Statistical Tool for Testing Agreement Level on Continuous Datasets

Abstract

Various analytical studies explored the new innovation for testing agreement level in medical and life sciences which can be simulated by Cohen ‘κ’ based on the practical applications. From the past medical literature, many authors suggested that, there is some disproportion research gaps that exists in the statistical methods for measuring the agreements between two or more observers from ‘Cohen ‘κ’,  these methods had some salient properties and analytical characteristics on qualitative data for testing the research hypothetical statements. The ‘k’ can be simulated based on few parameters, which can be estimated from the observed data sets at one point of time (t). This intervention will be restricted for the experimenter on measuring and comparing the extent of various agreements at varied time intervals t'. In this drawback, the present research article attempts to focus on the testing agreement level based on real values by using various mathematical iterations like bootstrap and Thompson (the measurement made on central tendency method). Since above cited methods extrapolate prediction values and Standard Errors (SE) on various agreements with continuous data scale. As per the model results, our formulated model will be able to measure and compare various parameters of our interest, we can also estimate various parameters from agreements between two or more observers by using ranking scale (converted in to random scale of measurement) at the same population in varied time interval ‘t'. The research findings clearly depend heavily on the exact distribution of Binomial and Poisson distribution with same dicatamous classification of the disease conditions. Results of bootstrap technique are more epoch rather than ‘k’ and it will provide a very good consistent prediction of different observer agreement level without any biased scale. This model also demonstrated how to examine various kinds of distributions at population level. Importantly, the driven model will explore fiducial limits of the parameters on the basis of agreement drawn with different time intervals t₁, t₂... t_n (by using real-life datasets from anti-leprosy vaccine trial conducted in south India). We found that, the model results would be coaxial changes between various factors viz (i) distribution of the sample estimators is non-Gaussianity, (ii) variance is underestimated and the confidence limits are asymmetric normally distributed data sets.