Consistency and asymptotic normality of the estimated effective doses in bioassay

In order to estimate the effective dose such as the 0.5 quantile ED₅₀ in a bioassay problem various parametric and semiparametric models have been used in the literature. If the true dose-response curve deviates significantly from the model, the estimates will generally be inconsistent. One strategy is to analyze the data making only a minimal assumption on the model, namely, that the dose-response curve is non-decreasing. In the present paper we first define an empirical dose-response curve based on the estimated response probabilities by using the "pool-adjacent-violators" (PAV) algorithm, then estimate effective doses ED_{100 p} for a large range of p by taking inverse of this empirical dose-response curve. The consistency and asymptotic distribution of these estimated effective doses are obtained. The asymptotic results can be extended to the estimated effective doses proposed by Glasbey [1987. Tolerance-distribution-free analyses of quantal dose-response data. Appl. Statist. 36 (3), 251-259] and Schmoyer [1984. Sigmoidally constrained maximum likelihood estimation in quantal bioassay. J. Amer. Statist. Assoc. 79, 448-453] under the additional assumption that the dose-response curve is symmetric or sigmoidal. We give some simulations on constructing confidence intervals using different methods.