• Bayes’ Theorem
• Diagnostic process
• Philosophical interpretations of probability
• Pre-test probability value

• Humans
• Bayes Theorem
• Probability

• Bayesian modeling
• Cold/hot-spot detection
• Crime mapping
• Crime statistics

• Alzheimer’s disease
• Bayesian statistics
• clinical trial
• drug development

Linear mixed effects (LME) modelling under both frequentist and Bayesian frameworks can be used to study longitudinal trajectories. We studied the performance of both frameworks on different dataset configurations using hippocampal volumes from longitudinal MRI data across groups—healthy controls (HC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD) patients, including subjects that converted from MCI to AD. We started from a big database of 1250 subjects from the Alzheimer’s disease neuroimaging initiative (ADNI), and we created different reduced datasets simulating real-life situations using a random-removal permutation-based approach. The number of subjects needed to differentiate groups and to detect conversion to AD was 147 and 115 respectively. The Bayesian approach allowed estimating the LME model even with very sparse databases, with high number of missing points, which was not possible with the frequentist approach. Our results indicate that the frequentist approach is computationally simpler, but it fails in modelling data with high number of missing values.

• multiscale data science
• multiscale modeling
• systems medicine

• Health utility
• discrete choice experiment
• time trade-off
• valuation

We conducted a veterinary clinic-based retrospective cohort study aimed at identifying child-, dog-, and home-environment factors associated with dog bites to children aged 5–15 years old living in the same home as a dog in Kingston, Jamaica (236) and San Francisco, USA (61). Secondarily, we wished to compare these factors to risk factors for dog bites to the general public. Participant information was collected via interviewer-administered questionnaire using proxy respondents. Data were analyzed using log-binomial regression to estimate relative risks and associated 95% confidence intervals (CIs) for each exposure–dog bite relationship. Exploiting the correspondence between X% confidence intervals and X% Bayesian probability intervals obtained using a uniform prior distribution, for each exposure, we calculated probabilities of the true (population) RRs ≥ 1.25 or ≤0.8, for positive or negative associations, respectively. Boys and younger children were at higher risk for bites, than girls and older children, respectively. Dogs living in a home with no yard space were at an elevated risk (RR = 2.97; 95% CI: 1.06–8.33) of biting a child living in the same home, compared to dogs that had yard space. Dogs routinely allowed inside for some portion of the day (RR = 3.00; 95% CI: 0.94–9.62) and dogs routinely allowed to sleep in a family member’s bedroom (RR = 2.82; 95% CI: 1.17–6.81) were also more likely to bite a child living in the home than those that were not. In San Francisco, but less so in Kingston, bites were inversely associated with the number of children in the home. While in Kingston, but not in San Francisco, smaller breeds and dogs obtained for companionship were at higher risk for biting than larger breeds and dogs obtained for protection, respectively. Overall, for most exposures, the observed associations were consistent with population RRs of practical importance (i.e., RRs ≥ 1.25 or ≤0.8). Finally, we found substantial consistency between risk factors for bites to children and previously reported risk factors for general bites.

• anthrozoology
• child
• cohort study
• dog bite
• home
• human–animal interaction
• risk factor

• Acinetobacter baumannii
• Antimicrobials
• Critically ill patients
• Drug-resistant
• Network meta-analysis
• Pneumonia

• Acinetobacter baumannii/drug effects
• Acinetobacter baumannii/pathogenicity
• Anti-Infective Agents/pharmacology
• Anti-Infective Agents/therapeutic use
• Bayes Theorem
• Colistin/pharmacology
• Colistin/therapeutic use
• Critical Illness/therapy
• Drug Resistance, Bacterial/drug effects
• Humans
• Microbial Sensitivity Tests/methods

The objective of this study was to use probabilistic sensitivity analysis to evaluate the cost-effectiveness of using an on-farm culture (OFC) approach to the treatment of clinical mastitis in dairy cows and compare this to a ‘standard’ treatment approach. A specific aim was to identify the herd circumstances under which an OFC approach would be most likely to be cost-effective. A stochastic Monte Carlo model was developed to simulate 5000 cases of clinical mastitis at the cow level and to calculate the associated costs simultaneously when treated according to 2 different treatment protocols; i) a 'conventional' approach (3 tubes of intramammary antibiotic) and ii) an OFC programme, whereby cows are treated according to the results of OFC. Model parameters were taken from recent peer reviewed literature on the use of OFC prior to treatment of clinical mastitis. Spearman rank correlation coefficients were used to evaluate the relationships between model input values and the estimated difference in cost between the standard and OFC treatment protocols. The simulation analyses revealed that both the difference in the bacteriological cure rate due to a delay in treatment when using OFC and the proportion of Gram-positive cases that occur on a dairy unit would have a fundamental impact on whether OFC would be cost-effective. The results of this study illustrated that an OFC approach for the treatment of clinical mastitis would probably not be cost-effective in many circumstances, in particular, not those in which Gram-positive pathogens were responsible for more than 20% of all clinical cases. The results highlight an ethical dilemma surrounding reduced use of antimicrobials for clinical mastitis since it may be associated with financial losses and poorer cow welfare in many instances.

• Cost-effectiveness
• Mastitis
• On-farm culture
• Probabilistic sensitivity analysis
• Treatment

• Appalachia
• child
• decay probability
• family characteristics
• oral health
• smoking

• Bayesian
• Cost-effectiveness
• Dairy cow
• Decision making
• Dry-period
• Mastitis control

• Animals
• Bayes Theorem
• Cattle
• Computer Simulation
• Cost-Benefit Analysis
• Decision Making
• Female
• Lactation
• Mastitis, Bovine/economics
• Mastitis, Bovine/prevention & control
• Models, Statistical
• United Kingdom

• Bayesian methods
• causal inferences
• principal stratification
• vaccine effects

• Bayes Theorem
• Biometry/methods
• Causality
• Computer Simulation
• Humans
• Infection Control/statistics & numerical data
• Likelihood Functions
• Models, Statistical
• Observational Studies as Topic/statistics & numerical data
• Pertussis Vaccine/pharmacology
• Randomized Controlled Trials as Topic/statistics & numerical data
• Rotavirus Vaccines/pharmacology
• Selection Bias
• Treatment Outcome
• Vaccines/pharmacology

• R01 AI085073 NIAID NIH HHS

• Interpersonal and Communication Skills
• Medical Knowledge
• Professionalism
• peer-assisted learning
• reciprocal
• surgical education
• tutor

• Education, Medical, Undergraduate/methods
• Educational Measurement/statistics & numerical data
• Peer Group

Sustained clinical improvement is unlikely without appropriate measuring and reporting techniques. Clinical indicators are tools to help assess whether a standard of care is being met. They are used to evaluate the potential to improve the care provided by healthcare organisations (HCOs). The analysis and reporting of these indicators for the Australian Council on Healthcare Standards have used a methodology which estimates, for each of the 338 clinical indicators, the gains in the system that would result from shifting the mean proportion to the 20^th centile. The results are used to provide a relative measure to help prioritise quality improvement activity within clinical areas, rather than simply focus on “poorer performing” HCOs. The method draws attention to clinical areas exhibiting larger between-HCO variation and affecting larger numbers of patients. HCOs report data in six-month periods, resulting in estimated clinical indicator proportions which may be affected by small samples and sampling variation. Failing to address such issues would result in HCOs exhibiting extremely small and large estimated proportions and inflated estimates of the potential gains in the system. This paper describes the 20^th centile method of calculating potential gains for the healthcare system by using Bayesian hierarchical models and shrinkage estimators to correct for the effects of sampling variation, and provides an example case in Emergency Medicine as well as example expert commentary from colleges based upon the reports. The application of these Bayesian methods enables all collated data to be used, irrespective of an HCO’s size, and facilitates more realistic estimates of potential system gains.

• Clinical indicators
• Improvement
• System gains
• Bayesian
• Statistical models

• Bayesian meta-analysis
• Monte Carlo method, sensitivity and specificity
• diagnostic accuracy
• meta-analysis

• Biomedical Research/organization & administration
• Clinical Competence
• Evidence-Based Medicine/methods
• Female
• Humans
• Male
• Needs Assessment
• Physician's Role
• Practice Patterns, Physicians'/organization & administration
• Quality Control

• clinical trial
• cocaine dependence
• d-amphetamine
• modafinil

• prior distribution
• clinical decision making
• bayesian analysis
• mastitis

• Animals
• Attitude
• Bayes Theorem
• Cattle
• Culture
• England
• Female
• Humans
• Mastitis, Bovine/prevention & control
• Mastitis, Bovine/transmission
• Models, Econometric
• Probability
• Randomized Controlled Trials as Topic
• Veterinarians
• Veterinary Medicine/standards
• Wales

• 076998 Wellcome Trust

• Adolescent
• Adult
• Aged
• Aged, 80 and over
• Audiometry
• Bayes Theorem
• Diagnosis, Differential
• Female
• Hearing Loss, Sensorineural/diagnosis
• Hearing Loss, Sensorineural/etiology
• Humans
• Magnetic Resonance Imaging
• Male
• Mass Screening
• Middle Aged
• Neuroma, Acoustic/complications
• Neuroma, Acoustic/diagnosis
• Sensitivity and Specificity

• Adolescent
• Adult
• Aged
• Aged, 80 and over
• Child
• Child, Preschool
• Cluster Analysis
• Female
• France/epidemiology
• Humans
• Male
• Markov Chains
• Middle Aged
• Monte Carlo Method
• Urinary Bladder Neoplasms/epidemiology

• Bayes Theorem
• Data Interpretation, Statistical
• Health Services Research/statistics & numerical data
• Humans
• Philosophy, Medical
• Probability
• Research Design

• Bipolar Disorder/classification
• Bipolar Disorder/drug therapy
• Humans
• Meta-Analysis as Topic
• Observation

• MH-64189 NIMH NIH HHS

• Congenital Abnormalities/epidemiology
• Congenital Abnormalities/ethnology
• Congenital Abnormalities/etiology
• Female
• Fertilization in Vitro/adverse effects
• Humans
• Incidence
• Infant, Newborn
• Infertility
• Male
• Paternal Age

• Decision Making
• Delivery of Health Care/trends
• Evidence-Based Medicine
• Humans
• Meta-Analysis as Topic
• Practice Guidelines as Topic