Abstract
The Sri Lankan Journal of Applied Statistics(SLJAS) is an open-access, international, double-blind peer-reviewed journal published by the Institute of Applied Statistics, Sri Lanka (IASSL). The main purpose of the journal is to publish the results of original work on applications of Statistics and on theoretical and methodical aspects of Statistics. The journal also welcomes critical reviews including conceptual discussions, opinions and book reviews. Applications of Statistics in the area of Agriculture & Forestry, Medical, Dental and Veterinary Sciences, Natural, Physical Sciences, Social Sciences, Economics and Actuarial Science fall within the scope of the journal. This journal does not charge any fee for article processing and submission.
Highlights
Multilevel or Hierarchical data are a commonly encountered phenomenon, in many data structures, especially in the fields of Medical, Biological and Social Sciences
Statistical modeling of multilevel data has been in discussion for many years and many developments have been made in this aspect Aitkin et al [1], Goldstein [2], Hedeker and Gibbons [3]
As most of the early developments are concentrated in the area of continuous response variables, the field of multilevel modeling for discrete categorical responses is a relatively new approach Goldstein [4], Rashbash et al [5], Fielding and Yang [6]
Summary
Multilevel or Hierarchical data are a commonly encountered phenomenon, in many data structures, especially in the fields of Medical, Biological and Social Sciences. Multilevel models for ordered categorical responses, which is somewhat of a recent development in this field, tries to avoid the arbitrariness of assumptions involved when assigning these numerical scores through the use of Cumulative Response Probabilities in place of response probabilities for each category. Fielding et al [9] presents an application of this method to an educational dataset This model is somewhat of a novel application in the field of medical data analysis as it has not yet been frequently utilized for the modeling of medical data. The main focus of this paper is to present the application of Generalized Linear Multilevel Models ( referred to as Generalized Multilevel Ordinal Models), for analysing multilevel ordinal categorical responses, in the field of medicine
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