Main Article Content
Abstract
The Quasi-Least Squares (QLS) is useful for different correlation structure with attachment of Generalized Estimating Equation (GEE). The purpose of this work is to compare the regression parameter in the presence of different correlation structure with respect to GEE and QLS method. The comparison of estimated regression parameter has been performed in clinical trial data set; studying the effect of drug treatment (metformin with pioglitazone) Vs (gliclazide with pioglitazone) in type 2 diabetes patients. In case of QLS, the correlation coefficient of post-parandinal blood sugar (PPBS) under tridiagonal correlation is 0.008 while it failed to produce by GEE. It has been found that the combination of metformin with pioglitazone is more effective as compared to the combination of gliclazide with pioglitazone.
Keywords
MCMC
AR1
Exchangeable
Unstructured Correlation
Article Details
Authors who publish with this journal agree to the following License
CC BY: This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
How to Cite
Nath, D. C., & Bhattacharjee, A. (2011). Effect of Correlation Structure in Generalized Estimating Equation and Quasi Least Square: An Application in Type 2 Diabetes Patient. Pakistan Journal of Statistics and Operation Research, 7(2-Sp). https://doi.org/10.18187/pjsor.v7i2-Sp.305
