• Association study
• Augmented estimation
• Electronic health records
• Manual chart review
• Outcome-dependent sampling
• Phenotyping error

• Electronic Health Records
• Humans
• Phenotype
• Algorithms
• Alzheimer Disease
• Female

• Bayesian latent class models
• National Health and Nutrition Examination Survey
• PHQ‐9
• depression prevalence

• Humans
• Nutrition Surveys/statistics & numerical data
• Prevalence
• Adult
• Female
• Male
• Middle Aged
• United States/epidemiology
• Depressive Disorder, Major/epidemiology
• Depressive Disorder, Major/diagnosis
• Young Adult
• Psychiatric Status Rating Scales/standards
• Sensitivity and Specificity
• Aged
• Bayes Theorem
• Adolescent

• E-Cost-CA18208-93c7fb4 European Cooperation in Science and Technology
• 530401393 Deutsche Forschungsgemeinschaft (DFG)
• European Cooperation in Science and Technology

• Bayesian latent class model
• Multilevel
• Sensitivity
• Simulation
• Specificity

• adult psychiatry
• depression & mood disorders

• Humans
• Depressive Disorder, Major/diagnosis
• Patient Health Questionnaire
• Prevalence
• Cross-Sectional Studies
• Bayes Theorem
• Europe/epidemiology

• Attraction
• Emotion detection
• Speed-dating
• Third-party observer
• Zero-acquaintance

• Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Clinical scores are practical tools that can be used in the daily management of cattle. Score building and validation are a challenge involving various methodological and statistical issues. This article provides a specific framework for clinical score building where the target condition can be assessed directly or indirectly. Practical examples are given throughout the manuscript in order to build new scores or to assess score robustness.

Clinical scores are commonly used for cattle. They generally contain a mix of categorical and numerical variables that need to be assessed by scorers, such as farmers, animal caretakers, scientists, and veterinarians. This article examines the key concepts that need to be accounted for when developing the test for optimal outcomes. First, the target condition or construct that the scale is supposed to measure should be defined, and if possible, an adequate proxy used for classification should be determined. Then, items (e.g., clinical signs) of interest that are either caused by the target condition (reflective items) or that caused the target condition (formative items) are listed, and reliable items (inter and intra-rater reliability) are kept for the next step. A model is then developed to determine the relative weight of the items associated with the target condition. A scale is then built after validating the model and determining the optimal threshold in terms of sensitivity (ability to detect the target condition) and specificity (ability to detect the absence of the target condition). Its robustness to various scenarios of the target condition prevalence and the impact of the relative cost of false negatives to false positives can also be assessed to tailor the scale used based on specific application conditions.

• cattle
• clinical scores
• methodological approach

• Attraction
• Error Management Theory
• Sexual overperception bias
• Social perception
• Speed-dating

• Arousal
• Emotions
• Female
• Humans
• Libido
• Male
• Sexual Behavior
• Sexual Partners

• Nederlandse Organisatie voor Wetenschappelijk Onderzoek
• H2020 European Research Council
• Templeton World Charity Foundation (the Diverse Intelligences Possibilities Fund)

• Berkson error
• SIMEX
• classical error
• differential error
• measurement error
• misclassification
• nondifferential error
• regression calibration
• sample size
• simulation extrapolation

• Bias
• Calibration
• Causality
• Computer Simulation
• Humans
• Models, Statistical
• Research Design

• U01 CA057030 NCI NIH HHS
• R01 AI131771 NIAID NIH HHS
• P30 CA012197 NCI NIH HHS
• MR/M014827/1 Medical Research Council
• R37 AI131771 NIAID NIH HHS
• MR/S017968/1 Medical Research Council

• Accuracy
• Bronchopneumonia
• Diagnosis
• Gold standard

• association study
• bias reduction
• differential misclassification
• electronic health records
• error in phenotype

• computational algebraic geometry
• latent class models
• polynomial equations
• survey sampling

• Bayesian Analysis
• Gastric Cancer
• Incidence

• Adolescent
• Adult
• Aged
• Bayes Theorem
• Child
• Child, Preschool
• Colorectal Neoplasms/epidemiology
• Female
• Humans
• Incidence
• Infant
• Infant, Newborn
• Iran/epidemiology
• Male
• Middle Aged
• Models, Biological
• Registries

• Bayesian
• external validation
• misclassification
• multiple imputation
• odds ratio
• pharmacoepidemiology
• venous thromboembolism

• Analysis
• Animal disease
• Design
• Quality standards
• Reporting
• Sensitivity
• Specificity
• Test accuracy

• dairy cow
• incomplete milking
• mastitis
• reproductive tract disease

Using imperfect tests may lead to biased estimates of disease frequency and measures of association. Many studies have looked into the effect of misclassification on statistical inferences. These evaluations were either within a cross-sectional study framework, assessing biased prevalence, or for cohort study designs, evaluating biased incidence rate or risk ratio estimates based on misclassification at one of the two time-points (initial assessment or follow-up). However, both observations at risk and incident cases can be wrongly identified in longitudinal studies, leading to selection and misclassification biases, respectively. The objective of this paper was to evaluate the relative impact of selection and misclassification biases resulting from misclassification, together, on measures of incidence and risk ratio. To investigate impact on measure of disease frequency, data sets from a hypothetical cohort study with two samples collected one month apart were simulated and analyzed based on specific test and disease characteristics, with no elimination of disease during the sampling interval or clustering of observations. Direction and magnitude of bias due to selection, misclassification, and total bias was assessed for diagnostic test sensitivity and specificity ranging from 0.7 to 1.0 and 0.8 to 1.0, respectively, and for specific disease contexts, i.e., disease prevalences of 5 and 20%, and disease incidences of 0.01, 0.05, and 0.1 cases/animal-month. A hypothetical exposure with known strength of association was also generated. A total of 1,000 cohort studies of 1,000 observations each were simulated for these six disease contexts where the same diagnostic test was used to identify observations at risk at beginning of the cohort and incident cases at its end. Our results indicated that the departure of the estimates of disease incidence and risk ratio from their true value were mainly a function of test specificity, and disease prevalence and incidence. The combination of the two biases, at baseline and follow-up, revealed the importance of a good to excellent specificity relative to sensitivity for the diagnostic test. Small divergence from perfect specificity extended quickly to disease incidence over-estimation as true prevalence increased and true incidence decreased. A highly sensitive test to exclude diseased subjects at baseline was of less importance to minimize bias than using a highly specific one at baseline. Near perfect diagnostic test attributes were even more important to obtain a measure of association close to the true risk ratio, according to specific disease characteristics, especially its prevalence. Low prevalent and high incident disease lead to minimal bias if disease is diagnosed with high sensitivity and close to perfect specificity at baseline and follow-up. For more prevalent diseases we observed large risk ratio biases towards the null value, even with near perfect diagnosis.

• bias (epidemiology)
• epidemiologic methods
• longitudinal study
• misclassification
• selection bias

•

A prediction rule using thoracic ultrasound as an imperfect test for BRD diagnosis was modeled.

Bovine respiratory disease complex is a major cause of illness in dairy calves. The diagnosis of active infection of the lower respiratory tract is challenging on daily basis in the absence of accurate clinical signs. Clinical scoring systems such as the Californian scoring system, are appealing but were developed without considering the imperfection of reference standard tests used for case definition. This study used a Bayesian latent class model to update Californian prediction rules. The results of clinical examination and ultrasound findings of 608 preweaned dairy calves were used. A model accounting for imperfect accuracy of thoracic ultrasound examination was used to obtain updated weights for the clinical signs included in the Californian scoring system. There were 20 points (95% Bayesian credible intervals: 11–29) for abnormal breathing pattern, 16 points (95% BCI: 4–29) for ear drop/head tilt, 16 points (95% BCI: 9–25) for cough, 10 points (95% BCI: 3–18) for the presence of nasal discharge, 7 points (95% BCI: −1 to 8) for rectal temperature ≥39.2 °C, and −1 points (95% BCI: −9 to 8) for the presence of ocular discharge. The optimal cut-offs were determined using the misclassification cost-term term (MCT) approach with different possible scenarios of expected prevalence and different plausible ratio of false negative costs/false positive costs. The predicted probabilities of active infection of the lower respiratory tract were also obtained using posterior densities of the main logistic regression model. Depending on the context, cut-off varying from 9 to 16 can minimized the MCT. The optimal cut-off decreased when expected prevalence of disease and false negative/false positive ratio increased.

• Accuracy
• Bayesian
• Latent class
• Pneumonia

• Agapornis roseicollis
• Chlamydia psittaci
• Columba livia
• PCV-1
• PCV-2
• Rock Dove
• Rosy-faced Lovebird
• psittacine circovirus

• Bayesian method
• health administrative databases
• matched case-control study
• odds ratio
• outcome misclassification

• Bias
• Dairy cow
• Epidemiologic methods
• Misclassification
• mastitis

• Bayesian correction
• Cancer incidence registry
• Misclassification
• Trend of hepatocellular carcinoma

• coagulase-negative staphylococci
• dairy
• intramammary infection
• mastitis
• prevalence

• Staphylococcus aureus
• coagulase-negative staphylococci
• mastitis
• non-aureus staphylococci
• somatic cell count

• Bayesian method
• Cause of death
• Gastric cancer
• Iran
• Misclassification

• ROC analysis
• electronic health records
• misclassification
• precision medicine
• risk prediction modeling

• Bayesian correction
• Cancer incidence registry
• Gastric cancer
• Iran
• Misclassification

• Obstetric fistula
• rectovaginal fistula
• reproductive health
• sexual health
• vesicovaginal fistula
• women’s health

• Adolescent
• Adult
• Africa South of the Sahara/epidemiology
• Bayes Theorem
• Circumcision, Female
• Educational Status
• Family Characteristics
• Female
• Health Surveys
• Humans
• Logistic Models
• Middle Aged
• Odds Ratio
• Pregnancy
• Pregnancy Complications/epidemiology
• Pregnancy Complications/etiology
• Risk Factors
• Sex Offenses
• Vaginal Fistula/epidemiology
• Vaginal Fistula/etiology
• Young Adult

• Canadian Institutes of Health Research

We evaluated the cost-effectiveness of combined single session brief behavioral intervention, either didactic or interactive (Mujer Mas Segura, MMS) to promote safer-sex and safer-injection practices among female sex workers who inject drugs (FSW-IDUs) in Tijuana (TJ) and Ciudad-Juarez (CJ) Mexico. Data for this analysis was obtained from a factorial RCT in 2008–2010 coinciding with expansion of needle exchange programs (NEP) in TJ, but not in CJ.

A Markov model was developed to estimate the incremental cost per quality adjusted life year gained (QALY) over a lifetime time frame among a hypothetical cohort of 1,000 FSW-IDUs comparing a less intensive didactic vs. a more intensive interactive format of the MMS, separately for safer sex and safer injection combined behavioral interventions. The costs for antiretroviral therapy was not included in the model. We applied a societal perspective, a discount rate of 3% per year and currency adjusted to US$2014. A multivariate sensitivity analysis was performed. The combined and individual components of the MMS interactive behavioral intervention were compared with the didactic formats by calculating the incremental cost-effectiveness ratios (ICER), defined as incremental unit of cost per additional health benefit (e.g., HIV/STI cases averted, QALYs) compared to the next least costly strategy. Following guidelines from the World Health Organization, a combined strategy was considered highly cost-effective if the incremental cost per QALY gained fell below the gross domestic product per capita (GDP) in Mexico (equivalent to US$10,300).

For CJ, the mixed intervention approach of interactive safer sex/didactic safer injection had an incremental cost-effectiveness ratio (ICER) of US$4,360 ($310–$7,200) per QALY gained compared with a dually didactic strategy. Using the dually interactive strategy had an ICER of US$5,874 ($310–$7,200) compared with the mixed approach. For TJ, the combination of interactive safer sex/didactic safer injection had an ICER of US$5,921 ($104–$9,500) per QALY compared with dually didactic. Strategies using the interactive safe injection intervention were dominated due to lack of efficacy advantage. The multivariate sensitivity analysis showed a 95% certainty that in both CJ and TJ the ICER for the mixed approach (interactive safer sex didactic safer injection intervention) was less than the GDP per capita for Mexico. The dual interactive approach met this threshold consistently in CJ, but not in TJ.

In the absence of an expanded NEP in CJ, the combined-interactive formats of the MMS behavioral intervention is highly cost-effective. In contrast, in TJ where NEP expansion suggests that improved access to sterile syringes significantly reduced injection-related risks, the interactive safer-sex combined didactic safer-injection was highly cost-effective compared with the combined didactic versions of the safer-sex and safer-injection formats of the MMS, with no added benefit from the interactive safer-injection component.

• CD4 Lymphocyte Count
• Cities
• Cost-Benefit Analysis
• Dangerous Behavior
• Drug Users
• Female
• HIV Infections/epidemiology
• HIV Infections/immunology
• HIV Infections/mortality
• HIV Infections/transmission
• Humans
• Mexico
• Models, Statistical
• Public Health Surveillance
• Quality of Life
• Risk Reduction Behavior
• Sex Workers
• Sexual Behavior

• R01DA023877-S1 NIDA NIH HHS
• K01 MH095680 NIMH NIH HHS
• R01 DA023877 NIDA NIH HHS
• R01 MH065849 NIMH NIH HHS
• R01DA023877 NIDA NIH HHS
• R01 DA015612 NIDA NIH HHS
• R37 DA015612 NIDA NIH HHS

• Alzheimer's disease
• Bayesian inference
• Misclassification
• latent class model
• sensitivity
• specificity

• Alzheimer Disease/psychology
• Bayes Theorem
• Bias
• Binomial Distribution
• Biostatistics
• Computer Simulation
• Disease Progression
• Humans
• Likelihood Functions
• Models, Statistical
• Regression Analysis

• U01 NS043127 NINDS NIH HHS
• U01 NS043128 NINDS NIH HHS
• U01NS43128 NINDS NIH HHS

• Hierarchical spatial modelling
• Malawi
• Misclassification error
• Pneumonia

Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue.

A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon.

A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression.

Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.

The online version of this article (doi:10.1186/s12936-015-0966-y) contains supplementary material, which is available to authorized users.

• Bayesian
• Bovine
• Lactate
• Metaphylaxis
• Misclassification cost-term
• Pneumonia

• Bias
• Differential misclassification
• Nonlinear mixed model
• Validation

• case–control study
• misclassification of outcome
• risk factors for prostate cancer

• Bayesian logistic regression
• Conditional autoregressive models
• Misclassification
• Slice sampling
• Spatial correlation