Drug profiling: a new scientific contribution to law enforcement operations in Viet Nam

Since 1995 heroin sample comparisons have been carried out in Viet Nam to establish links between wholesalers and retailers. To that end, the physical and chemical characteristics of samples are analysed: their colour, the packaging material, including fingerprints, diacetylmorphine (heroin) content and the composition of some main alkaloids. At the beginning of 2002, having acquired expertise on impurity profiling and with the support of new instruments, the Institute of Forensic Sciences of Viet Nam introduced the routine impurity profiling of seized heroin and methamphetamine and later undertook to explain that process to national law enforcement bodies. Since then, 375 heroin and 29 methamphetamine samples have been analysed for major and minor impurities. Substances detected in the analysis of illicit heroin include diacetylmorphine, morphine, codeine, 06-monoacetylmorphine and acetylcodeine as well as adulterants such as paracetamol and caffeine. Since methamphetamine impurity profiling began, 29 samples have so far been analysed, and some samples have been grouped through the application of cluster analysis. In the case of heroin, impurity profiling has established a link between two major trafficking groups suspected of obtaining heroin from the same source of production. Analysis has also revealed a link between one wholesaler and several retailers in one region. In addition, impurity profiling provides new information on the preparation and production of some methamphetamine and fake Ecstasy tablets.

Group sequential test strategies for superiority and non-inferiority hypotheses in active controlled clinical trials

In a group sequential active controlled clinical trial, the study hypothesis may be a superiority hypothesis that an experimental treatment is more effective than the active control therapy or a non-inferiority hypothesis that the treatment is no worse than the active control within some non-inferiority range. When it is necessary to plan for testing the superiority and the non-inferiority hypotheses, we propose an adaptive group sequential closed test strategy by which the sample size is planned for testing superiority and is to be increased for showing non-inferiority given that it is deemed more plausible than superiority based on the observed sample path during the course of the trial. The proposed adaptive test strategy is valid in terms of having the type I error probability maintained at the targeted alpha level for both superiority and non-inferiority. It has power advantage or sample size saving over the traditional group sequential test designed for testing either superiority only or non-inferiority only.

Evaluation of a combination drug with multiple doses in unbalanced factorial design clinical trials

Flexibilities for sample size allocation are often demanded for achieving multiple study objectives in combination drug trials. The global tests of Hung, Chi and Lipicky are extended for analysis of unbalanced factorial design trials to test the hypothesis that at least one of the non-zero dose combinations of two drugs is more effective than the respective component doses. When the dose combinations have heterogeneous effect sizes, an unbalanced design may induce greater power than the balanced design. An adjusted p-value approach is proposed for testing the individual dose combinations under the condition that the maximum type I error probability is protected.

Modification of sample size in group sequential clinical trials

In group sequential clinical trials, sample size reestimation can be a complicated issue when it allows for change of sample size to be influenced by an observed sample path. Our simulation studies show that increasing sample size based on an interim estimate of the treatment difference can substantially inflate the probability of type I error in most practical situations. A new group sequential test procedure is developed by modifying the weights used in the traditional repeated significance two-sample mean test. The new test has the type I error probability preserved at the target level and can provide a substantial gain in power with the increase of sample size. Generalization of the new procedure is discussed.

Use of two-stage test statistic in the two-period crossover trials

For two-period crossover trials where the residual carryover can only exist in the presence of treatment effect, Willan (1988, Biometrics 44, 211-218) recommended use of the maximum test statistic that chooses the analysis with the larger test statistic corresponding to the parallel analysis and the crossover analysis. We construct two alternative two-stage test procedures, based on Grizzle's approach, that maintain the actual type I error rate at the desirable level. The power, accuracy, and precision of the analysis based on the modified two-stage procedures are compared to those based on the parallel analysis, crossover analysis, and the maximum test statistic.

Large sample tests for binary outcomes in fixed-dose combination drug studies

Several test statistics are developed for testing the hypothesis that the combination of two drugs at a fixed-dose regimen is more effective than both of the single drugs used alone with respect to a dichotomous response variable. The response probability, logit, and arcsine-root scales are considered. The power function and the significance level are derived for large samples. For the sample size per group of 20 or greater, the power and type I error rate can be accurately calculated using the large sample power function when the response probability ranges from 0.2 to 0.8. These tests have similar power behaviors. In small samples, the large sample power functions of two of the tests can severely underestimate the type I error rate while overestimation can occur with one other test. The utilities of these tests are extended to unbalanced sample size cases. Generally speaking, there is a loss of power with unequal sample size allocation, but the loss is not severe.

The behavior of the P-value when the alternative hypothesis is true

The P-value is a random variable derived from the distribution of the test statistic used to analyze a data set and to test a null hypothesis. Under the null hypothesis, the P-value based on a continuous test statistic has a uniform distribution over the interval [0, 1], regardless of the sample size of the experiment. In contrast, the distribution of the P-value under the alternative hypothesis is a function of both sample size and the true value or range of true values of the tested parameter. The characteristics, such as mean and percentiles, of the P-value distribution can give valuable insight into how the P-value behaves for a variety of parameter values and sample sizes. Potential applications of the P-value distribution under the alternative hypothesis to the design, analysis, and interpretation of results of clinical trials are considered.

Global tests for combination drug studies in factorial trials

To test the hypothesis that there are some studied dose combinations more effective in treating a disease than their respective component doses of two drugs, Hung, Chi and Lipicky proposed two alpha-level tests for normally distributed data. This paper extends the utilities of these tests to the outcome variable that has variance as a function of its mean, such as with a binomially distributed outcome, and to incomplete factorial design settings where some cells are not studied. I explore the impacts of excluding cells from study on the power performances of these tests.

Efficacy evaluation for monotherapies in two-by-two factorial trials

For factorial clinical trials in which two monotherapy treatments under study can interact only in the presence of treatment effects for each treatment, the always-pooled test statistic using data from all four groups has a correct size in detecting the simple effect of an individual treatment used alone. However, this test statistic may have an unbounded bias in estimation of the simple effect. The never-pooled test statistic that uses only data from the treatment group not receiving the other treatment has poor precision for estimating the simple effect. Two alternative test statistics under consideration are the two-stage statistic involving a preliminary test of treatment interaction and the maximum test statistic taking the larger of the always-pooled and the never-pooled statistics. The power, bias, and mean square error of all four tests are compared. When negative interactions exist, the two-stage and maximum statistics are generally superior to the always-pooled statistic and compare reasonably well with the never-pooled statistic; the maximum statistic seems slightly more favorable than the two-stage statistic. The two-stage statistic is the best choice when a treatment interaction can be large.

Two-stage tests for studying monotherapy and combination therapy in two-by-two factorial trials

Two-stage testing involves a preliminary test of a nuisance parameter prior to testing a main hypothesis. In a two-by-two factorial trial, the treatment interaction is the nuisance to the inference about the efficacy of one of the treatments given alone. In comparing a combination therapy to both of its component therapies, the nuisance parameter is the difference in the component effects. When the preliminary test is an integral part of inference about the main parameter, the actual level of significance for the two-stage test procedure can be much higher than the desired nominal level. If one places no restriction on the value of the nuisance parameter, then any two-stage test with its significance level properly controlled has undesirable properties. This applies to comparative studies of combination agents relative to the component agents. When the interaction with an ineffective treatment is null, two-stage testing may have some power advantage for assessing monotherapy efficacy.

Testing for the existence of a desirable dose combination

We consider the problem of studying several dose combinations of two drugs for a therapeutic endpoint in a multilevel factorial clinical trial. Two test statistics are constructed to test whether there exists at least one dose combination that is more effective than its component doses. Their distributions involve nuisance parameters quantifying the mean differences among the doses of the two component drugs. It is shown that their power functions achieve maxima as all the nuisance parameters approach infinity in absolute value. The significance levels of the two tests are derived and two alpha-level tests are proposed. Tables are given to provide the alpha-level critical values for these tests and to gain insights into their power performances.

On identifying a positive dose-response surface for combination agents

This article concerns construction of a confidence surface for tangential slopes of the dose-response surface of a combination therapy to identify where response increases as a function of drug dosage. This approach extends to the assessment of the effectiveness of the combination therapy.

Multidimensional data format specification: a generalization of the American College of Radiology-National Electric Manufacturers Association standards

Multidimensional image data are becoming increasingly common in biomedical imaging. Three-dimensional visualization and analysis techniques based on three-dimensional image data have become an established discipline in biomedicine. Some imaging problems generate image data of even higher dimensions. It often becomes necessary, rather than just convenient, to consider the higher-dimensional data as a whole to adequately answer the underlying imaging questions. Despite this established need for convenient exchange of image and image-derived information, no exchange protocols are available that adequately meet the needs of multidimensional imaging systems. This paper describes an exchange protocol that has been designed after careful consideration of the common requirements of methodologies for visualization and analysis of multidimensional data. It is based on and is a generalization of the widely accepted American College of Radiology-National Electrical Manufacturers Association (ACR-NEMA) standards specified for two-dimensional images. It is implemented and actively being used in a data-, application-, and machine-independent software environment, being developed in the authors' department, for the visualization and analysis of multidimensional images.

Surface and volume rendering in three-dimensional imaging: a comparison

Many surface rendering techniques are currently available for the three-dimensional display of structure data captured by imaging devices. Comparatively fewer volume rendering techniques are also available for the same purpose. The relative performance of these two methodologies in visualization tasks has been a subject of much discussion recently. Although it is very desirable to establish, based on observer studies, objective guidelines stating the relative merits of the two methodologies even for specific situations, it is impossible to conduct meaningful observer studies that take into account the numerousness of the techniques in each methodology, and within each technique, the numerousness of the parameters and their values that control the outcome of the technique. Our aim in this article is to compare the two methodologies purely on a technical basis in an attempt to understand their common weaknesses and disparate strengths. The purpose of this article is twofold--to report a new surface rendering technique and to compare it with two volume rendering techniques reported recently in the literature. The bases of comparison are: ability to portray thin bones; clarity of portrayal of sutures, fractures, fine textures, and gyrations; smoothness of natural ridges and silhouettes; and computational time and storage requirements. We analyze the underlying algorithms to study how they behave under each of these comparative criteria. Our conclusion is that, at the current state of development, the surface method has a slight edge over the volume methods for portrayal of information of the type described above and a significant advantage considering time and storage requirements, for implementations in identical environments.