On Choosing Among Several Experimental Treatments and a Control

The two classical selection approaches in comparing experimental treatments with a control are combined to form an integrated approach. In this integrated approach, there is a preference zone (PZ) and an indifference zone (IZ), and the concept of a correct decision (CD) is defined differently in each of these zones. In the PZ, we are required to select the best treatment for a correct decision (CD₁) but in the IZ, we define any selected subset to be correct (CD₂) if it contains the best treatment among all the experimental treatments and the controlled treatment. We propose a single-stage procedure R to achieve the selection goals CD₁ and CD₂ simultaneously with certain probability requirements. It is shown that both the probability of a correct decision under PZ, P(CD₁ | PZ), and the probability of a correct decision under IZ, P(CD₂ | IZ), satisfy some monotonicity properties and the least favorable configuration in PZ and the worst configuration in IZ are derived by these properties. We also derive formulas for the probabilities of correct decision and provide a brief table to illustrate the magnitude of the procedure parameters and the common sample sizes needed for various probability requirements and configurations.     

A Simple Testing Procedure “Control versus k Treatments” for One-sided Ordered Alternatives,with Application in Toxicology

A simple testing procedure “control versus k treatments” for one-sided ordered alternatives for univariate, continuous variables is given. With a simulation study both the first kind risk a and the power behaviour under several distributions, expected value profiles, sample sizes and a levels are shown.     

Two-Stage Procedures for Comparing Treatments with a Control: Elimination at the First Stage and Estimation at the Second Stage

We consider the problem of comparing a set of p₁ test treatments with a control treatment. This is to be accomplished in two stages as follows: In the first stage, N₁ observations are allocated among the p₁ treatments and the control, and the subset selection procedure of Gupta and Sobel (1958) is employed to eliminate “inferior” treatments. In the second stage, N₂ observations are allocated among the (randomly) selected subset of p₂(≤p₁) treatments and the control, and joint confidence interval estimates of the treatment versus control differences are calculated using Dunnett's (1955) procedure. Here both N₁ and N₂ are assumed to be fixed in advance, and the so-called square root rule is used to allocate observations among the treatments and the control in each stage. Dunnett's procedure is applied using two different types of estimates of the treatment versus control mean differences: The unpooled estimates are based on only the data obtained in the second stage, while the pooled estimates are based on the data obtained in both stages. The procedure based on unpooled estimates uses the critical point from a p₂-variate Student t-distribution, while that based on pooled estimates uses the critical point from a p₁-variate Student t-distribution. The two procedures and a composite of the two are compared via Monte Carlo simulation. It is shown that the expected value of p₂ determines which procedure yields shorter confidence intervals on the average. Extensions of the procedures to the case of unequal sample sizes are given. Applicability of the proposed two-stage procedures to a drug screening problem is discussed.     

Exact Confidence Bounds for Comparing Two Regression Lines with a Control Regression Line on a Fixed Interval

The problem of finding exact simultaneous confidence bounds for comparing simple linear regression lines for two treatments with a simple linear regression line for the control over a fixed interval is considered. The assumption that errors are iid normal random is considered. It is assumed that the design matrices for the two treatments are equal and the design matrix for the control has the same number of copies of each distinct row of the design matrix for the treatments. The method is based on a pivotal quantity that can be expressed as a function of four t variables. The probability point depends on the size of an angle associated with the interval. We present probability points for various sample sizes and angles. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Multiple Comparisons of Treatments with Stable Multivariate Tests in a Two‐Stage Adaptive Design,Including a Test for Non‐Inferiority

The application of stabilized multivariate tests is demonstrated in the analysis of a two‐stage adaptive clinical trial with three treatment arms. Due to the clinical problem, the multiple comparisons include tests of superiority as well as a test for non‐inferiority, where non‐inferiority is (because of missing absolute tolerance limits) expressed as linear contrast of the three treatments. Special emphasis is paid to the combination of the three sources of multiplicity – multiple endpoints, multiple treatments, and two stages of the adaptive design. Particularly, the adaptation after the first stage comprises a change of the a‐priori order of hypotheses.     

A Multivariate Procedure for Comparing Mean Vectors for Populations with Unequal Regression Coefficient and Residual Covariance Matrices

In fisheries research there is a need to compare vectors of means of continuous random response variables adjusted for concomitant variations of covariables for populations that have unequal regression coefficient and residual covariance matrices. A multivariate procedure that provides an extended comparison of vectors of adjusted means is presented. An example is presented using a real data set. The procedure is quite general and applicable to many other fields of research.     

Microfluidics and photonics for Bio‐System‐on‐a‐Chip: A review of advancements in technology towards a microfluidic flow cytometry chip

Microfluidics and photonics come together to form a field commonly referred to as ‘optofluidics’. Flow cytometry provides the field with a technology base from which both microfluidic and photonic components be developed and integrated into a useful device. This article reviews some of the more recent developments to familiarize a reader with the current state of the technologies and also highlights the requirements of the device and how researchers are working to meet these needs. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Recombinant Gaussia luciferase with a reactive cysteine residue for chemical conjugation: Expression, purification and its application for bioluminescent immunoassays

The mutated recombinant Gaussia luciferase (hgGLase) having the hinge sequence with a reactive cysteine residue at the carboxyl terminal region was purified from Escherichia coli cells by nickel-chelate affinity chromatography and hydrophobic chromatography. The biotinylated hgGLase (Biotin-hgGLase) was prepared by chemical conjugation with a maleimide activated biotin and apply to bioluminescent immunoassay. In the streptavidin and biotin complex system using Biotin-hgGLase, the measurable range of α-fetoprotein as a model analyte was 0.02–100 ng/ml with the coefficient of variation between 2.5% and 5.2%. The sensitivity of Biotin-hgGLase was similar to that by using the detection system of aequorin, alkaline phosophatase and horseradish peroxidase as a label enzyme.