scholarly journals Frequentist and Bayesian approaches to prevalence estimation using examples from Johne's disease

2008 ◽  
Vol 9 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Locksley L. McV. Messam ◽  
Adam J. Branscum ◽  
Michael T. Collins ◽  
Ian A. Gardner
Keyword(s):  
Bayesian Methods ◽  
Direct Computation ◽  
Bayesian Approaches ◽  
Interval Estimates ◽  
True Prevalence ◽  
Prevalence Estimation ◽  
Point Estimates ◽  
Population Prevalence ◽  
Small Test ◽  
Pooled Samples

AbstractAlthough frequentist approaches to prevalence estimation are simple to apply, there are circumstances where it is difficult to satisfy assumptions of asymptotic normality and nonsensical point estimates (greater than 1 or less than 0) may result. This is particularly true when sample sizes are small, test prevalences are low and imperfect sensitivity and specificity of diagnostic tests need to be incorporated into calculations of true prevalence. Bayesian approaches offer several advantages including direct computation of range-respecting interval estimates (e.g. intervals between 0 and 1 for prevalence) without the requirement of transformations or large-sample approximations. They also allow direct probabilistic interpretation, and the flexibility to model in a straightforward manner the probability of zero prevalence. In this review, we present frequentist and Bayesian methods for animal- and herd-level true prevalence estimation based on individual and pooled samples. We provide statistical methods for detecting differences between population prevalence and frequentist methods for sample size and power calculations. All examples are motivated usingMycobacterium aviumsubspeciesparatuberculosisinfection and we provide WinBUGS code for all examples of Bayesian estimation.

scholarly journals tPRiors: A tool for prior elicitation and obtaining posterior distributions of true disease prevalence

2021 ◽  
Author(s):  
Konstantinos Pateras
Keyword(s):  
Bayesian Methods ◽  
Diagnostic Tests ◽  
False Negative ◽  
Disease Prevalence ◽  
Final Report ◽  
Prior Elicitation ◽  
True Prevalence ◽  
Prevalence Estimation ◽  
Prevalence Estimates ◽  
Multiple Population

Abstract Background: Tests have false positive or false negative results, which, if not properly accounted for, may provide misleading apparent prevalence estimates based on the observed rate of positive tests and not the true disease prevalence estimates. Methods to estimate the true prevalence of disease, adjusting for the sensitivity and the specificity of the diagnostic tests are available and can be applied, though, such procedures can be cumbersome to researchers with or without a solid statistical background.Objective: To create a web-based application that integrates statistical methods for Bayesian inference of true disease prevalence based on prior elicitation for the accuracy of the diagnostic tests. This tool allows practitioners to simultaneously analyse and visualize results while using interactive sliders and output prior/posterior plots.Methods: Three methods for prevalence prior elicitation and four core families of Bayesian methods have been combined and incorporated in this web tool. |tPRiors| user interface has been developed with R and Shiny and may be freely accessed on-line.Results: |tPRiors| allows researchers to use preloaded data or upload their own datasets and perform analysis on either single or multiple population groups clusters), allowing, if needed, for excess zero prevalence. The final report is exported in raw parts either as .rdata or .png files. We utilize a real multiple-population and a toy single-population dataset to demonstrate the robustness and capabilities of |tPRiors|.Conclusions: We expect |tPRiors| to be helpful for researchers interested in true disease prevalence estimation and they are keen on accounting for prior information. |tPRiors| acts both as a statistical tool and a simplified step-by-step statistical framework that facilitates the use of complex Bayesian methods. The application of |tPRiors| is expected to aid standardization of practices in the field of Bayesian modelling on subject and multiple group-based true prevalence estimation.

CAN BAYESIAN METHODS MAKE DATA AND ANALYSES MORE RELEVANT TO DECISION MAKERS?

2001 ◽  
Vol 17 (1) ◽  
pp. 114-122 ◽  
Author(s):  
Steven H. Sheingold
Keyword(s):  
Decision Making ◽  
Bayesian Methods ◽  
Paradigm Shift ◽  
Relevant Information ◽  
Decision Makers ◽  
Evidence Based ◽  
Critical Importance ◽  
Bayesian Approaches ◽  
Bayesian Analyses ◽  
And Performance

Decision making in health care has become increasingly reliant on information technology, evidence-based processes, and performance measurement. It is therefore a time at which it is of critical importance to make data and analyses more relevant to decision makers. Those who support Bayesian approaches contend that their analyses provide more relevant information for decision making than do classical or “frequentist” methods, and that a paradigm shift to the former is long overdue. While formal Bayesian analyses may eventually play an important role in decision making, there are several obstacles to overcome if these methods are to gain acceptance in an environment dominated by frequentist approaches. Supporters of Bayesian statistics must find more accommodating approaches to making their case, especially in finding ways to make these methods more transparent and accessible. Moreover, they must better understand the decision-making environment they hope to influence. This paper discusses these issues and provides some suggestions for overcoming some of these barriers to greater acceptance.

scholarly journals Bayesian Metric Multidimensional Scaling

2013 ◽  
Vol 21 (1) ◽  
pp. 125-140 ◽  
Author(s):  
Ryan Bakker ◽  
Keith T. Poole
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Bayesian Methods ◽  
Optimal Solution ◽  
Full Rank ◽  
Mcmc Methods ◽  
Ratio Scale ◽  
Posterior Distributions ◽  
Point Estimates ◽  
Inflection Points

In this article, we show how to apply Bayesian methods to noisy ratio scale distances for both the classical similarities problem as well as the unfolding problem. Bayesian methods produce essentially the same point estimates as the classical methods, but are superior in that they provide more accurate measures of uncertainty in the data. Identification is nontrivial for this class of problems because a configuration of points that reproduces the distances is identified only up to a choice of origin, angles of rotation, and sign flips on the dimensions. We prove that fixing the origin and rotation is sufficient to identify a configuration in the sense that the corresponding maxima/minima are inflection points with full-rank Hessians. However, an unavoidable result is multiple posterior distributions that are mirror images of one another. This poses a problem for Markov chain Monte Carlo (MCMC) methods. The approach we take is to find the optimal solution using standard optimizers. The configuration of points from the optimizers is then used to isolate a single Bayesian posterior that can then be easily analyzed with standard MCMC methods.

scholarly journals Bayesian Approaches to Subgroup Analysis and Related Adaptive Clinical Trial Designs

2019 ◽  
pp. 1-9
Author(s):  
Ciara Nugent ◽  
Wentian Guo ◽  
Peter Müller ◽  
Yuan Ji
Keyword(s):  
Clinical Trial ◽  
Bayesian Methods ◽  
Subgroup Analysis ◽  
Clinical Trial Design ◽  
Bayesian Decision ◽  
Bayesian Approaches ◽  
Alternative Approaches ◽  
Clinical Trial Designs ◽  
Selection Of

We review Bayesian and Bayesian decision theoretic approaches to subgroup analysis and applications to subgroup-based adaptive clinical trial designs. Subgroup analysis refers to inference about subpopulations with significantly distinct treatment effects. The discussion mainly focuses on inference for a benefiting subpopulation, that is, a characterization of a group of patients who benefit from the treatment under consideration more than the overall population. We introduce alternative approaches and demonstrate them with a small simulation study. Then, we turn to clinical trial designs. When the selection of the interesting subpopulation is carried out as the trial proceeds, the design becomes an adaptive clinical trial design, using subgroup analysis to inform the randomization and assignment of treatments to patients. We briefly review some related designs. There are a variety of approaches to Bayesian subgroup analysis. Practitioners should consider the type of subpopulations in which they are interested and choose their methods accordingly. We demonstrate how subgroup analysis can be carried out by different Bayesian methods and discuss how they identify slightly different subpopulations.

Objective Inference for Climate Parameters: Bayesian, Transformation-of-Variables, and Profile Likelihood Approaches

2014 ◽  
Vol 27 (19) ◽  
pp. 7270-7284 ◽  
Author(s):  
Nicholas Lewis
Keyword(s):  
Climate Sensitivity ◽  
Bayesian Methods ◽  
Likelihood Function ◽  
Profile Likelihood ◽  
Likelihood Method ◽  
Jeffreys Prior ◽  
Bayesian Approaches ◽  
Noninformative Priors ◽  
Likelihood Functions ◽  
Transformation Of Variables

Abstract Insight is provided into the use of objective-Bayesian methods for estimating climate sensitivity by considering their relationship to transformations of variables in the context of a simple case considered in a previous study, and some misunderstandings about Bayesian inference are discussed. A simple model in which climate sensitivity (S) and effective ocean heat diffusivity (Kυ) are the only parameters varied is used, with twentieth-century warming attributable to greenhouse gases (AW) and effective ocean heat capacity (HC) being the only data-based observables. Probability density functions (PDFs) for AW and HC are readily derived that represent valid independent objective-Bayesian posterior PDFs, provided the error distribution assumptions involved in their construction are justified. Using them, a standard transformation of variables provides an objective joint posterior PDF for S and Kυ; integrating out Kυ gives a marginal PDF for S. Close parametric approximations to the PDFs for AW and HC are obtained, enabling derivation of likelihood functions and related noninformative priors that give rise to the objective posterior PDFs that were computed initially. Bayes’s theorem is applied to the derived AW and HC likelihood functions, demonstrating the effect of differing prior distributions on PDFs for S. Use of the noninformative Jeffreys prior produces an identical PDF to that derived using the transformation-of-variables approach. It is shown that similar inference for S to that based on these two alternative objective-Bayesian approaches is obtained using a profile likelihood method on the derived joint likelihood function for AW and HC.

scholarly journals A primer on Bayesian estimation of prevalence of COVID-19 patient outcomes

JAMIA Open ◽  
2020 ◽  
Author(s):  
Xiang Gao ◽  
Qunfeng Dong
Keyword(s):  
Patient Outcomes ◽  
Posterior Probability ◽  
The United States ◽  
Data Sets ◽  
Medical Outcomes ◽  
Posterior Probability Distribution ◽  
Prevalence Estimation ◽  
Point Estimates ◽  
Asymptomatic Children ◽  
Research Task

Abstract A common research task in COVID-19 studies often involves the prevalence estimation of certain medical outcomes. Although point estimates with confidence intervals are typically obtained, a better approach is to estimate the entire posterior probability distribution of the prevalence, which can be easily accomplished with a standard Bayesian approach using binomial likelihood and its conjugate beta prior distribution. Using two recently published COVID-19 data sets, we performed Bayesian analysis to estimate the prevalence of infection fatality in Iceland and asymptomatic children in the United States.

Diagnosis, management and prevalence estimation of gonorrhoea: influences of Aptima Combo 2 assay with alternative target confirmation

2009 ◽  
Vol 20 (5) ◽  
pp. 315-319 ◽  
Author(s):  
M Mahto ◽  
S Zia ◽  
D Ritchie ◽  
H Mallinson
Keyword(s):  
Chlamydia Trachomatis ◽  
Laboratory Data ◽  
Standard Of Care ◽  
Substantial Contribution ◽  
Sexually Transmitted ◽  
True Prevalence ◽  
Medicine Clinic ◽  
Prevalence Estimation ◽  
M 10 ◽  
Primary Test

Case-notes and laboratory data were retrospectively reviewed for influences of dual testing by Aptima Combo 2 (AC2) for Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) on the diagnosis, management and prevalence estimation of gonorrhoea in the genitourinary (GU) medicine clinic and community. NG positives by AC2 were confirmed by Aptima Gonococcus assay. Unconfirmed positives were rare. Our study showed that in the GU medicine clinic, AC2 detected about 20% extra cases of NG beyond culture. For best standard of care, NG culture and microscopy are still required in some patients to ensure that treatment is rapid and appropriate. Compared to self-referral at the GU medicine clinic, community tests made a substantial contribution to the overall number of NG cases found (40 community versus 35 Macclesfield GU medicine clinic). The ratio of female to male NG cases found was significantly higher ( P = 0.002) in the community (13 M, 27 F) than at the GU medicine clinic (25 M, 10 F). In the community, over 60% of NG infections occurred in chlamydia-negative patients. The overall prevalence of NG in the GU medicine clinic was 1.3%, the true prevalence being much lower at 0.9% on primary test. Prevalence in the community was 0.4%. Data from dual testing in the community can clarify NG prevalence beyond the existing KC60 (sexually transmitted infections) reports.

scholarly journals Three-year monitoring of paratuberculosis in dairy cattle by pooled faecal culture and individual prevalence estimation

2018 ◽  
Vol 18 (1) ◽  
pp. 99-111
Author(s):  
Alberto Prieto ◽  
José Manuel Díaz-Cao ◽  
Pablo Díaz ◽  
Ana Pérez-Creo ◽  
Gonzalo López-Lorenzo ◽  
...  
Keyword(s):  
First Year ◽  
Dairy Herds ◽  
Bayesian Approaches ◽  
Faecal Culture ◽  
Control Programmes ◽  
Second Year ◽  
Mycobacterium Avium Subsp Paratuberculosis ◽  
Pooled Samples ◽  
Culture Positive

Abstract The objective of this study was to assess the implementation of a three-year Mycobacterium avium subsp. paratuberculosis monitoring programme using pooled faecal culture in small and mediumsized dairy herds to classify them as infected or non-infected and apply proper hygiene and biosecurity measures. Over a three-year period, 35 dairy herds were analysed annually by faecal culture of ten pooled samples. In addition, proper hygiene and biosecurity protocols were implemented in the farms after the first testing round. Considering a herd as infected with at least one culture positive in any of the three years, the accumulated percentage of infected herds was 25.7%, 40% and 45.7%, for each year respectively. Assuming that all infected herds had been detected at the end of the study, the percentage of infected herds detected each year was 56.25% and 87.5% for the first and second year, respectively. Using frequentist and Bayesian approaches, the estimated individual prevalence revealed a downward trend from 3.30-3.65% in the first year to 1.66-1.86% in the third year. The results of this study indicate that pooled faecal culture allowed for proper classification of the herds and can be a useful tool for monitoring dairy herds against paratuberculosis. In addition, statistical analysis of pooled faecal culture results can be used to evaluate the evolution of individual prevalence in the population and therefore the function of the implemented control programmes.

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