摘要:

In developing generic drug products, pilot trials are used for identifying successful test formulations to enter pivotal trials. In this study, we derive the power function based on the log-normal distribution and evaluate the effects of potential influential factors—the true test-reference ratio, intrasubject variability, and sample sizes—on the statistical power of a pilot trial to identify successful test formulations, defined as the probability that the test-reference ratio estimate from a pilot trial falls within a predetermined acceptance range when the true ratio is acceptable. Of these influential factors, the test-reference ratio exhibits the largest impact on the statistical power of a pilot trial, followed by intrasubject variability, sample sizes of pivotal trials, and sample sizes of pilot trials. The sample sizes that are used in pilot trials (8–12 subjects) may be sufficient for test products with low intrasubject variability and true ratio close to 1 and may fall short otherwise.

ISSN:1054-3406    

DOI:10.1081/BIP-100101007

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor

摘要:

The work reported in this article was undertaken to evaluate the utility of SAS PROC.MIXED for testing hypotheses concerning GROUP and TIME × GROUP effects in repeated measurements designs with dropouts. If dropouts are not completely at random, covariate control over informative individual differences on which dropout data patterns depend is widely recognized to be important. However, the inclusion of baseline scores and time-in-study as between-subject covariates in an otherwise well formulated SAS PROC.MIXED model resulted in inadequate control over type I error in simulated data with or without dropouts present. The inadequate model formulations and resulting deviant test sizes are presented here as a warning for others who might be guided by the same information sources to employ similar model specifications when analyzing data from actual clinical trials. It is important that the complete model specification be provided in detail when reporting applications of the general linear mixed-model procedure. A single random-coefficients model produced appropriate test sizes, but it provided inferior power when informative covariates were added in the attempt to adjust for dropouts. As an alternative, the incorporation of covariate controls in simpler two-stage endpoint or random regression analyses is documented to be effective in dealing with dropouts under specifiable conditions.

ISSN:1054-3406    

DOI:10.1081/BIP-100101008

出版商:Taylor & Francis Group    

年代:1999

数据来源: Taylor