Biomaterial Database

Using replicate observations in observer agreement studies with binary assessments. 1991

S G Baker, and L S Freedman, and M K Parmar

Biometry Branch, DCPC, National Cancer Institute, Bethesda, Maryland 20892.

By introducing replicate observations into observer agreement studies, one can obtain better measures of observer agreement than heretofore possible. New methodology based on the analysis of latent variables allows a separation of within- and between-observer variation for binary measures of assessment among pairs of observers. Maximum likelihood estimation and hypothesis testing are discussed. The methodology is illustrated using data on the assessment of dysplasia by pathologists.

UI	MeSH Term	Description	Entries
D011379	Prognosis	A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations.	Prognostic Factor,Prognostic Factors,Factor, Prognostic,Factors, Prognostic,Prognoses
D001749	Urinary Bladder Neoplasms	Tumors or cancer of the URINARY BLADDER.	Bladder Cancer,Bladder Neoplasms,Cancer of Bladder,Bladder Tumors,Cancer of the Bladder,Malignant Tumor of Urinary Bladder,Neoplasms, Bladder,Urinary Bladder Cancer,Bladder Cancers,Bladder Neoplasm,Bladder Tumor,Cancer, Bladder,Cancer, Urinary Bladder,Neoplasm, Bladder,Neoplasm, Urinary Bladder,Tumor, Bladder,Tumors, Bladder,Urinary Bladder Neoplasm
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D001699	Biometry	The use of statistical and mathematical methods to analyze biological observations and phenomena.	Biometric Analysis,Biometrics,Analyses, Biometric,Analysis, Biometric,Biometric Analyses
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D015588	Observer Variation	The failure by the observer to measure or identify a phenomenon accurately, which results in an error. Sources for this may be due to the observer's missing an abnormality, or to faulty technique resulting in incorrect test measurement, or to misinterpretation of the data. Two varieties are inter-observer variation (the amount observers vary from one another when reporting on the same material) and intra-observer variation (the amount one observer varies between observations when reporting more than once on the same material).	Bias, Observer,Interobserver Variation,Intraobserver Variation,Observer Bias,Inter-Observer Variability,Inter-Observer Variation,Interobserver Variability,Intra-Observer Variability,Intra-Observer Variation,Intraobserver Variability,Inter Observer Variability,Inter Observer Variation,Inter-Observer Variabilities,Inter-Observer Variations,Interobserver Variabilities,Interobserver Variations,Intra Observer Variability,Intra Observer Variation,Intra-Observer Variabilities,Intra-Observer Variations,Intraobserver Variabilities,Intraobserver Variations,Observer Variations,Variabilities, Inter-Observer,Variabilities, Interobserver,Variabilities, Intra-Observer,Variabilities, Intraobserver,Variability, Inter-Observer,Variability, Interobserver,Variability, Intra-Observer,Variability, Intraobserver,Variation, Inter-Observer,Variation, Interobserver,Variation, Intra-Observer,Variation, Intraobserver,Variation, Observer,Variations, Inter-Observer,Variations, Interobserver,Variations, Intra-Observer,Variations, Intraobserver,Variations, Observer
D016013	Likelihood Functions	Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.	Likelihood Ratio Test,Maximum Likelihood Estimates,Estimate, Maximum Likelihood,Estimates, Maximum Likelihood,Function, Likelihood,Functions, Likelihood,Likelihood Function,Maximum Likelihood Estimate,Test, Likelihood Ratio