摘要:

This article is an extension of the work of Huang and Wong [1], who found dual-objective designs for models with a continuous outcome. We consider quantal dose-response experiments with a binary outcome and develop multiple-objective designs for two or more Bayesian optimality criteria. Using the logit model as an illustrative example, we construct numerically optimal designs for estimating model parameters and percentiles, with possibly unequal interest in each of the objectives. We also show that the popular equal dosage assignment rule can be a rather inefficient design for estimating model parameters under a Bayesian setup.

ISSN:1054-3406    

DOI:10.1081/BIP-100101009

出版商:Taylor & Francis Group    

年代:2000

数据来源: Taylor

摘要:

Identifying the maximum safe dose (MAXSD) is an objective of both randomized clinical dose-finding studies for the safety endpoint and toxicological studies. MAXSD is defined as the highest experimental dose with no significant increased safety effect relative to the placebo or control group. In safety assessment, the primary control of the false-negative error rate is more important than that of the false-positive rate. Therefore, we propose a multiple testing procedure for equivalence in the many-to-one design with a priori ordered contrasts (shifted control vs. dose), where the acceptable risk δ is defined in advance. Tests for shifted and ratio hypotheses are presented and discussed.

ISSN:1054-3406    

DOI:10.1081/BIP-100101010

出版商:Taylor & Francis Group    

年代:2000

数据来源: Taylor

摘要:

In bioequivalence studies, drug formulations are compared in terms of bioavailability parameters such as the area under the concentration-time curve (AUC), the maximum concentration (Cmax), and the time to maximum concentration (tmax). Accuracy in measuring these parameters directly affects the accuracy of bioequivalence tests. Because the number of blood draws per patient is limited, the blood collection times must be spaced so that concentration-time curve measurements can produce accurate bioavailability parameter estimates. This paper describes an optimization approach for calculating optimal time designs for one-compartment models, but is sufficiently general for other compartmental models. Simulation indicates that the optimal design improves the accuracy of AUC estimation.

ISSN:1054-3406    

DOI:10.1081/BIP-100101011

出版商:Taylor & Francis Group    

年代:2000

数据来源: Taylor

摘要:

We would like to estimate the parameters of a dose-response function with the greatest precision as possible. For a two-parameter model, this is equivalent to minimizing the area of the confidence ellipsoid, i.e., aD-optimal design. Previous work on this particular model has included minimal designs. These designs are unable to determine lack of fit. We introduce a distinct dose level to the design to be able to estimate the lack of fit. The minimal and new designs will be compared, and the sample size needed to achieve adequate power for the lack-of-fit test will be derived.

ISSN:1054-3406    

DOI:10.1081/BIP-100101012

出版商:Taylor & Francis Group    

年代:2000

数据来源: Taylor

摘要:

The main aim of many studies in clinical pharmacology is to describe the pharmacokinetic activity of a given compound. This pharmacokinetic activity for an individual is then evaluated through a series of summary parameters, such as area under the concentration-time curve (AUC), maximum concentration (Cmax) and the rate constant λ, and it is evaluated across individuals by descriptive statistics of these parameters, such as the mean and range and a measure of spread such as the standard deviation. How the pharmacokinetic parameters are derived is described here. It is demonstrated that the assumption of an exponential half-life is often fundamental to the derivation of pharmacokinetic parameters. Given this fact, one would think it logical that data are analyzed with the appropriate statistics on the log-scale and not by summary statistics, such as arithmetic means, on the original scale. Why arithmetic means are used to describe the data is explored and the special nature of the log-transformation highlighted.

ISSN:1054-3406    

DOI:10.1081/BIP-100101013

出版商:Taylor & Francis Group    

年代:2000

数据来源: Taylor

摘要:

The aim of a stability study is to check whether or not a drug maintains its initial properties (over a given range) in a period of time. There are many parameters considered in a stability study, but in this paper only the active content will be considered. Evaluation of this parameter is performed by measuring the content of the active ingredient in the drug at different times and on expiration to see if it is within specification [1]. The problem also involves estimating whether the drug will perform satisfactorily and maintain its initial properties in the future. In this paper, we provide a criterion for the evaluation of experimental design used in a stability study for predicting the content of an active ingredient in a drug, starting from the confidence limit calculated following International Conference on Harmonisation of Technical Requirements for the Registration of Pharmaceuticals for Human Use (ICH) recommendations. The point of view adopted is akin to hypothesis testing. The main question is this: “If the drug fulfills the requirements at a given time in the future, what is the probability that the data collected for that drug will show its suitability?” For this purpose, two concepts are used—producer gainandconsumer loss—which are defined as follows: producer gain is the advantage (for the producer) of recognizing a drug as “good” (provided it is good and within specification); consumer loss is the loss (for the consumer) when the drug is no longer good, but the data collected indicate that it is good. The aim of the experimental design in this study is to increase the probability of producer gain and maximize it on the basis of a given consumer loss.

ISSN:1054-3406    

DOI:10.1081/BIP-100101014

出版商:Taylor & Francis Group    

年代:2000

数据来源: Taylor

摘要:

In this paper, we focus on estimating an upper specification for non-normally distributed data. Cornish-Fisher expansions were exploited for derivations of the method. The method was applied for upper specification calculations of two particle-size data sets, and simulation studies were also performed to demonstrate the accuracy of the approximations. The method was also used to calculate the critical values for Δminfor the Anderson-Hauck test for equivalence using bootstrap samplings. The main advantage of the method is in calculating specifications with limited data.

ISSN:1054-3406    

DOI:10.1081/BIP-100101015

出版商:Taylor & Francis Group    

年代:2000

数据来源: Taylor

摘要:

Response-adaptive designs in clinical trials involve incorporating accruing information from patient responses to treatment into the randomization scheme in order to assign more patients to the treatment that has performed better in the trial up to that point. One probability model useful in generating an adaptive randomization scheme is an urn model. We will give a short overview of such adaptive models and compare four of them. We will be interested in how these four models minimize the number of treatment failures in a clinical trial with dichotomous response treatments. Comparison will be done via simulations for four treatments and exactly for three treatments for moderate sample sizes. We compare designs under the assumption that the results of treatments are known immediately, and we also allow some delay in response. Power is analyzed under various alternatives. Our results indicate that a birth and death urn with immigration is the best unless success probabilities are very small, in which case a randomized version of Pólya's urn is preferred.

ISSN:1054-3406    

DOI:10.1081/BIP-100101016

出版商:Taylor & Francis Group    

年代:2000

数据来源: Taylor

摘要:

Generalized linear models are developed for crossover trials with no carryover effects and fixed subject effects. A general multinominal model for the distribution of data is considered. This subsumes both binary and categorical data. Conditional inferences eliminate subject effects by conditioning on their sufficient statistics. For normal data, least-squares analysis is exact with identical treatment inferences from unconditional and conditional analyses. For Poisson data, unconditional and conditional analyses are also identical, but for multinomial data this is not the case and the unconditional analysis is invalid. For multinomial data, asymptotic tests of both treatment effects and goodness of fit are unreliable with small samples. Procedures for exact tests are developed to overcome such problems, using enumeration, random sampling, and a hybrid of importance sampling and enumeration. A four-period binary crossover trial is used to illustrate an exact test of treatment effects by a two-stage sampling procedure based on a factorization of the conditional distribution of the sufficient statistics. An exact test of goodness of fit on the same data illustrates a two-stage scheme mixing importance sampling and enumeration.

ISSN:1054-3406    

DOI:10.1081/BIP-100101017

出版商:Taylor & Francis Group    

年代:2000

数据来源: Taylor