Evidence of absence regression: a binomial N‐mixture model for estimating fatalities at wind energy facilities

Evidence of Absence Regression: A Binomial N-Mixture Model for Estimating Bird and Bat Fatalities at Wind Power Facilities

10.1101/2020.01.21.914754 ◽

2020 ◽

Author(s):

Trent McDonald ◽

Kimberly Bay ◽

Jared Studyvin ◽

Jesse Leckband ◽

Amber Schorg ◽

...

Keyword(s):

Wind Power ◽

Mixture Model ◽

Negative Binomial ◽

Negative Binomial Regression ◽

Complete Separation ◽

Probability Of Detection ◽

Deposition Rates ◽

Power Facility ◽

Binomial Regression ◽

Evidence Of Absence

AbstractEstimating bird and bat fatalities caused by wind-turbine facilities is challenging when fatalities are rare and the number of observed carcasses is either exactly zero or very near zero. The rarity of found carcasses is exacerbated when particular species are rare, when carcasses degrade quickly, when they are removed by scavengers, or when they are not detected by observers. With few observed fatalities, common statistical methods like logistic, Poisson, or negative binomial regression are biased and prone to fail due to complete or quasi-complete separation. Here, we propose a binomial N-mixture model to estimate fatality rates and totals that incorporates study covariates and separate information on probability of detection. Our model extends the ‘evidence of absence’ model (Huso et al., 2015) by relating carcass deposition rates to study covariates and by incorporating the number of turbines. Our model, which we call Evidence of Absence Regression (EoAR), can retrospectively and prospectively estimate the total number of birds or bats killed at a single wind-power facility or a fleet of wind-power facilities given covariates in the relation. Furthermore, with accurate prior distributions the model’s results are extremely robust to complete or quasi-complete separation. In this paper, we describe the model, show its low bias and high precision via computer simulation, and apply it to bat fatalities observed on 21 wind power facilities in Iowa.

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The association of Raynaud’s phenomenon/syndrome with scleroderma and Sjögren’s syndrome – a meta-analysis

VASA ◽

10.1024/0301-1526.37.s73.26 ◽

2008 ◽

Vol 37 (Supplement 73) ◽

pp. 26-32 ◽

Cited By ~ 1

Author(s):

Schlattmann ◽

Höhne ◽

Plümper ◽

Heidrich

Keyword(s):

Quantitative Analysis ◽

Sjögren’S Syndrome ◽

Mixture Model ◽

Sjögren's Syndrome ◽

Meta Analysis ◽

Sjogren’S Syndrome ◽

Sjogren's Syndrome ◽

Raynaud’S Syndrome ◽

Raynaud's Syndrome ◽

Sjögren‘S Syndrome

Background: In order to analyze the prevalence of Raynaud’s syndrome in diseases such as scleroderma and Sjögren’s syndrom – a meta-analysis of published data was performed. Methods: The PubMed data base of the National Library of Medicine was used for studies dealing with Raynaud’s syndrome and scleroderma or Raynaud’s syndroem and Sjögren’s syndrom respectively. The studies found provided data sufficient to estimate the prevalence of Raynaud’s syndrome. The statistical analysis was based on methods for a fixed effects meta-analysis and finite mixture model for proportions. Results: For scleroderma a pooled prevalence of 80.9% and 95% CI (0.78, 0.83) was obtained. A mixture model analysis found four latent classes. We identified a class with a very low prevalence of 11%, weighted with 0.15. On the other hand there is a class with a very high prevalence of 96%. Analysing the association with Sjögren’s syndrome, the pooled analysis leads to a prevalence of Raynaud’s syndrome of 32%, 95% CI(26.7%, 37.7%). A mixture model finds a solution with two latent classes. Here, 38% of the studies show a prevalence of 18.8% whereas 62% observe a prevalence of 38.3%. Conclusion: There is strong variability of studies reporting the prevalence of Raynaud’s syndrome in patients suffering from scleroderma or Sjögren’s syndrome. The available data are insufficient to perform a proper quantitative analysis of the association of Raynaud’s phenomenon with scleroderma or Sjögren’s syndrome. Properly planned and reported epidemiological studies are needed in order to perform a thorough quantitative analysis of risk factors for Raynaud’s syndrome.

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