MODELLING REPEATED MEASURES DOSE-RESPONSE MORTALITY DATA USING GENERALIZED ESTIMATING EQUATIONS (GEE)

Abstract

ABSTRACT

Repeated measures dose-response mortality studies usually involve obtaining responses at different times on the same group of subjects, which often leads to correlation. A commonly used method for correlated dose-response mortality data is the Probit analytical technique which is suitable for data collected at one point in time and not for repeated measures. This study developed a Generalized Estimating Equations (GEE) using logistic regression for estimating the model parameters in repeated measures dose-response mortality data. The GEE model was applied to adult-termites mortality data observed at 6, 12, 18 and 24 hours respectively from an experiment conducted in the Entomology Division of the Nigerian Institute for Oil Palm Research, NIFOR, Edo State, Nigeria. In the experiment conducted, adult-termites were exposed to two plant extracts; Jatropha Curcas and Ricinus Cummunis at varying concentration levels (10%, 20% and 35%) respectively. The GEE estimated LT50 results for each plant extracts at varying concentration levels were given as J.Curcas (LT50=12.47hrs, 12.47hrs and 12.47hrs) and R.Cummunis (LT50=12.47hrs, 12.47hrs and 12.47hrs) which shows that the potency of the concentration levels is the same considering the time to mortality. Repeated measures logistic regression using GEE has proven to be a robust method in estimating LT50 since it consistently gave precise LT50 estimates with a smaller confidence interval, thus should be incorporated into studies of this nature as other existing methods for analyzing data from bioassay experiments.

Keywords: Repeated measures, dose-response, correlation, Probit analysis, GEE, Survival data, plant extracts, and mortality.