in future research to improve understanding of determinants of RE acceptance.

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Neither fixed nor random: weighted least squares meta‐regression

Cited by 189 publications

References 50 publications

Using structural equation modeling for network meta-analysis

Using structural equation modeling for network meta-analysis

Smoking cessation interventions for adults aged 50 or older: A systematic review and meta-analysis

Assessing the Determinants of Renewable Electricity Acceptance Integrating Meta-Analysis Regression and a Local Comprehensive Survey

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