Biomedical subjects

Hansheng Wang

Publications and source records attributed to Hansheng Wang.

7 recordsLinked to original sources

PubMed2003

In vitro bioequivalence testing.

A statistical test is proposed for in vitro bioequivalence testing between drug products such as nasal aerosols and nasal sprays. The proposed test generalizes the one recommended in the FDA 1999 guidance to the situation where replicated observations obtained from each sampled canister or bottle of the drug product are available. The technique developed by Hyslop, Hsuan and Holder is used so that the proposed test is asymptotically accurate. The type I error probability and power of the proposed test are investigated through a simulation study. A method for determining the required sample size to achieve a desired power is also proposed. A numerical example is given for illustration.

Administration, Intranasal↗

PubMed2002

A practical approach for comparing means of two groups without equal variance assumption.

In this paper we consider two-groups of i.i.d. normally distributed random variables (N(mu(x),sigma(x) (2)) and N(mu(y),sigma(y) (2))) without assuming equal variance (sigma(x) (2) = sigma(y) (2)). We propose a simple method for constructing confidence bounds based on Howe's approximation I. Its applications in parallel clinical trial (testing H(0) : mu(x)-mu(y)=0 versus H(1) : mu(x)-mu(y)<0) and parallel bioequivalence (BE) trial (testing H(0):mid R:mu(x)-mu(y)mid R:delta versus H(1):mid R:mu(x)-mu(y)mid R:<delta) are studied. Sample size calculation formulae for both cases are derived. Their performances are evaluated by simulation. Our study shows that the proposed procedure can control type I error satisfactorily compared with Cochran-Cox's and Satterthwaite's approximations while maintaining a relatively high power. The proposed approach is not only simple for constructing the confidence limit, but also provides a simple and accurate formula for sample size calculation.

Analysis of Variance↗

PubMed2002

Individual bioequivalence testing under 2x3 designs.

In recent years, as more generic drug products become available, it is a concern not only whether generic drug products that have been approved based on the regulation of average bioequivalence will have the same quality, safety and efficacy as that of the brand-name drug product, but also whether the approved generic drug products can be used interchangeably. In its recent draft guidance, the U.S. Food and Drug Administration (FDA) recommends that individual bioequivalence (IBE) be assessed using the method proposed by Hyslop, Hsuan, and Holder to address drug switchability. The FDA suggests that a 2x4 cross-over design be considered for assessment of IBE, while a 2x3 cross-over design may be used as an alternative design to reduce the length and cost of the study. Little or no information regarding the statistical procedures under 2x3 cross-over designs is discussed in the guidance. In this paper, a detailed statistical procedure for assessment of IBE under 2x3 cross-over designs is derived. The main purpose of this paper, however, is to derive an IBE test under an alternative 2x3 design and show that the resulting IBE test is better than that under a 2x3 cross-over design and is comparable to or even better than that under a 2x4 cross-over design. Our conclusions are supported by theoretical considerations and empirical results. Furthermore, a method of determining the sample sizes required for IBE tests to reach a given level of power is proposed.

Computer Simulation↗

PubMed2002

Probability lower bounds for USP/NF tests.

In the pharmaceutical industry, a number of tests such as content uniformity and dissolution testing are usually performed at various stages of drug manufacturing process to ensure that the drug product meets standards for identity, strength, quality, purity, and stability of the drug product as specified in the United States Pharmacopedia and National Formulary (USP/NF). The USP/NF provides requirements for sampling plans, testing procedures, and acceptance criteria for these tests. To ensure that there is a high probability of passing the USP/NF tests, the sponsors usually establish in-house specification limits based on some lower bounds of the probabilities of passing USP/NF tests for future samples. In this article, we derive some probability lower bounds for USP/NF tests. It is shown that the proposed probability lower bounds are better than the existing ones and are very close to the true probabilities in a broad range of the population mean and variance of the test sample.

Drug Compounding↗

PubMed2002

A note on sample size calculation for mean comparisons based on noncentral t-statistics.

One-sample and two-sample t-tests are commonly used in analyzing data from clinical trials in comparing mean responses from two drug products. During the planning stage of a clinical study, a crucial step is the sample size calculation, i.e., the determination of the number of subjects (patients) needed to achieve a desired power (e.g., 80%) for detecting a clinically meaningful difference in the mean drug responses. Based on noncentral t-distributions, we derive some sample size calculation formulas for testing equality, testing therapeutic noninferiority/superiority, and testing therapeutic equivalence, under the popular one-sample design, two-sample parallel design, and two-sample crossover design. Useful tables are constructed and some examples are given for illustration.

Algorithms↗

PubMed2002

On sample size calculation based on odds ratio in clinical trials.

Sample size calculation formulas for testing equality, noninferiority, superiority, and equivalence based on odds ratio were derived under both parallel and one-arm crossover designs. An example concerning the study of odds ratio between a test compound (treatment) and a standard therapy (control) for prevention of relapse in subjects with schizophrenia and schizoaffective disorder is presented to illustrate the derived formulas for sample size calculation for various hypotheses under both a parallel design and a crossover design. Simulations were performed to assess the adequacy of the sample size calculation formulas. Simulation results were given at the end of the paper.

Algorithms↗

PubMed2002

Tests for inter-subject and total variabilities under crossover designs.

In this paper, we consider statistical tests for inter-subject and total variabilities between treatments under crossover designs. Since estimators of variance components for inter-subject variability and total variability in crossover design are not independent, the usual F-test cannot be applied. Alternatively, we propose a test based on the concept of the extension of the modified large sample method to compare inter-subject variability and total variability between treatments under a 2 x 2 m replicated crossover design. An asymptotic power of the proposed test is derived. A sensitivity analysis is performed based on the asymptotic power to determine how the power changes with respect to various parameters such as inter-subject correlation and intra-class correlation. Also the two methods for sample size calculation for testing total variability under 2 x 4 crossover design are discussed. The method based on the Fisher-Cornish inversion shows better performance than the method based on the normal approximation. Several simulation studies were conducted to investigate the finite sample performance of the proposed test. Our simulation results show that the proposed test can control type I error satisfactorily.

Analysis of Variance↗