Investigating environmental regulation effects on technological innovation: A meta-regression analysis

2021 ◽  
pp. 0958305X2110696
Author(s):  
Zhuanlan Sun ◽  
Demi Zhu
Keyword(s):  
Environmental Regulation ◽  
Bayesian Model Averaging ◽  
Meta Analysis ◽  
Model Averaging ◽  
Policy Instrument ◽  
Five Factors ◽  
Level Data ◽  
Study Results ◽  
The Difference ◽  
Meta Regression Analysis

The relationship between environmental regulation (ER) and any associated innovative technologies has been studied in the previous decades; however, the estimated results have varied with no obvious consensus. To analyse what drives the different estimates in existing studies, we investigated the regulation–innovation relationship through a meta-analysis of 1276 estimates reported in 49 studies. In our analysis, 41 aspects of study design were controlled, Bayesian model averaging (BMA), and frequentist model averaging (FMA) methods were used to address model uncertainty problems. Our results suggested that controlling resources and ignoring endogeneity problems both played robust and methodical roles in explaining the differences in individual study results. Additionally, our results also indicated that five factors (middle year, publication year, usage of province-level data, linear model function, and the difference-in-differences (DID) model) consistently explained the differences in the reported estimates. We found that the ER had almost zero influence on technical innovation; more than one flexible policy instrument was required to trigger innovative activities among firms and sectors.

Carbapenem Resistance, Initial Antibiotic Therapy, and Mortality inKlebsiella pneumoniaeBacteremia: A Systematic Review and Meta-Analysis

2017 ◽  
Vol 38 (11) ◽  
pp. 1319-1328 ◽  
Author(s):  
Philipp P. Kohler ◽  
Cheryl Volling ◽  
Karen Green ◽  
Elizabeth M. Uleryk ◽  
Prakesh S. Shah ◽  
...  
Keyword(s):  
Systematic Review ◽  
Antibiotic Therapy ◽  
Meta Analysis ◽  
Carbapenem Resistance ◽  
Carbapenem Resistant ◽  
Initial Antibiotic Therapy ◽  
Meta Regression ◽  
The Difference ◽  
Meta Regression Analysis ◽  
The Impact

BACKGROUNDMortality associated with infections caused by carbapenem-resistantEnterobacteriaceae(CRE) is higher than mortality due to carbapenem-sensitive pathogens.OBJECTIVETo examine the association between mortality from bacteremia caused by carbapenem-resistant (CRKP) and carbapenem-sensitiveKlebsiella pneumoniae(CSKP) and to assess the impact of appropriate initial antibiotic therapy (IAT) on mortality.DESIGNSystematic review and meta-analysisMETHODSWe searched MEDLINE, EMBASE, CINAHL, and Wiley Cochrane databases through August 31, 2016, for observational studies reporting mortality among adult patients with CRKP and CSKP bacteremia. Search terms were related toKlebsiella, carbapenem-resistance, and infection. Studies including fewer than 10 patients per group were excluded. A random-effects model and meta-regression were used to assess the relationship between carbapenem-resistance, appropriateness of IAT, and mortality.RESULTSMortality was higher in patients who had CRKP bacteremia than in patients with CSKP bacteremia (15 studies; 1,019 CRKP and 1,148 CSKP patients; unadjusted odds ratio [OR], 2.2; 95% confidence interval [CI], 1.8–2.6; I2=0). Mortality was lower in patients with appropriate IAT than in those without appropriate IAT (7 studies; 658 patients; unadjusted OR, 0.5; 95% CI, 0.3–0.8; I2=36%). CRKP patients (11 studies; 1,326 patients; 8-year period) were consistently less likely to receive appropriate IAT (unadjusted OR, 0.5; 95% CI, 0.3–0.7; I2=43%). Our meta-regression analysis identified a significant association between the difference in appropriate IAT and mortality (OR per 10% difference in IAT, 1.3; 95% CI, 1.0–1.6).CONCLUSIONSAppropriateness of IAT is an important contributor to the observed difference in mortality between patients with CRKP bacteremia and patients with CSKP bacteremia.Infect Control Hosp Epidemiol2017;38:1319–1328

scholarly journals A Bayesian Model-Averaged Meta-Analysis of the Power Pose Effect with Informed and Default Priors: The Case of Felt Power

2017 ◽  
Author(s):  
Quentin Frederik Gronau ◽  
Sara van Erp ◽  
Daniel W. Heck ◽  
Joseph Cesario ◽  
Kai Jonas ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Model Averaging ◽  
Fixed Effect Model ◽  
Body Postures ◽  
Effect Model ◽  
Meta Analyses

Carney, Cuddy, and Yap (2010) found that --compared to participants who adopted constrictive body postures-- participants who adopted expansive body postures reported feeling more powerful, showed an increase in testosterone and a decrease in cortisol, and displayed anincreased tolerance for risk. However, these power pose effects have recently come under considerable scrutiny. Here we present a Bayesian meta-analysis of six preregistered studies from this special issue, focusing on the effect of power posing on felt power. Our analysisimproves on standard classical meta-analyses in several ways. First and foremost, we considered only preregistered studies, eliminating concerns about publication bias. Second, the Bayesian approach enables us to quantify evidence for both the alternative and the null hypothesis. Third, we use Bayesian model-averaging to account for the uncertainty with respect to the choice for a fixed-effect model or a random-effect model. Fourth, based on a literature review we obtained an empirically informed prior distribution for the between-studyheterogeneity of effect sizes. This empirically informed prior can serve as a default choice not only for the investigation of the power pose effect, but for effects in the field of psychology more generally. For effect size, we considered a default and an informed prior. Our meta-analysis yields very strong evidence for an effect of power posing on felt power. However, when the analysis is restricted to participants unfamiliar with the effect, the meta-analysis yields evidence that is only moderate.

scholarly journals Meta-Analysis with Few Studies and Binary Data: A Bayesian Model Averaging Approach

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2159
Author(s):  
Francisco-José Vázquez-Polo ◽  
Miguel-Ángel Negrín-Hernández ◽  
María Martel-Escobar
Keyword(s):  
Model Uncertainty ◽  
Binary Data ◽  
Posterior Probability ◽  
Bayesian Model Averaging ◽  
Meta Analysis ◽  
Cluster Structure ◽  
Model Averaging ◽  
Cluster Models ◽  
Mixing Distribution ◽  
Sample Heterogeneity

In meta-analysis, the existence of between-sample heterogeneity introduces model uncertainty, which must be incorporated into the inference. We argue that an alternative way to measure this heterogeneity is by clustering the samples and then determining the posterior probability of the cluster models. The meta-inference is obtained as a mixture of all the meta-inferences for the cluster models, where the mixing distribution is the posterior model probabilities. When there are few studies, the number of cluster configurations is manageable, and the meta-inferences can be drawn with BMA techniques. Although this topic has been relatively neglected in the meta-analysis literature, the inference thus obtained accurately reflects the cluster structure of the samples used. In this paper, illustrative examples are given and analysed, using real binary data.

Using Nonword Repetition to Identify Language Impairment in Bilingual Children: A Meta-Analysis of Diagnostic Accuracy

2021 ◽  
pp. 1-21
Author(s):  
José A. Ortiz
Keyword(s):  
Diagnostic Accuracy ◽  
Language Impairment ◽  
Meta Analysis ◽  
Nonword Repetition ◽  
Bilingual Children ◽  
Eligibility Criteria ◽  
Study Results ◽  
Meta Regression Analysis ◽  
Word Search ◽  
Meta Analyses

Purpose Nonword repetition has been endorsed as a less biased method of assessment for children from culturally and linguistically diverse backgrounds, but there are currently no systematic reviews or meta-analyses on its use with bilingual children. The purpose of this study was to evaluate diagnostic accuracy of nonword repetition in the identification of language impairment (LI) in bilingual children. Method Using a key word search of peer-reviewed literature from several large electronic databases, as well as ancestral and forward searches, 13 studies were identified that met the eligibility criteria. Studies were evaluated on the basis of quality of evidence, design characteristics, and reported diagnostic accuracy. A meta-regression analysis, based on study results, was conducted to identify task characteristics that may be associated with better classification accuracy. Results Diagnostic accuracy across studies ranged from poor to good. Bilingual children with LI performed with more difficulty on nonword repetition tasks than those with typical language. Quasi-universal tasks, which account for the phonotactic constraints of multiple languages, exhibited better diagnostic accuracy and resulted in less misidentification of children with typical language than language-specific tasks. Conclusions Evidence suggests that nonword repetition may be a useful tool in the assessment and screening of LI in bilingual children, though it should be used in conjunction with other measures. Quasi-universal tasks demonstrate the potential to further reduce assessment bias, but extant research is limited.

scholarly journals Uncertainty analysis of hydrological multi-model ensembles based on CBP-BMA method

2018 ◽  
Vol 49 (5) ◽  
pp. 1636-1651 ◽  
Author(s):  
Shaokun He ◽  
Shenglian Guo ◽  
Zhangjun Liu ◽  
Jiabo Yin ◽  
Kebing Chen ◽  
...  
Keyword(s):  
Flood Control ◽  
Bayesian Model Averaging ◽  
Evaluation Criteria ◽  
Model Performance ◽  
Model Averaging ◽  
Probability Forecast ◽  
Average Deviation ◽  
Probabilistic Forecasts ◽  
Study Results ◽  
Model Ensembles

Abstract Quantification of the inherent uncertainty in hydrologic forecasting is essential for flood control and water resources management. The existing approaches, such as Bayesian model averaging (BMA), hydrologic uncertainty processor (HUP), copula-BMA (CBMA), aim at developing reliable probabilistic forecasts to characterize the uncertainty induced by model structures. In the probability forecast framework, these approaches either assume the probability density function (PDF) to follow a certain distribution, or are unable to reduce bias effectively for complex hydrological forecasts. To overcome these limitations, a copula Bayesian processor associated with BMA (CBP-BMA) method is proposed with ensemble lumped hydrological models. Comparing with the BMA and CBMA methods, the CBP-BMA method relaxes any assumption on the distribution of conditional PDFs. Several evaluation criteria, such as containing ratio, average bandwidth and average deviation amplitude of probabilistic application, are utilized to evaluate the model performance. The case study results demonstrate that the CBP-BMA method can improve hydrological forecasting precision with higher cover ratios more than 90%, which are increased by 4.4% and 3.2%, 2.2% and 1.7% over those of BMA and CBMA during the calibration and validation periods, respectively. The proposed CBP-BMA method provides an alternative approach for uncertainty estimation of hydrological multi-model forecasts.

A Comparison between Raw Ensemble Output, (Modified) Bayesian Model Averaging, and Extended Logistic Regression Using ECMWF Ensemble Precipitation Reforecasts

2010 ◽  
Vol 138 (11) ◽  
pp. 4199-4211 ◽  
Author(s):  
Maurice J. Schmeits ◽  
Kees J. Kok
Keyword(s):  
Logistic Regression ◽  
Bias Correction ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
The Difference ◽  
Model Output Statistics

Abstract Using a 20-yr ECMWF ensemble reforecast dataset of total precipitation and a 20-yr dataset of a dense precipitation observation network in the Netherlands, a comparison is made between the raw ensemble output, Bayesian model averaging (BMA), and extended logistic regression (LR). A previous study indicated that BMA and conventional LR are successful in calibrating multimodel ensemble forecasts of precipitation for a single forecast projection. However, a more elaborate comparison between these methods has not yet been made. This study compares the raw ensemble output, BMA, and extended LR for single-model ensemble reforecasts of precipitation; namely, from the ECMWF ensemble prediction system (EPS). The raw EPS output turns out to be generally well calibrated up to 6 forecast days, if compared to the area-mean 24-h precipitation sum. Surprisingly, BMA is less skillful than the raw EPS output from forecast day 3 onward. This is due to the bias correction in BMA, which applies model output statistics to individual ensemble members. As a result, the spread of the bias-corrected ensemble members is decreased, especially for the longer forecast projections. Here, an additive bias correction is applied instead and the equation for the probability of precipitation in BMA is also changed. These modifications to BMA are referred to as “modified BMA” and lead to a significant improvement in the skill of BMA for the longer projections. If the area-maximum 24-h precipitation sum is used as a predictand, both modified BMA and extended LR improve the raw EPS output significantly for the first 5 forecast days. However, the difference in skill between modified BMA and extended LR does not seem to be statistically significant. Yet, extended LR might be preferred, because incorporating predictors that are different from the predictand is straightforward, in contrast to BMA.

scholarly journals No Need to Choose: Robust Bayesian Meta-Analysis with Competing Publication Bias Adjustment Methods

2021 ◽  
Author(s):  
František Bartoš ◽  
Maximilian Maier ◽  
Eric-Jan Wagenmakers ◽  
Hristos Doucouliagos ◽  
T D Stanley
Keyword(s):  
Publication Bias ◽  
Bayesian Model Averaging ◽  
Meta Analysis ◽  
Scientific Evidence ◽  
Model Averaging ◽  
Bias Adjustment ◽  
Model Average ◽  
Wide Range ◽  
Data Generating Process ◽  
The Impact

Publication bias is a ubiquitous threat to the validity of meta-analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation studies have shown the methods’ performance to depend on the true data generating process – no method consistently outperforms the others across a wide range of conditions. To avoid the condition-dependent, all-or-none choice between competing methods we extend robust Bayesian meta-analysis and model-average across two prominent approaches of adjusting for publication bias: (1) selection models of p-values and (2) models of the relationship between effect sizes and their standard errors. The resulting estimator weights the models with the support they receive from the existing research record. Applications, simulations, and comparisons to preregistered, multi-lab replications demonstrate the benefits of Bayesian model-averaging of competing publication bias adjustment methods.

scholarly journals In search of determinants of FDI backward spillovers: a meta-analysis

2019 ◽  
Vol 8 (3) ◽  
pp. 122
Author(s):  
Hongzhong Fan ◽  
Shi He
Keyword(s):  
Foreign Direct Investment ◽  
Ownership Structure ◽  
Direct Investment ◽  
Bayesian Model Averaging ◽  
Meta Analysis ◽  
Model Averaging ◽  
Foreign Firms ◽  
Prime Objective ◽  
Firm Attributes ◽  
Local Firms

Drawing on a unique dataset of 694 estimates from 24 studies on foreign direct investment backward productivity spillovers in China, our prime objective is to investigate determinants of backward spillovers from foreign direct investment using Bayesian Model Averaging based Meta-Analysis. Our results suggest that backward spillovers vary across firm attributes, including the ownership structure of foreign firms, the origin of foreign firms, market orientation of foreign firms, the ownership structure of local firms and the technological levels of local firms. For instance, export-orientated foreign firms generate largest benefits of backward spillovers for the domestic economy among firm attributes.

scholarly journals Positron emission tomography (PET) for prediction of glioma histology: protocol for an individual-level data meta-analysis of test performance

BMJ Open ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. e020187 ◽  
Author(s):  
Nikolaos A Trikalinos ◽  
Takashi Nihashi ◽  
Evangelos Evangelou ◽  
Teruhiko Terasawa
Keyword(s):  
Positron Emission Tomography ◽  
Test Performance ◽  
Meta Analysis ◽  
Clinical Trial Design ◽  
Emission Tomography ◽  
Low Grade ◽  
Individual Level ◽  
Positron Emission ◽  
Level Data ◽  
Study Results

IntroductionGliomas, the most commonly diagnosed primary brain tumours, are associated with varied survivals based, in part, on their histological subtype. Therefore, accurate pretreatment tumour grading is essential for patient care and clinical trial design.Methods and analysisWe will perform an individual-level data meta-analysis of published studies to evaluate the ability of different types of positron emission tomography (PET) to differentiate high from low-grade gliomas. We will search PubMed and Scopus from inception through 30 July 2017 with no language restriction and full-text evaluation of potentially relevant articles. We will choose studies that assess PET using 18-Fludeoxyglucose (18F-FDG), l-[Methyl-()11C]Methionine (11C-MET), 18F-Fluoro-Ethyl-Tyrosine (18F-FET) or (18)F-Fluorothymidine (18F-FLT)for grading, verified with histological confirmation. We will include both prospective and retrospective studies. Bias will be assessed by two reviewers with the Quality Assessment of Diagnostic Accuracy Studies-2 tool and as per method described by Deeks et al.Ethics and disseminationEthics approval was not applicable, as this is a meta-analytic study. Results of the analysis will be submitted for publication in a peer-reviewed journal.PROSPERO registration numberCRD42017078649.

scholarly journals A Bayesian Model-Averaged Meta-Analysis of the Power Pose Effect with Informed and Default Priors: The Case of Felt Power

2017 ◽  
Author(s):  
Quentin Frederik Gronau ◽  
Sara van Erp ◽  
Daniel W. Heck ◽  
Joseph Cesario ◽  
Kai Jonas ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Model Averaging ◽  
Fixed Effect Model ◽  
Body Postures ◽  
Effect Model ◽  
Meta Analyses

Carney, Cuddy, and Yap (2010) found that --compared to participants who adopted constrictive body postures-- participants who adopted expansive body postures reported feeling more powerful, showed an increase in testosterone and a decrease in cortisol, and displayed an increased tolerance for risk. However, these power pose effects have recently come under considerable scrutiny. Here we present a Bayesian meta-analysis of six preregistered studies from this special issue, focusing on the effect of power posing on felt power. Our analysis improves on standard classical meta-analyses in several ways. First and foremost, we considered only preregistered studies, eliminating concerns about publication bias. Second, the Bayesian approach enables us to quantify evidence for both the alternative and the null hypothesis. Third, we use Bayesian model-averaging to account for the uncertainty with respect to the choice for a fixed-effect model or a random-effect model. Fourth, based on a literature review we obtained an empirically informed prior distribution for the between-study heterogeneity of effect sizes. This empirically informed prior can serve as a default choice not only for the investigation of the power pose effect, but for effects in the field of psychology more generally. For effect size, we considered a default and an informed prior. Our meta-analysis yields very strong evidence for an effect of power posing on felt power. However, when the analysis is restricted to participants unfamiliar with the effect, the meta-analysis yields evidence that is only moderate.

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