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Peter Ouyang

Publications and source records attributed to Peter Ouyang.

2 recordsLinked to original sources

Controlling type I error rate for fast track drug development programmes.

The U.S. Food and Drug Administration (FDA) Modernization Act of 1997 has a Section (No. 112) entitled 'Expediting Study and Approval of Fast Track Drugs' (the Act). In 1998, the FDA issued a 'Guidance for Industry: the Fast Track Drug Development Programs' (the FTDD programmes) to meet the requirement of the Act. The purpose of FTDD programmes is to 'facilitate the development and expedite the review of new drugs that are intended to treat serious or life-threatening conditions and that demonstrate the potential to address unmet medical needs'. Since then many health products have reached patients who suffered from AIDS, cancer, osteoporosis, and many other diseases, sooner by utilizing the Fast Track Act and the FTDD programmes. In the meantime several scientific issues have also surfaced when following the FTDD programmes. In this paper we will discuss the concept of two kinds of type I errors, namely, the 'conditional approval' and the 'final approval' type I errors, and propose statistical methods for controlling them in a new drug submission process.

Clinical Trials, Phase III as Topic↗

Dunnett's many-to-one test and least square means.

Dunnett's many-to-one test is used frequently today, especially in dose-finding studies. Using Dunnett's test, the Type I error level for the comparison between the raw mean of the control and the raw means of the study drug groups can be exactly calculated for the normal data. However, this computability depends on the independence of the raw means. Unfortunately, this independence does not exist for the model-based likelihood estimates (least square means) in the cases of ANCOVA and two-way ANOVA models without interaction for unbalanced data. This paper investigates this dependence between the least square means and derives some new procedures to calculate the joint distribution of the statistic for Dunnett's test.

Analysis of Variance↗