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Effect of Correlation Structure in Generalized Estimating Equation and Quasi Least Square: An Application in Type 2 Diabetes Patient

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Author(s): Dilip C Nath | Atanu Bhattacharjee

Journal: Pakistan Journal of Statistics and Operation Research
ISSN 1816-2711

Volume: 7;
Issue: 2-Sp;
Date: 2011;
Original page

Keywords: MCMC | AR1 | Exchangeable | Unstructured Correlation

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.
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