Modelling monotonic effects of ordinal predictors in Bayesian regression models

Bürkner, Paul - Christian; Charpentier, Emmanuel

doi:10.1111/bmsp.12195

Cited by 116 publications

(99 citation statements)

References 53 publications

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“…Since vaccination intent is modelled as an ordered variable, one can expect the treatment to impact vaccination intent monotonically. To this end, W is modelled as a monotonic ordered predictor 63,64 . Using W as a predictor for Y has two advantages: it (1) controls for sampling discrepancies between the treatment and control groups, and (2) allows for the treatment to differentially affect those with different prior vaccination intents.…”

Section: Selection Of Imagesmentioning

confidence: 99%

Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA

et al. 2021

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he spread of SARS-CoV-2, the causative agent of COVID-19, has resulted in an unprecedented global public health and economic crisis 1,2. The outbreak was declared a pandemic by the World Health Organization on 11 March 2020 3 , and development of COVID-19 vaccines has been a major undertaking in fighting the disease. As of December 2020, many candidate vaccines have been shown to be safe and effective at generating an immune response 4-6 , with interim analysis of phase III trials suggesting efficacies as high as 95% 7-9. At least two vaccine candidates have been authorized for emergency use in the USA 10,11 , the UK 12,13 , the European Union 14 and elsewhere, with more candidates expected to follow soon. For these COVID-19 vaccines to be successful, they need to be not only be proven safe and efficacious, but also widely accepted. It is estimated that a novel COVID-19 vaccine will need to be accepted by at least 55% of the population to provide herd immunity, with estimates reaching as high as 85% depending on country and infection rate 15,16. Reaching these required vaccination levels should not be assumed given well-documented evidence of vaccine hesitancy across the world 17 , which is often fuelled by online and offline misinformation surrounding the importance, safety or effectiveness of vaccines 18-20. There has been widely circulating false information about the pandemic on social media platforms, such as that 5G mobile networks are linked with the virus, that vaccine trial participants have died after taking a candidate COVID-19 vaccine, and that the pandemic is a conspiracy or a bioweapon 21-23. Such information can build on pre-existing fears, seeding doubt and cynicism over new vaccines, and threatens to limit public uptake of COVID-19 vaccines. While large-scale vaccine rejection threatens herd immunity goals, large-scale acceptance with local vaccine rejection can also have negative consequences for community (herd) immunity, as clustering of non-vaccinators can disproportionately increase the needed percentage of vaccination coverage to achieve herd immunity in adjacent geographical regions and encourage epidemic spread 24. Estimates of acceptance of a COVID-19 vaccine in June 2020 suggest that 38% of the public surveyed in the UK and 34.2% of the public in the USA would accept a COVID-19 vaccine (a further 31% and 25% were, respectively, unsure that they would accept vaccination against COVID-19) 25. Worryingly, more recent polling in the USA (September 2020) has shown significant falls in willingness to accept a COVID-19 among both males and females, all age groups, all ethnicities and all major political groups 26 , possibly due to the heavy politicization of COVID-19 vaccination in the run up to the 2020 presidential election on both sides of the political debate 27,28. The public's willingness to accept a vaccine is therefore not static; it is highly responsive to current information and sentiment around a COVID-19 vaccine, as well as the state of the epidemic and perceived risk of contractin...

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Section: Selection Of Imagesmentioning

confidence: 99%

Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA

et al. 2021

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“…We did Bayesian binomial regression models (Carpenter et al 2017;Bürkner 2017;Bürkner & Charpentier 2020) to test the reliability of our effects. For clarity purposes, we will only report and explain the strong effects we found in the statistical analysis.…”

Section: Resultsmentioning

confidence: 99%

Failures of Gricean reasoning and the role of stereotypes in the production of gender marking in French

Pozniak

Burnett

2021

Glossa: A Journal of General Linguistics

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We partly replicate von der Malsburg et al. (2020)'s recent experiments investigating the relationship between speaker expectations, gender stereotypes and language use in English on a grammatical gender language: French. The results of our experiment show how the linguistic particularities of the English and French gender marking systems interact with speaker expectations and stereotypes to create different patterns of gender marking production. They also raise a puzzle for current theoretical and computational frameworks that formalize Gricean pragmatics, particularly those in which informativity (Gricean Quantity) is assumed to play a driving role in linguistic production.

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“…Real stage was modeled as a monotonic effect [58]. The interaction term, real stage Ã rater, accounts for pathologist/rater bias.…”

Section: Quantitative Assessment Of Concordancementioning

confidence: 99%

A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies

et al. 2021

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Non-alcoholic steatohepatitis (NASH) is a fatty liver disease characterized by accumulation of fat in hepatocytes with concurrent inflammation and is associated with morbidity, cirrhosis and liver failure. After extraction of a liver core biopsy, tissue sections are stained with hematoxylin and eosin (H&E) to grade NASH activity, and stained with trichrome to stage fibrosis. Methods to computationally transform one stain into another on digital whole slide images (WSI) can lessen the need for additional physical staining besides H&E, reducing personnel, equipment, and time costs. Generative adversarial networks (GAN) have shown promise for virtual staining of tissue. We conducted a large-scale validation study of the viability of GANs for H&E to trichrome conversion on WSI (n = 574). Pathologists were largely unable to distinguish real images from virtual/synthetic images given a set of twelve Turing Tests. We report high correlation between staging of real and virtual stains ($${\rho} = 0.86$$ ρ = 0.86 ; 95% CI: 0.84–0.88). Stages assigned to both virtual and real stains correlated similarly with a number of clinical biomarkers and progression to End Stage Liver Disease (Hazard Ratio HR = 2.06, 95% CI: 1.36–3.12, p < 0.001 for real stains; HR = 2.02, 95% CI: 1.40–2.92, p < 0.001 for virtual stains). Our results demonstrate that virtual trichrome technologies may offer a software solution that can be employed in the clinical setting as a diagnostic decision aid.

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Modelling monotonic effects of ordinal predictors in Bayesian regression models

Cited by 116 publications

References 53 publications

Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA

Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA

Failures of Gricean reasoning and the role of stereotypes in the production of gender marking in French

A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies

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