Biomaterial Database

Modelling covariate effects in observer agreement studies: the case of nominal scale agreement. 1995

P Graham

Department of Public Health and General Practice, Christchurch School of Medicine, New Zealand.

Correction for chance-expected agreement has become an accepted technique in the analysis of observer agreement data and may be particularly useful when the level of agreement achieved in different populations is compared. However, formal methods for making comparisons of chance-corrected agreement or, more generally, for studying the effects of covariates on chance-corrected agreement have not received much attention. For nominal scale agreement data we show how Tanner and Young's model for observer agreement can be applied to this problem. The models discussed can be fitted using existing software and certain model parameters have interpretations in terms of positive and negative agreement odds ratios. The proposed methodology facilitates investigation of issues such as confounding of covariate effects and interaction between covariates in their effect on chance-corrected agreement. The methods outlined therefore allow observer agreement data to be analyzed in a manner strongly analogous to the logistic modelling of the association between disease and suspected risk factors. The methods are illustrated using data on the comparability of primary and proxy respondent reports of the primary respondents participation in physically vigorous leisure time activity.

UI	MeSH Term	Description	Entries
D007899	Leisure Activities	Voluntary use of free time for activities outside the daily routine.	Leisure,Activities, Leisure,Activity, Leisure,Leisure Activity,Leisures
D008297	Male		Males
D008875	Middle Aged	An adult aged 45 - 64 years.	Middle Age
D003327	Coronary Disease	An imbalance between myocardial functional requirements and the capacity of the CORONARY VESSELS to supply sufficient blood flow. It is a form of MYOCARDIAL ISCHEMIA (insufficient blood supply to the heart muscle) caused by a decreased capacity of the coronary vessels.	Coronary Heart Disease,Coronary Diseases,Coronary Heart Diseases,Disease, Coronary,Disease, Coronary Heart,Diseases, Coronary,Diseases, Coronary Heart,Heart Disease, Coronary,Heart Diseases, Coronary
D005260	Female		Females
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000704	Analysis of Variance	A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.	ANOVA,Analysis, Variance,Variance Analysis,Analyses, Variance,Variance Analyses
D012680	Sensitivity and Specificity	Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)	Specificity,Sensitivity,Specificity and Sensitivity
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