Vaka-Kontrol Çalışmalarında Uzun Süreli Verilere Sahip Çok-Yönlü Çapraz Tablolar için Genelleştirilmiş Tahmin Denklemleri Yaklaşımı

Abstract

Objective: Log-linear analysis is a classical statistical method for the analysis of association between variables in multi-way contingency tables. Generalized Estimating Equations (GEEs) approach is popular especially for analyzing longitudinal data, since it enables to take into account the correlation of repeated measures over time within subjects by defining a so-called ''working correlation structure''. GEEs provide consistent regression parameter estimates even if working correlation structure is misspecified. In this paper, we suggest GEEs approach to the analysis of a multi-way contingency table with longitudinal data, which consists of more than one contingency tables obtained over time in case-control studies and examine the method of GEEs by considering four different working correlation structures for correlations between longitudinal count data in the table. Material and Methods: Log-linear analysis and GEEs method for longitudinal data in the multi-way contingency table with time factor are performed by SAS 9.4 statistical software program. A real genetic association case-control study with longitudinal data was illustrated to compare both methods. Results: Using either the classical log-linear analysis or GEEs method with an independent (IND) working correlation structure generates similar results for parameter estimates. It is found that linear model fitting longitudinal data in the multi-way contingency table is observed to be same for both log-linear analysis and GEEs approach performed under all correlation structures. Conclusion: This study indicates that GEEs approach provides more efficient and unbiased regression parameter estimates for the multi-way contingency table designed by responses measured repeatedly over time in the case-control study.