This is my Ph.D thesis research with my advisor Prof. Hedayat and co-advisor Dr Lawrence Lin.
Study of measuring agreement is mainly aimed to answer one question, whether the readings from one instrument/method agree with the those from another instrument/method. In our study, we are going to present a general method to assess agreement for continuous and categorical data with repeated measurements using linear and generalized linear mixed models. Likelihood-based approaches are developed to estimate all the within- and between-instrument agreement statistics, and asymptotic properties of these agreement estimates are discussed for different data structures. Furthermore, our method has the merit of handling missing values and covariates naturally. And a new set of restricted agreement statistics is proposed in order to capture the true random variations and between-instrument effects rather than the covariate effects. Simulations and several case studies, involving method comparison and bioequivalence, are used to show the accuracy and effectiveness of our method.
My seminar talk in UIC: Slides
R package for computing agreement statistics: Agreement
– Completion: 80%
Agreement for continuous data;
Agreement for categorical data;
Unified Agreement for both continuous and categorical data;
Comparative Agreement for both continuous and categorical data;
Output summary table and figure to html files.
– To do:
Agreement for covariates;
Sample size calculation;
Leave a comment