Biomedical subjects

Ajit C Tamhane

Publications and source records attributed to Ajit C Tamhane.

3 recordsLinked to original sources

PubMed2007

Tree-structured gatekeeping tests in clinical trials with hierarchically ordered multiple objectives.

This paper discusses a new class of multiple testing procedures, tree-structured gatekeeping procedures, with clinical trial applications. These procedures arise in clinical trials with hierarchically ordered multiple objectives, for example, in the context of multiple dose-control tests with logical restrictions or analysis of multiple endpoints. The proposed approach is based on the principle of closed testing and generalizes the serial and parallel gatekeeping approaches developed by Westfall and Krishen (J. Statist. Planning Infer. 2001; 99:25-41) and Dmitrienko et al. (Statist. Med. 2003; 22:2387-2400). The proposed testing methodology is illustrated using a clinical trial with multiple endpoints (primary, secondary and tertiary) and multiple objectives (superiority and non-inferiority testing) as well as a dose-finding trial with multiple endpoints.

Antihypertensive Agents↗

PubMed2004

Finding the maximum safe dose level for heteroscedastic data.

In this article we extend to the heteroscedastic setting the multiple stepwise test procedures proposed in Tamhane et al. [Tamhane. A. C., Dunnett, C. W., Green, J. W., Wetherington, J. F. (2001). Multiple test procedures for identifying a safe dose. J. Am. Statist. Assoc. 96:835-843] for finding the maximum safe dose. Toxicological data are often heteroscedastic; therefore, the extensions given herein should be highly useful in practice. Simulations are performed to study the Type I familywise error rate and power properties of the procedures. A real data example is given to illustrate the procedures.

Algorithms↗

PubMed2002

Accurate critical constants for the one-sided approximate likelihood ratio test of a normal mean vector when the covariance matrix is estimated.

Tang, Gnecco, and Geller (1989, Biometrika 76, 577-583) proposed an approximate likelihood ratio (ALR) test of the null hypothesis that a normal mean vector equals a null vector against the alternative that all of its components are nonnegative with at least one strictly positive. This test is useful for comparing a treatment group with a control group on multiple endpoints, and the data from the two groups are assumed to follow multivariate normal distributions with different mean vectors and a common covariance matrix (the homoscedastic case). Tang et al. derived the test statistic and its null distribution assuming a known covariance matrix. In practice, when the covariance matrix is estimated, the critical constants tabulated by Tang et al. result in a highly liberal test. To deal with this problem, we derive an accurate small-sample approximation to the null distribution of the ALR test statistic by using the moment matching method. The proposed approximation is then extended to the heteroscedastic case. The accuracy of both the approximations is verified by simulations. A real data example is given to illustrate the use of the approximations.

Biometry↗