Testing for Excess Over Highest Single Agent with an Application to High-throughput Screening of Pairs of Compounds
John J. Peterson
Abstract: Combination drug therapy offers much promise for discovering pharmaceutical treatments that are efficacious and safe. A key efficacy criterion for a combination of two compounds is that the combination is superior to both of its component compounds used alone. This article proposes a simultaneous testing procedure, based upon step-down trend tests, that identifies dose combinations for pairs of compounds that produce efficacy results with excess over highest single agent (i.e. the combination is superior to both of the component compounds). This testing procedure is applied to data from experiments for a pilot high-throughput screening study for pairs of compounds evaluated at nine dose levels using 9×9 factorial experiments. This procedure is easily automated and can be computed using the SAS® MULTTEST procedure.
Inter-translation of Biomedical Coding Schemes Using UMLS
Jeffery L. Painter, Kristopher M. Kleiner, Gary H. Merrill
Abstract: We report the results of our work in using the Unified Medical Language System (UMLS) 1 to apply biomedical ontologies to practical problems faced by epidemiologists in extracting study cohorts from large disparate observational data bases.
O’Brien’s OLS and GLS Statistics in Clinical Trials: Multivariate Approach
Nigel Dallow, Sergei Leonov and James Roger
Abstract: Multivariate techniques of O’Brien’s OLS and GLS statistics are discussed in the context of their application in clinical trials. We introduce the concept of an operational effect size and illustrate its use to evaluate power. An extension describing how to handle covariates and missing data is developed in the context of Mixed models. This extension allowing adjustment for covariates is easily programmed in any statistical package including SAS. Monte Carlo simulation is used for a number of different sample sizes to compare the actual size and power of the tests based on O’Brien’s OLS and GLS statistics.
Estimation of Population PK Measures: Selection of Sampling Grids
Valerii Fedorov and Sergei Leonov
Abstract: In clinical pharmacokinetic (PK) studies multiple blood samples are taken for each enrolled patient, and various population PK measures, such as area under the curve (AUC), maximal concentration (Cmax) and time to maximal concentration (Tmax) are estimated. In this paper we compare a model-based approach, where parameters of a compartmental model are estimated and the explicit formulae for PK measures are used, and an empirical, or model-independent, approach, where numerical integration algorithms are used for AUC and sample estimates for Cmax and Tmax. Since regulatory agencies usually require the model-independent estimation of PK measures, we focus on the empirical approach while using the model-based approach as a benchmark. We show how to “split” a single sampling grid into two or more subsets, which substantially reduces the number of samples taken for each patient, but often has little effect on the precision of estimation of PK measures in terms of mean squared error (MSE). For a number of special cases we derive explicit formulae for the MSE of the empirical estimator of AUC. When costs of patient’s enrolment and costs of analyzing samples are introduced, these formulae allow the optimal selection of the number of patients and samples per patient.
