Locally and maximin optimal designs for multi-factor nonlinear models

A web-based tool for designing experimental studies to detect hormesis and estimate the threshold dose

Hormesis has been widely observed and debated in a variety of context in biomedicine and toxicological sciences. Detecting its presence can be an important problem with wide ranging implications. However, there is little work on constructing an efficient experiment to detect its existence or estimate the threshold dose. We use optimal design theory to develop a variety of locally optimal designs to detect hormesis, estimate the threshold dose and the zero-equivalent point (ZEP) for commonly used models in toxicology and risk assessment. To facilitate use of more efficient designs to detect hormesis, estimate threshold dose and estimate the ZEP in practice, we implement computer algorithms and create a user-friendly web site to help the biomedical researcher generate different types of optimal designs. The online tool facilitates the user to evaluate robustness properties of a selected design to various model assumptions and compare designs before implementation.

Maximin optimal designs for cluster randomized trials

We consider design issues for cluster randomized trials (CRTs) with a binary outcome where both unit costs and intraclass correlation coefficients (ICCs) in the two arms may be unequal. We first propose a design that maximizes cost efficiency (CE), defined as the ratio of the precision of the efficacy measure to the study cost. Because such designs can be highly sensitive to the unknown ICCs and the anticipated success rates in the two arms, a local strategy based on a single set of best guesses for the ICCs and success rates can be risky. To mitigate this issue, we propose a maximin optimal design that permits ranges of values to be specified for the success rate and the ICC in each arm. We derive maximin optimal designs for three common measures of the efficacy of the intervention, risk difference, relative risk and odds ratio, and study their properties. Using a real cancer control and prevention trial example, we ascertain the efficiency of the widely used balanced design relative to the maximin optimal design and show that the former can be quite inefficient and less robust to mis-specifications of the ICCs and the success rates in the two arms.