J C Gittins | PubMed Health

PubMed2002

A decision theoretic approach to sample size determination in clinical trials.

In this paper, we discuss a Behavioral Bayes approach to the determination of sample size in phase III clinical trials for which the data are assumed to come from a normal distribution for which the mean and variance are both unknown. Software is described which minimizes the expected net cost as a function of the sample size, thereby establishing the optimal sample size. This methodology extends previous work by the assumption of unknown variance. Numerical examples show that the more general model can have a large effect on the optimal sample size, compared with a procedure which uses the known variance model with an estimate of the variance.

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Prediction of biological activity for high-throughput screening using binary kernel discrimination.

High-throughput screening has made a significant impact on drug discovery, but there is an acknowledged need for quantitative methods to analyze screening results and predict the activity of further compounds. In this paper we introduce one such method, binary kernel discrimination, and investigate its performance on two datasets; the first is a set of 1650 monoamine oxidase inhibitors, and the second a set of 101 437 compounds from an in-house enzyme assay. We compare the performance of binary kernel discrimination with a simple procedure which we call "merged similarity search", and also with a feedforward neural network. Binary kernel discrimination is shown to perform robustly with varying quantities of training data and also in the presence of noisy data. We conclude by highlighting the importance of the judicious use of general pattern recognition techniques for compound selection.

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