Abstract

The dose-response design is often used in agricultural research when it is necessary to measure a biological response at various levels of an experimental factor. This type of problem is common in chemical and pesticide research, however, it can also occur in other disciplines such as plant, animal, soil, and environmental sciences. While the analysis of dose-response data usually involves fitting a regression curve, the primary objective often centers on the estimation of dose related percentiles such as the LD50 or LC50. These measures are useful for comparing the relative efficacy of various treatments, however, the estimation of the specified percentiles is not always straightforward. Traditional methodology has relied on inverted solutions or asymptotic theory for statistical inference. More recently, computer intensive methods have been used to model dose-response relationships and can be more appropriate than traditional methods in some situations. This paper examines both the traditional and modem approaches to estimating doseresponse functions as they apply to binomial data. The techniques will be demonstrated using mortality data collected on black vine weevil eggs exposed to an organic pesticide treatment.

Keywords

Linearized Probit Analysis, Generalized Nonlinear Models, Bayesian Estimation

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Apr 27th, 10:00 AM

COMPARING ESTIMATION PROCEDURES FOR DOSE-RESPONSE FUNCTIONS

The dose-response design is often used in agricultural research when it is necessary to measure a biological response at various levels of an experimental factor. This type of problem is common in chemical and pesticide research, however, it can also occur in other disciplines such as plant, animal, soil, and environmental sciences. While the analysis of dose-response data usually involves fitting a regression curve, the primary objective often centers on the estimation of dose related percentiles such as the LD50 or LC50. These measures are useful for comparing the relative efficacy of various treatments, however, the estimation of the specified percentiles is not always straightforward. Traditional methodology has relied on inverted solutions or asymptotic theory for statistical inference. More recently, computer intensive methods have been used to model dose-response relationships and can be more appropriate than traditional methods in some situations. This paper examines both the traditional and modem approaches to estimating doseresponse functions as they apply to binomial data. The techniques will be demonstrated using mortality data collected on black vine weevil eggs exposed to an organic pesticide treatment.