Applying Meta-Analysis to Adverse Impact Assessment: A Monte Carlo Simulation

2006 ◽  
Author(s):  
John F. Skinner ◽  
Scott B. Morris
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Impact Assessment ◽  
Meta Analysis ◽  
Adverse Impact

Bias and Precision of Alternate Estimators in Meta-Analysis: Benefits of Blending Schmidt-Hunter and Hedges Approaches

2017 ◽  
Vol 22 (2) ◽  
pp. 490-514 ◽  
Author(s):  
Michael T. Brannick ◽  
Sean M. Potter ◽  
Bryan Benitez ◽  
Scott B. Morris
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Confidence Interval ◽  
Root Mean Square Error ◽  
Random Effects ◽  
Meta Analysis ◽  
Size Distributions ◽  
Mean Square ◽  
The Mean

We describe a new estimator (labeled Morris) for meta-analysis. The Morris estimator combines elements of both the Schmidt-Hunter and Hedges estimators. The new estimator is compared to (a) the Schmidt-Hunter estimator, (b) the Schmidt-Hunter estimator with variance correction for the number of studies (“ k correction”), (c) the Hedges random-effects estimator, and (d) the Bonett unit weights estimator in a Monte Carlo simulation. The simulation was designed to represent realistic conditions faced by researchers, including population random-effects distributions, numbers of studies, and skewed sample size distributions. The simulation was used to evaluate the estimators with respect to bias, coverage of the 95% confidence interval of the mean, and root mean square error of estimates of the population mean. We also evaluated the quality of credibility intervals. Overall, the new estimator provides better coverage and slightly better credibility values than other commonly used methods. Thus it has advantages of both commonly used approaches without the apparent disadvantages. The new estimator can be implemented easily with existing software; software used in the study is available online, and an example is included in the appendix in the Supplemental Material available online.

scholarly journals Commuting-Adjusted Short-Term Health Impact Assessment of Airborne Fine Particles with Uncertainty Quantification via Monte Carlo Simulation

2015 ◽  
Vol 123 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Michela Baccini ◽  
Laura Grisotto ◽  
Dolores Catelan ◽  
Dario Consonni ◽  
Pier Alberto Bertazzi ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Impact Assessment ◽  
Uncertainty Quantification ◽  
Health Impact Assessment ◽  
Fine Particles ◽  
Health Impact ◽  
Short Term

Aflatoxin M1 in human breast milk: A global systematic review, meta-analysis, and risk assessment study (Monte Carlo simulation)

2019 ◽  
Vol 88 ◽  
pp. 333-342 ◽  
Author(s):  
Yadolah Fakhri ◽  
Jamal Rahmani ◽  
Carlos Augusto Fernandes Oliveira ◽  
Larissa Tuanny Franco ◽  
Carlos Humberto Corassin ◽  
...  
Keyword(s):  
Systematic Review ◽  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Breast Milk ◽  
Meta Analysis ◽  
Aflatoxin M1 ◽  
Human Breast Milk ◽  
Human Breast ◽  
Assessment Study

Improved impact assessment of odorous compounds from landfills using Monte Carlo simulation

2019 ◽  
Vol 648 ◽  
pp. 805-810 ◽  
Author(s):  
Yanjun Liu ◽  
Wenjing Lu ◽  
Hongtao Wang ◽  
Xingbao Gao ◽  
Qifei Huang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Impact Assessment ◽  
Odorous Compounds

scholarly journals Linezolid pharmacokinetics in MDR-TB: a systematic review, meta-analysis and Monte Carlo simulation

2018 ◽  
Vol 73 (7) ◽  
pp. 1755-1762 ◽  
Author(s):  
James Millard ◽  
Henry Pertinez ◽  
Laura Bonnett ◽  
Eva Maria Hodel ◽  
Véronique Dartois ◽  
...  
Keyword(s):  
Systematic Review ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Meta Analysis ◽  
Mdr Tb
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