Bayesian sample size determination for a Phase III clinical trial with diluted treatment effect

When Phase III treatment effect is diluted from what was observed from Phase II results, we propose to determine the Bayesian sample size for a Phase III clinical trial based on the normal, uniform, and truncated normal prior distributions of the treatment effects on an interval, which starts from an acceptable treatment effect to the observed treatment effect from Phase II. After incorporating the prior information of the treatment effects, the Bayesian sample size is the number of patients of the Phase III trial for a given Bayesian Predictive Power (BPP) or Bayesian Historical Predictive Power (BHPP). After that, the numerical simulations are carried out to determine the Bayesian sample size for the Phase III clinical trial. In particular, there exists a hook phenomenon for the BHPP when the number of patients of the Phase II trial equals 70 assuming the normal, uniform, or truncated normal treatment effect. Moreover, we add some sensitivity analysis of the Bayesian sample size about the parameters in the simulations. Finally, we determine the Bayesian sample size (number of events or deaths) of the Phase III trial for a fixed power, Bayesian Historical Power (BHP), and BHPP in the axitinib example.     

On Power and Sample Size Calculation in Ethnic Sensitivity Studies

In ethnic sensitivity studies, it is of interest to know whether the same dose has the same effect over populations in different regions. Glasbrenner and Rosenkranz (2006 Glasbrenner , M. and Rosenkranz , G. ( 2006 ). A note on ethnic sensitivity studies . Journal of Biopharmaceutical Statistics 16 : 15 – 23 .[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) proposed a criterion for ethnic sensitivity studies in the context of different dose-exposure models. Their method is liberal in the sense that their sample size will not achieve the target power. We will show that the power function can be easily calculated by numeric integration, and the sample size can be determined by bisection.     

Statistical Issues Including Design and Sample Size Calculation in Thorough QT/QTc Studies∗

We consider the estimation of shelf life of a drug product when the stability data are discrete. When there is no batch-to-batch variation, the proposed shelf life estimator is an approximate 95% lower confidence bound of the true shelf life. In the presence of batch-to-batch variation, the proposed shelf life estimator is an approximate 95% lower prediction bound of the shelf life of future batches. As a result, the proposed shelf life is applicable to all future batches of the same drug product. Testing for batch-to-batch variation based on discrete responses is also discussed.     

Experience with Reviewing Bayesian Medical Device Trials

The purpose of this paper is to present a statistical reviewer's perspective on some technical aspects of reviewing Bayesian medical device trials submitted to the Food and Drug Administration. The discussion reflects the experiences of the authors and should not be misconstrued as official guidance by the FDA. A variety of applications are described, reflecting our experience with therapeutic and diagnostic devices. In addition to Bayesian analysis of trials, Bayesian trial design and Bayesian monitoring are discussed. Analyses were implemented in WinBUGS (http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml), with the code provided.     

Demonstrating Biosimilarity via Equivalence in Clinical Trials

The opportunities for biosimilar medicines have stimulated much legal and regulatory debates and actions around the world, most notably the passing of the Biologics Price Competition and Innovation Act in the United States in 2009. A key difference between the development of a biosimilar product versus a generic chemical entity is the requirement for well-controlled clinical studies to demonstrate similarity in efficacy and safety. The main objective of this article is to extend the clinical study design methods commonly used in noninferiority trials to equivalence trials within the context of biosimilar product development. We extend the synthesis method to the equivalence setting and provide sample size considerations. We show that while an equivalence trial in general requires a larger sample size than a noninferiority trial, the difference may not be substantial depending on the significance level required for the equivalence trial.     

A Conditional Power Approach to the Evaluation of Predictive Power

In the 1960s and 1970s, almost all clinical trials were designed with a single efficacy analysis at the end. Despite this design, many NIH-sponsored clinical trials were reviewed periodically by Policy Advisory Boards (now called Data and Safety Monitoring Boards). At these reviews, clinicians on the Board often asked: “If the current trend continues, what is the chance that we will have a positive study at the end?” We discuss how to put this question into a statistical framework and provide a simple answer. The “chance” is called conditional power (CP) or predictive power (PP). We discuss the use of CP and PP for early termination of a clinical trial. The concepts of CP and PP can also be applied to sample size determination for a new study or reestimation of sample size in an adaptive design.     

Sample Size Calculation for Poisson Endpoint Using the Exact Distribution of Difference Between Two Poisson Random Variables

We propose a method that compares Poisson distributed outcomes. Our method uses the exact distribution of the difference between two Poisson variables to calculate the sample size required to detect a given difference with prespecified power. When the true difference between the two Poisson rates is more than 1.2 units, the number of subjects and events needed at the desired power and Type I error rate is 5–10% less than that computed by simulation based on the normal approximation method. The normal approximation method is more comparable to the exact sample size method when the difference between the rates is less than 1.2 units. The proposed method is more intuitive, efficient, and less subjective than the normal approximation method. A simple code is developed in R to estimate the sample size and critical values.     

On Incremental Incidence Rates in Long-Term Cohort Safety Studies

In long-term single cohort safety studies, the incremental incidence rate is a useful concept in evaluating risks related to adverse events of medical interest (EMI). We propose estimates for the incremental (say annual) incidence rates in the presence of independent random right censoring and describe their statistical properties. More importantly, we propose tests for the null hypothesis that the incremental incidence rates remain constant during the study against various alternative hypotheses, such as rates changing over the years or having a specific trend, say monotonic increasing or decreasing. In contrast with the existing methods that test constant hazard rates, the proposed method allows nonconstant hazard within each year; therefore, it is especially useful for EMIs that have a seasonal occurrence pattern. This article explores and characterizes properties of these tests under various alternatives via theory and simulations. A numerical example based on a real long-term single cohort safety study is also provided to demonstrate the application and interpretation of the proposed tests. It also illustrates limitations of visually inspecting the rates, as opposed to formally testing them, to assert a causal link between exposure and adverse EMIs.     

Two-Stage Sample Size Reassessment Using Perturbed Unblinding

Sample size reassessment (SSR) is an increasingly popular strategy for designing and conducting clinical trials. In particular, SSR based on updating the variance estimate is a prudent practice accepted by the regulatory authorities to assure adequate power for a study. Since its development in the early 1990s, however, debate has continued over whether a treatment-blinded or unblinded approach should be used for SSR based on the variance estimate. A blind procedure is preferred from the regulatory standpoint, because it better preserves the study integrity; however, it does not provide the best-unbiased estimate of the variance. On the other hand, the usual unblinded analysis reveals the treatment effect, which leads to controversy regarding the interpretation of the targeted effect size as well as concerns of inflating the Type I error and possibly biasing the trial. In this article, we devise a novel solution to this problem, one that uses perturbed unblinding to estimate the variance but still keeps the treatment effect masked. We then give a bias-corrected final test, which preserves the Type I error rate. We also discuss several points of consideration, with a focus on the issues of SSR, that were raised at the recent workshop on adaptive designs held jointly by the U.S. Food and Drug Administration (FDA) and the Pharmaceutical Research and Manufacturers of America (PhRMA). In particular, we propose a switch of paradigm from SSR to a two-stage design for clinical trials to alleviate the concern of possible “change” of behavior due to “change” of sample size.     

中药材质量变异概述

由于受品种、产地、生长年限、采收季节、药用部位、栽培及野生等诸多因素的影响,中药材的质量具有很大变异性。本文在综合分析中药材质量变异影响因子的基础上,提出了针对质量变异研究的采样策略,认为中药材标准制定中应充分考虑指标因子的变异幅度,强调在中成药制剂时,作为原料的中药材应固定产地,稳定栽培措施。