Journal of biopharmaceutical statistics
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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. ⋯ 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.
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Historical Article
Biopharmaceutical statistics in a pharmaceutical regulated environment: past, present, and future.
The practice of statistics in the pharmaceutical industry has changed markedly over the last 25 years. This paper examines the evolution of clinical trial statistics in relationship to advances in statistical methodology and computational power as well as the changing regulatory environment. The current role of the biopharmaceutical statistician is assessed along with the drivers for future change.
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Recent literature has discussed the value of adjustment for important covariates in models involving nonnormal data. It is generally concluded that, when performing traditional "fixed sample size" clinical trials, covariate adjustment influences the magnitude of the treatment effect but has little effect on precision of the estimate. ⋯ Sequential and fixed sample analyses are compared, with and without covariate adjustment of the treatment effect. It was found that conclusions similar to those for the fixed sample size case also apply in the sequential case, but that incorporation of covariate information can present added complications in this setting.
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Comparative Study
Confidence intervals and p-values for Williams' and other step-down multiple comparison tests against control.
When single-step multiple comparison tests against control, such as Dunnett's test, are used, p-values and confidence intervals can be reported. However, Williams' test and other step-down multiple comparison tests only provide results in terms of statistical significance. ⋯ The proposed simultaneous confidence intervals associated with Williams' test, Dunnett's step-down test, and the closed t test are all found to have good coverage, typically between 94% and 96% for a nominal value of 95%. Thus practicing statisticians can now quote p-values for these tests and use simple confidence intervals to aid interpretation of test results.
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At early stages of drug development, the maximum tolerated dose (MTD) must be determined in order to have a range of doses that can be safely administered in humans. This is necessary before the compound can be tested in phase II clinical trials to find the optimal dose in terms of clinical outcome. Although heavily criticized, traditional dose escalation methods are still widely used to estimate the MTD. ⋯ The LDRS requires the use of hypothesized data through a seed data set established before the start of the trial. When a wide dose range needs to be tested, the assumed information (prior distribution or seed data set) can have a great impact on dosages used during the trial and on the final estimates of the MTD. This paper combines the LDRS and the traditional dose escalation procedure to suggest a practical two-stage method that provides reliable estimates through relatively easy computation, and yet requires almost no prior information.