Improved estimation of potency in slope ratio assays

Regression coefficients are regarded as stochastic variables in order to account for this kind of variability. This is needed, because differences in responses at successive doses vary generally and thus the assumption of constancy of regression coefficients is violated.     

Multivariate slope ratio assay with repeated measurements

A method is given for analyzing a slope ratio assay in which a test drug is compared with a standard drug, two or more response variates being measured on each subject at each of several successively increased drug doses. The method requires all subjects to receive the same number of doses, all subjects on the same drug to receive the same doses, the ratio of corresponding doses of the two drugs to be constant over the successive increases, and response variables to be measured only once on each subject at each dose with no missing data allowed. The technique is also applicable when doses are randomly assigned, provided there is no carry-over effect between doses. For each of the J response variates, the relative potency of the test drug with respect to the standard is defined and estimated in the usual way; a 100(1-alpha)% confidence region is then obtained for the vector of the J relative potencies. A procedure is given for testing the equality of some or all of the J relative potencies; an estimator of a common relative potency is obtained by a standard multivariate least squares method. A common relative potency is of interest because the multiple outcome variables are often different indicators of a general physiologic response. The procedures in the paper are illustrated by a simple example concerning the effects of two anesthetics on children.     

A Bayesian analysis of the slope ratio bioassay

The analysis of biological assays has received the attention of statisticians for many years. However, when an indirect assay is considered and a continuous response variable is measured, the standard models lead to the problem of estimating a ratio, which has proved to be rather controversial if the statistical analysis is conducted under the classical approach. In this paper, within the Bayesian framework, the reference posterior distribution of slope ratio is obtained. This is the parameter of interest in a large class of biological assays. The results obtained are provided to avoid the drawbacks of the classical methods and generalize previous Bayesian analysis of the ratio of normal means.     

Indirect assays for deoxyhypusine hydroxylase using dual-label ratio changes and oxidative release of radioactivity

Two procedures for rapid assay of deoxyhypusine hydroxylase activity are described. One of these assays measures changes in the 3H:14C ratio of dual-labeled protein that results from the release of tritium from a specific position in the side chain of the 3H,14C-labeled constituent amino acid deoxyhypusine upon its conversion to [3H,14C]hypusine. The other assay relies upon release of radioactivity from product protein by periodate oxidation of the radiolabeled side chain of component hypusine. The good correspondence of each of these assays with the ion exchange chromatographic method which measures hypusine and deoxyhypusine in acid hydrolysates of protein indicates that each provides a valid means of determining deoxyhypusine hydroxylase activity.     

Dose-Finding Designs for HIV Studies

We present a class of simple designs that can be used in early dose-finding studies in HIV. Such designs, in contrast with Phase I designs in cancer, have a lot of the Phase II flavor about them. Information on efficacy is obtained during the trial and is as important as that relating to toxicity. The designs proposed here sequentially incorporate the information obtained on viral reduction. Initial doses are given from some fixed range of dose regimens. The doses are ordered in terms of their toxic potential. At any dose, a patient can have one of three outcomes: inability to take the treatment (toxicity), ability to take the treatment but insufficient reduction in viral load (viral failure), and ability to take the treatment as well as a sufficient reduction of viral load (success). A clear goal for some class of designs would be the identification of the dose leading to the greatest percentage of successes. Under certain assumptions, which we identify and discuss, we can obtain efficient designs for this task. Under weaker, sometimes more realistic assumptions, we can still obtain designs that have good operating characteristics in identifying a level, if such a level exists, having some given or greater success rate. In the absence of such a level, the designs will come to an early closure, indicating the ineffectiveness of the new treatment.     

Designs for Bottles and Textiles

Unfamiliar Masterpieces of Painting: October House, Inc. Publishers. List price $17.50. Reviewed by Walther Scheidig.

The Art Buff's Book Robert B. Luce, Inc., Publishers List price $3.95. Reviewed by Arthur Cody

Master Watercolors of the Twentieth Century Introduction, Inc. Publishers List price: $17.50 Reviewed by Werner Haftmann Harry N. Abrams     

Efficiency Indices for Second-Order Designs

Design efficiencies are studied for inferences regarding the coefficients of second-order models. Subspaces of parameters are identified wherein each of two designs dominates the other locally, or the two designs are equally efficient. Both Fisher efficiency in estimation and Pitman efficiency in hypothesis testing are considered. Eight small second-order designs in k=3 regressor variables are screened using a four-part efficiency index. Of these, the central composite, Box-Behnken, small composite, and hybrid H310 and H311 B designs are found to be generally superior and roughly comparable. Detailed comparisons among these five designs are then undertaken. This work provides further tools for use in design evaluation, requiring only basic matrix operations independently of experimental data.     

Subset Selection Procedures for Randomized Designs

Starting from the principle that there exists a randomization procedure that assigns treatments to experimental units, four subset selection rules for the problem of selecting the best treatment from a set of different treatments are proposed. Two of these are extensions of already existing subset selection procedures, which were defined for unbalanced designs, and need a separate selection constant for each individual treatment. The other two rules proposed are new and need only one selection constant for all treatments. The various procedures are compared, and illustrated by application to a plant breeding variety trial.     

Statistical Analyses for Multistage Experiment Designs

Suppose that t experiments are conducted simultaneously on the same set of experimental units. For example, suppose that t mutually orthogonal latin square experiment designs are used for the t experiments on n² experimental units. Statistical literature is voluminous on construction of such designs, but contains relatively little and incomplete results on statistical analyses for such designs. Six statistical analyses are presented for a pair of orthogonal latin square experiment designs. Then, the methods are generalized for t mutually orthogonal experiment designs. The results are also extended to a set of t mutually balanced Youden experiment designs.     

Rank Tests for Complete Block Designs

In this article a general univariate K-sample rank test for complete block designs with proportional cell frequencies is derived. It is shown that the test statistic has under H₀ and for arbitrary scores asymptotically a X²-distribution with K — 1 degrees of freedom. Special cases of this test are the Kruskal-Wallis test and the Friedman test. The test is compared with the Benard-van-Elteren test, the Mack-Skillings test and a test proposed by Downton. Finally the application of the test is illustrated by two examples.     

Flexible Designs for Genomewide Association Studies

Summary .  Genomewide association studies attempting to unravel the genetic etiology of complex traits have recently gained attention. Frequently, these studies employ a sequential genotyping strategy: A large panel of markers is examined in a subsample of subjects, and the most promising markers are genotyped in the remaining subjects. In this article, we introduce a novel method for such designs enabling investigators to, for example, modify marker densities and sample proportions while strongly controlling the family-wise type I error rate. Loss of efficiency is avoided by redistributing conditional type I error rates of discarded markers. Our approach can be combined with cost optimal designs and entails a greater flexibility than all previously suggested designs. Among other features, it allows for marker selections based upon biological criteria instead of statistical criteria alone, or the option to modify the sample size at any time during the course of the project. For practical applicability, we develop a new algorithm, subsequently evaluate it by simulations, and illustrate it using a real data set.