Statistical Considerations for Testing Multiple Endpoints in Group Sequential or Adaptive Clinical Trials

Many clinical trials are designed with a fixed sample size or total number of events to detect a postulated size of treatment effect on a primary efficacy endpoint. When the trial is completed and the primary efficacy endpoint achieves statistical significance, formal statistical testing of other clinically important secondary endpoints often follows in order for the statistically and clinically significant results of these endpoints to be included in the label of the test pharmaceutical product. In conventional fixed designs without any interim analysis or trial extension, these endpoints are often tested in a pre-specified hierarchical order, following the closed testing principle. This testing strategy ensures a strong control of the overall type I error. However, when trials are conducted using a group-sequential design with interim analyses or can be extended using an adaptive design with an increase of sample size or total number of events, this conventional hierarchical testing strategy may violate the closure principle and the overall type I error rate may not be controlled in the strong sense.     

Parametric Two-Stage Sequential Quality Assurance Test of Dose Content Uniformity

The United States Pharmacopeia (USP) content uniformity sampling acceptance plan consisting of a two-stage sampling plan with criteria on sample mean and number of out-of-range tablets is the standard for compendium. It is, however, often used mistakenly for lot quality assurance. In comparison to the Japan Phamacopeia (JP) procedure, USP procedure is less discriminative between lots with on-target mean and small variance and lots with off-target mean and large variance. The new European Pharmacopeia (EP) and USP harmonized test adopted a tolerance interval approach. But the “no-difference zone” criteria modification for off-target products make the approaches biased in favor of off-target products. We propose a parametric tolerance interval procedure to test a two-sided specification that is equivalent to the test of two one-sided hypotheses. Testing against a lower specification is to assure that the drug product is not under-dosed for the sake of efficacy. On the other hand, testing against an upper specification is to assure that the drug product is not over-dosed for the sake of safety. The operating curves of the proposed procedure are compared with those of the USP test to illustrate the difference in acceptance probability against the mean and variance of the lot.     

Parametric Two-Tier Sequential Quality Assurance Test of Delivery Dose Uniformity of Multiple-Dose Inhaler and Dry Powder Inhaler Drug Products

The delivery dose uniformity is one of the most critical requirements of dry powder inhaler and metered dose inhaler products. In 1998, the U.S. Food and Drug Administration recommended a two-tier acceptance sampling plan in the Draft Guidance of Metered Dose Inhaler and Dry Powder Inhaler Drug Products Chemistry, Manufacturing and Controls. The two-tier procedure is a modification of the United States Pharmacopeia (USP) sampling plan of dose content uniformity. It employed a zero tolerance criterion. In addition, it has a near-zero probability acceptance at the second tier. In this article, a two-tier sequential tolerance interval approach is proposed that is equivalent to a two-tier two one-sided testing procedure. It controls the probability of the product delivering below a prespecified effective dose and the probability of the product delivering over a prespecified safety dose.     

Statistical Monitoring of Safety in Clinical Trials

Appropriate monitoring of safety data during the conduct of a clinical trial can ensure timely alteration or termination of the trial to protect patients from potentially harmful treatment. Quantitative evaluation in safety monitoring is important for the study team and the data monitoring committee to make timely recommendations. This article provides an overview of statistical methods for monitoring a prespecified adverse event of interest in a single-arm or controlled clinical trial, including those described in the literature and two proposed methods following a general Bayesian framework using conjugate families. The implementation of statistical methods on safety monitoring is illustrated through clinical trial examples. Practical challenges and considerations are also discussed via simulation studies.     

Adaptive Statistical Analysis Following Sample Size Modification Based on Interim Review of Effect Size

ABSTRACT

In designing a comparative clinical trial, the required sample size is a function of the effect size, the value of which is unknown and at best may be estimated from historical data. Insufficiency in sample size as a result of overestimating the effect size can be destructive to the success of the clinical trial. Sample size re-estimation may need to be properly considered as a part of clinical trial planning. This paper is intended to give the motivations for the sample size re-estimation based partly on the effect size observed at an interim analysis and for a resulting simple adaptive test strategy. The performance of this adaptive design strategy is assessed by comparing it with a fixed maximum sample size design that is properly adjusted in anticipation of the possible sample size adjustment.