scholarly journals The Support Interval

2018 ◽  
Author(s):  
Eric-Jan Wagenmakers ◽  
Quentin Frederik Gronau ◽  
Fabian Dablander ◽  
Alexander Etz
Keyword(s):  
Confidence Interval ◽  
Hypothesis Test ◽  
Credible Interval ◽  
Minimum Amount ◽  
Evidence Based ◽  
Support Interval ◽  
Bayesian Credible Interval ◽  
Parameter Values ◽  
Lindley’S Paradox ◽  
Bayesian Support

A frequentist confidence interval can be constructed by inverting a hypothesis test, such that the interval contains only parameter values that would not have been rejected by the test. We show how a similar definition can be employed to construct a Bayesian support interval. Consistent with Carnap’s theory of corroboration, the support interval contains only parameter values that receive at least some minimum amount of support from the data. The support interval is not subject to Lindley’s paradox and provides an evidence-based perspective on inference that differ from the belief-based perspective that forms the basis of the standard Bayesian credible interval.

The Support Interval

2019 ◽  
Author(s):  
Eric-Jan Wagenmakers ◽  
Quentin Frederik Gronau ◽  
Fabian Dablander ◽  
Alexander Etz
Keyword(s):  
Confidence Interval ◽  
Hypothesis Test ◽  
Credible Interval ◽  
Minimum Amount ◽  
Evidence Based ◽  
Support Interval ◽  
Bayesian Credible Interval ◽  
Parameter Values ◽  
Lindley’S Paradox ◽  
Bayesian Support

A frequentist confidence interval can be constructed by inverting a hypothesis test, such that the interval contains only parameter values that would not have been rejected by the test. We show how a similar definition can be employed to construct a Bayesian support interval. Consistent with Carnap’s theory of corroboration, the support interval contains only parameter values that receive at least some minimum amount of support from the data. The support interval is not subject to Lindley's paradox and provides an evidence-based perspective on inference that differs from the belief-based perspective that forms the basis of the standard Bayesian credible interval.

scholarly journals Empirical Bayes estimation of farm prevalence adjusting for multistage sampling and uncertainty in test performance: a Brucella cross-sectional serostudy in southern Kazakhstan

2016 ◽  
Vol 144 (16) ◽  
pp. 3531-3539 ◽  
Author(s):  
W. BEAUVAIS ◽  
M. ORYNBAYEV ◽  
J. GUITIAN
Keyword(s):  
Confidence Interval ◽  
Test Performance ◽  
Empirical Bayes ◽  
Small Ruminants ◽  
Credible Interval ◽  
Bayes Estimation ◽  
Test Accuracy ◽  
Endemic Region ◽  
Cross Sectional ◽  
Seropositive Animal

SUMMARYEstimation of farm prevalence is common in veterinary research. Typically, not all animals within the farm are sampled, and imperfect tests are used. Often, assumptions about herd sizes and sampling proportions are made, which may be invalid in smallholder settings. We propose an alternative method for estimating farm prevalence in the context of Brucella seroprevalence estimation in an endemic region of Kazakhstan. We collected 210 milk samples from Otar district, with a population of about 1000 cattle and 16 000 small ruminants, and tested them using an indirect ELISA. Individual-level prevalence and 95% confidence intervals were estimated using Taylor series linearization. A model was developed to estimate the smallholding prevalence, taking into account variable sampling proportions and uncertainty in the test accuracy. We estimate that 73% of households that we sampled had at least one Brucella-seropositive animal (95% credible interval 68–82). We estimate that 58% (95% confidence interval 40–76) of lactating small ruminants and 14% (95% confidence interval 1–28) of lactating cows were seropositive. Our results suggest that brucellosis is highly endemic in the area and conflict with those of the official brucellosis-testing programme, which found that in 2013 0% of cows and 1·7% of small ruminants were seropositive.

scholarly journals Decline towards extinction of Mexico's vaquita porpoise ( Phocoena sinus )

2019 ◽  
Vol 6 (7) ◽  
pp. 190598 ◽  
Author(s):  
Armando M. Jaramillo-Legorreta ◽  
Gustavo Cardenas-Hinojosa ◽  
Edwyna Nieto-Garcia ◽  
Lorenzo Rojas-Bracho ◽  
Len Thomas ◽  
...  
Keyword(s):  
Population Size ◽  
Total Population ◽  
Population Decline ◽  
Credible Interval ◽  
Critically Endangered ◽  
Posterior Mean ◽  
Bayesian Credible Interval ◽  
Independent Estimate ◽  
Rate Of Decline ◽  
Passive Acoustic

The vaquita ( Phocoena sinus ) is a small porpoise endemic to Mexico. It is listed by IUCN as Critically Endangered because of unsustainable levels of bycatch in gillnets. The population has been monitored with passive acoustic detectors every summer from 2011 to 2018; here we report results for 2017 and 2018. We combine the acoustic trends with an independent estimate of population size from 2015, and visual observations of at least seven animals in 2017 and six in 2018. Despite adoption of an emergency gillnet ban in May 2015, the estimated rate of decline remains extremely high: 48% decline in 2017 (95% Bayesian credible interval (CRI) 78% decline to 9% increase) and 47% in 2018 (95% CRI 80% decline to 13% increase). Estimated total population decline since 2011 is 98.6%, with greater than 99% probability the decline is greater than 33% yr −1 . We estimate fewer than 19 vaquitas remained as of summer 2018 (posterior mean 9, median 8, 95% CRI 6–19). From March 2016 to March 2019, 10 dead vaquitas killed in gillnets were found. The ongoing presence of illegal gillnets despite the emergency ban continues to drive the vaquita towards extinction. Immediate management action is required if the species is to be saved.

scholarly journals Associations between Owners’ Reports of Unwanted Ridden Behaviour and In-Hand Behaviour in Horses

Animals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 2431
Author(s):  
Nicole Romness ◽  
Kate Fenner ◽  
Jessica McKenzie ◽  
Ashley Anzulewicz ◽  
Bibiana Burattini ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Confidence Interval ◽  
Odds Ratio ◽  
B Value ◽  
Parallel Analysis ◽  
Evidence Based ◽  
Linear Modelling ◽  
Reported Data ◽  
First Time

An evidence-based understanding of dangerous or unwelcome behaviour in horses would greatly benefit both horses and humans who interact with them. Using owner-reported data from the Equine Behaviour Assessment and Research Questionnaire (E-BARQ), the current study investigated in-hand behaviours associated with dangerous or unwelcome ridden behaviours, notably bolting, rearing and bucking. Respondents (n = 1584) to the ridden horse section of the E-BARQ answered 42 demographic questions, followed by 268 behavioural items. Parallel analysis was conducted to group individual behaviours into rotated components to create independent and dependent indices. Multivariable general linear modelling and ordinal logistic regression were used to identify behaviours associated with bolting, rearing and bucking. Results revealed that safety-from-bolt increased as social confidence with horses (Odds ratio (OR) = 1.06; 95% confidence interval (cf = 1.02–1.09) and other animals (OR = 1.08; cf = 1.03–1.12), compliance in-hand (OR = 1.10; cf = 1.06–1.16) and tolerance of restraint (OR = 1.05; cf = 1.0–1.11) increased; and decreased as loading problems (OR = 0.95; cf = 0.92–0.99) increased. Safety-from-rear increased as tolerance of restraint (OR = 1.07; cf = 1.02–1.12) and social confidence with other animals (OR = 1.05; cf = 1.01–1.09) increased; and decreased as loading problems (OR = 0.94; cf = 0.91–0.98) increased. Safety-from-buck increased as social confidence with horses (b-value = 0.011, p < 0.001) and other animals (b-value = 0.010, p = 0.002), compliance in-hand (b-value = 0.015, p < 0.001), tolerance of restraint (b-value = 0.009, p = 0.027) and tolerance of haltering/bridling (b-value = 0.016, p = 0.010) increased, and it decreased as loading problems increased (b-value = −0.011, p < 0.001). By revealing, for the first time, that specific behaviours on the ground are associated with particular responses in the same horses when ridden, this study advances equitation science considerably. Identification of risk factors for dangerous behaviour while under saddle can improve safety for horses and riders and highlights the importance of effective and humane in-hand training.

Explorations in statistics: confidence intervals

2009 ◽  
Vol 33 (2) ◽  
pp. 87-90 ◽  
Author(s):  
Douglas Curran-Everett
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Hypothesis Test ◽  
Statistical Information ◽  
Experimental Result ◽  
P Value ◽  
Population Parameter ◽  
True Value ◽  
Level Of Confidence ◽  
The Mean

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This third installment of Explorations in Statistics investigates confidence intervals. A confidence interval is a range that we expect, with some level of confidence, to include the true value of a population parameter such as the mean. A confidence interval provides the same statistical information as the P value from a hypothesis test, but it circumvents the drawbacks of that hypothesis test. Even more important, a confidence interval focuses our attention on the scientific importance of some experimental result.

scholarly journals Facility and Regional Factors Associated With the New Adoption of Electronic Medical Records in Japan: Nationwide Longitudinal Observational Study (Preprint)

2019 ◽  
Author(s):  
Hideaki Kawaguchi ◽  
Soichi Koike ◽  
Kazuhiko Ohe
Keyword(s):  
Longitudinal Data ◽  
Logistic Models ◽  
Credible Interval ◽  
Fiscal Year ◽  
Multinomial Logistic Model ◽  
Factors Associated ◽  
Bayesian Credible Interval ◽  
Emr Adoption ◽  
Regional Factors ◽  
Over Time

BACKGROUND The rate of adoption of electronic medical record (EMR) systems has increased internationally, and new EMR adoption is currently a major topic in Japan. However, no study has performed a detailed analysis of longitudinal data to evaluate the changes in the EMR adoption status over time. OBJECTIVE This study aimed to evaluate the changes in the EMR adoption status over time in hospitals and clinics in Japan and to examine the facility and regional factors associated with these changes. METHODS Secondary longitudinal data were created by matching data in fiscal year (FY) 2011 and FY 2014 using reference numbers. EMR adoption status was defined as “EMR adoption,” “specified adoption schedule,” or “no adoption schedule.” Data were obtained for hospitals (n=4410) and clinics (n=67,329) that had no adoption schedule in FY 2011 and for hospitals (n=1068) and clinics (n=3132) with a specified adoption schedule in FY 2011. The EMR adoption statuses of medical institutions in FY 2014 were also examined. A multinomial logistic model was used to investigate the associations between EMR adoption status in FY 2014 and facility and regional factors in FY 2011. Considering the regional variations of these models, multilevel analyses with second levels were conducted. These models were constructed separately for hospitals and clinics, resulting in four multinomial logistic models. The odds ratio (OR) and 95% Bayesian credible interval (CI) were estimated for each variable. RESULTS A total of 6.9% of hospitals and 14.82% of clinics with no EMR adoption schedules in FY 2011 had adopted EMR by FY 2014, while 10.49% of hospitals and 33.65% of clinics with specified adoption schedules in FY 2011 had cancelled the scheduled adoption by FY 2014. For hospitals with no adoption schedules in FY 2011, EMR adoption/scheduled adoption was associated with practice size characteristics, such as number of outpatients (from quantile 4 to quantile 1: OR 1.67, 95% CI 1.005-2.84 and OR 2.40, 95% CI 1.80-3.21, respectively), and number of doctors (from quantile 4 to quantile 1: OR 4.20, 95% CI 2.39-7.31 and OR 2.02, 95% CI 1.52-2.64, respectively). For clinics with specified EMR adoption schedules in FY 2011, the factors negatively associated with EMR adoption/cancellation of scheduled EMR adoption were the presence of beds (quantile 4 to quantile 1: OR 0.57, 95% CI 0.45-0.72 and OR 0.74, 95% CI 0.58-0.96, respectively) and having a private establisher (quantile 4 to quantile 1: OR 0.27, 95% CI 0.13-0.55 and OR 0.43, 95% CI 0.19-0.91, respectively). No regional factors were significantly associated with the EMR adoption status of hospitals with no EMR adoption schedules; population density was positively associated with EMR adoption in clinics with no EMR adoption schedule (quantile 4 to quantile 1: OR 1.49, 95% CI 1.32-1.69). CONCLUSIONS Different approaches are needed to promote new adoption of EMR systems in hospitals as compared to clinics. It is important to induce decision making in small- and medium-sized hospitals, and regional postdecision technical support is important to avoid cancellation of scheduled EMR adoption in clinics.

scholarly journals Comparison of Bayesian and frequentist methods for prevalence estimation under misclassification

2020 ◽  
Author(s):  
Matthias Flor ◽  
Michael Weiβ ◽  
Thomas Selhorst ◽  
Christine Müller-Graf ◽  
Matthias Greiner
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Bayesian Method ◽  
Estimation Method ◽  
Simulated Data ◽  
Credible Interval ◽  
Interval Length ◽  
Point Estimate ◽  
Data Sets ◽  
Prevalence Estimation

Abstract Background: Various methods exist for statistical inference about a prevalence that consider misclassifications due to an imperfect diagnostic test. However, traditional methods are known to suffer from truncation of the prevalence estimate and the confidence intervals constructed around the point estimate, as well as from under-performance of the confidence intervals' coverage. Methods: In this study, we used simulated data sets to validate a Bayesian prevalence estimation method and compare its performance to frequentist methods, i.e. the Rogan-Gladen estimate for prevalence, RGE, in combination with several methods of confidence interval construction. Our performance measures are (i) error distribution of the point estimate against the simulated true prevalence and (ii) coverage and length of the confidence interval, or credible interval in the case of the Bayesian method. Results: Across all data sets, the Bayesian point estimate and the RGE produced similar error distributions with slight advanteges of the former over the latter. In addition, the Bayesian estimate did not suffer from the RGE's truncation problem at zero or unity. With respect to coverage performance of the confidence and credible intervals, all of the traditional frequentist methods exhibited strong under-coverage, whereas the Bayesian credible interval as well as a newly developed frequentist method by Lang and Reiczigel performed as desired, with the Bayesian method having a very slight advantage in terms of interval length. Conclusion: The Bayesian prevalence estimation method should be prefered over traditional frequentist methods. An acceptable alternative is to combine the Rogan-Gladen point estimate with the Lang-Reiczigel confidence interval.

Comparing Medical Care Costs using Bayesian Credible Intervals for the Ratio of Means of Delta-Lognormal Distributions

2020 ◽  
Vol 28 (Supp01) ◽  
pp. 51-68 ◽  
Author(s):  
Patcharee Maneerat ◽  
Sa-Aat Niwitpong
Keyword(s):  
Medical Care ◽  
Average Length ◽  
Credible Interval ◽  
Error Rates ◽  
Lognormal Distributions ◽  
Medical Care Costs ◽  
Bayesian Credible Interval ◽  
Credible Intervals ◽  
Variance Estimates ◽  
Care Costs

When considering the medical care costs data with a high proportion of zero items of two inpatient groups, comparing them can be estimated using confidence intervals for the ratio of the means of two delta-lognormal distributions. The Bayesian credible interval-based uniform-beta prior (BCIh-UB) is proposed and compared with the generalized confidence interval (GCI), fiducial GCI (FGCI), the method of variance estimates recovery (MOVER), BCIh based on Jeffreys’ rule prior (BCIh-JR), and BCIh based on the normal-gamma prior (BCIh-NG). The coverage probability (CP), average length (AL), and lower and upper error rates were the performance measures applied for assessing the methods through a Monte Carlo simulation. A numerical evaluation showed that BCIh-UB had much better CP and AL than the others even with a large difference between the variances and with a high proportion of zero. Finally, to illustrate the efficacy of BCIh-UB, the methods were applied to two sets of medical care costs data.

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