Inference and Decision Making for 21st-Century Drug Development and Approval

ABSTRACT

The cost and time of pharmaceutical drug development continue to grow at rates that many say are unsustainable. These trends have enormous impact on what treatments get to patients, when they get them and how they are used. The statistical framework for supporting decisions in regulated clinical development of new medicines has followed a traditional path of frequentist methodology. Trials using hypothesis tests of “no treatment effect” are done routinely, and the p-value < 0.05 is often the determinant of what constitutes a “successful” trial. Many drugs fail in clinical development, adding to the cost of new medicines, and some evidence points blame at the deficiencies of the frequentist paradigm. An unknown number effective medicines may have been abandoned because trials were declared “unsuccessful” due to a p-value exceeding 0.05. Recently, the Bayesian paradigm has shown utility in the clinical drug development process for its probability-based inference. We argue for a Bayesian approach that employs data from other trials as a “prior” for Phase 3 trials so that synthesized evidence across trials can be utilized to compute probability statements that are valuable for understanding the magnitude of treatment effect. Such a Bayesian paradigm provides a promising framework for improving statistical inference and regulatory decision making.     

Three-level main-effects designs exploiting prior information about model uncertainty

To explore the projection efficiency of a design, Tsai, et al [2000. Projective three-level main effects designs robust to model uncertainty. Biometrika 87, 467–475] introduced the Q criterion to compare three-level main-effects designs for quantitative factors that allow the consideration of interactions in addition to main effects. In this paper, we extend their method and focus on the case in which experimenters have some prior knowledge, in advance of running the experiment, about the probabilities of effects being non-negligible. A criterion which incorporates experimenters’ prior beliefs about the importance of each effect is introduced to compare orthogonal, or nearly orthogonal, main effects designs with robustness to interactions as a secondary consideration. We show that this criterion, exploiting prior information about model uncertainty, can lead to more appropriate designs reflecting experimenters’ prior beliefs.     

Simultaneous Confidence Intervals for the Population Cell Means,for Two-by-Two Factorial Data,that Utilize Uncertain Prior Information

Consider a two-by-two factorial experiment with more than one replicate. Suppose that we have uncertain prior information that the two-factor interaction is zero. We describe new simultaneous frequentist confidence intervals for the four population cell means, with simultaneous confidence coefficient 1 ? α, that utilize this prior information in the following sense. These simultaneous confidence intervals define a cube with expected volume that (a) is relatively small when the two-factor interaction is zero and (b) has maximum value that is not too large. Also, these intervals coincide with the standard simultaneous confidence intervals obtained by Tukey’s method, with simultaneous confidence coefficient 1 ? α, when the data strongly contradict the prior information that the two-factor interaction is zero. We illustrate the application of these new simultaneous confidence intervals to a real data set.     

Work performance and tax compliance in flat and progressive tax systems

Different tax systems, and their impact on work motivation and tax compliance are significant issues in contemporary political and economic debates. The proportional feature of a flat tax system is assumed to lead to higher performance, while the fairness of the redistributive progressive tax system is assumed to result in higher tax compliance. However, empirical findings on the topic are inconclusive. Both work performance and tax compliance under different tax systems were examined in an experiment, with special attention devoted to the effect of a change in tax systems. A flat tax system was supposed to induce greater work performance, whereas a progressive tax system was expected to increase tax compliance based on fairness perceptions, allowing for the opposite effect due to higher complexity. Furthermore, it was assumed that performance and tax payments would be influenced by motives of self-interest. The design included 20 rounds with a real-effort task in each round, determining participants’ experimental income. Participants (N = 191) made decisions about their tax payments from round-to-round in four different experimental conditions: (1) a flat tax system, (2) a progressive tax system, (3) starting with a flat and changing to a progressive, and (4) starting with a progressive and changing to a flat tax system. Results indicate higher work performance in a progressive system. However, a change from a progressive tax system to a flat system led to increased tax compliance.     

On the construction of optimal experimental designs: a penalty approach

Tn optimal experimental design theory there are well-known situations, in which additional constraints are implied to the design set. These constraints destroy in general the simplex structure of the set of feasible points of the design set. Thus the available iteration procedures for the unrestricted case are no longer applicable.

In this paper a penalty approach is suggested which transforms the restricted problem to the unrestricted case and allows the application of well-known algorithms such as the Fedorov-Wynn-type or the projected gradient procedure.     

三峡工程二期截流水力学试验研究

为了解决好大流量下的深水截流．并且保证整个截流施工期通航.长江科学院在1：80与1：100整体与1：40断面三个模型上进行了水力学试验研究．研究的主要内容有：截流时段与截流流量的选择;龙口位置与宽度的选择；纯立堵与先平抛垫底立堵方案的比较；以上两种方案的通航水流条件；两种方案的龙口水力学条件；戗堤坍塌试验与防护措施等．     

康德的“先验图式说”解析

康德是从分析、审查人的认识能力开始 ,研究科学认识的可能性及条件的。在研究范畴对构成具有客观有效性的科学知识的作用上 ,他提出了“图式说” ,其目的在于探讨先天的范畴怎样和时间提供的感性杂多 (质料 )结合起来 ,从而构成自然科学的先天综合判断的。本文主要解析了康德的先验图式说的提出、内容、本质及其影响     

On Rates of Convergence for Posterior Distributions Under Misspecification

We extend the approach of Walker (2003 Walker , S. ( 2003 ). On sufficient conditions for Bayesian consistency . Biometrika 90 ( 2 ): 482 – 488 .[Crossref], [Web of Science ®] , [Google Scholar]); (2004 Walker , S. ( 2004 ). New approaches to Bayesian consistency . Ann. Statist. 32 ( 5 ): 2028 – 2043 .[Crossref], [Web of Science ®] , [Google Scholar]) to the case of misspecified models. A sufficient condition for establishing rates of convergence is given based on a key identity involving martingales, which does not require construction of tests. We also show roughly that the result obtained by using tests can also be obtained by our approach, which demonstrates the potential wider applicability of this method.     

Some Applications of the Rao Distance to Shrinkage Estimators

Bayesian statistics is concerned with how prior information influence inferences. This article studies this problem by comparing the value of the Rao distance between prior and posterior normal distributions. Particular cases include the linear Bayes estimator, the mixed estimator, and ridge-type estimators.