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.     

On Bartlett correction of empirical likelihood for regularly spaced spatial data

Bartlett correction constitutes one of the attractive features of empirical likelihood because it enables the construction of confidence regions for parameters with improved coverage probabilities. We study the Bartlett correction of spatial frequency domain empirical likelihood (SFDEL) based on general spectral estimating functions for regularly spaced spatial data. This general formulation can be applied to testing and estimation problems in spatial analysis, for example testing covariance isotropy, testing covariance separability as well as estimating the parameters of spatial covariance models. We show that the SFDEL is Bartlett correctable. In particular, the improvement in coverage accuracies of the Bartlett‐corrected confidence regions depends on the underlying spatial structures. The Canadian Journal of Statistics 47: 455–472; 2019 © 2019 Statistical Society of Canada     

Multi-valued strategy-proof social choice rules

In this paper we introduce a new definition of strategy-proofness for multi-valued social choice correspondences. We prove two Gibbard-Satterthwaite type results for strategy-proof social choice correspondences. These results show that allowing multiple outcomes as social choices will not necessarily lead to an escape from the Gibbard-Satterthwaite impossibility theorem. Received: 24 January 2001/Accepted: 19 March 2001     

1937年上海“纱交风潮”——以《申报》和《大公报》报道为中心的考察

1937年上海“纱交风潮”系上海纱布交易所爆发的影响巨大且后果最严重的一次风潮。此次风潮的起因是多方面的，其中最根本的原因是孔家官僚资本为获取巨利插手纱交、操纵市场、大搞投机活动。这场风潮促使国民政府颁布《取缔上海纱布交易所投机办法》，并对此次风潮进行“澈查”，然“澈查”结果却虎头蛇尾。由此看出，国民政府与官僚资本的密切关系，即官僚资本已凭借各种超经济特权在国民经济中取得了独特的优势地位。     

社会学视野下的单亲家庭

随着人们家庭婚姻观念的转变,离婚率的迅速上升,单亲家庭也日益增多。在社会学的视野下,单亲家庭作为一种特殊的家庭形式,它的出现在一定程度上是社会文化发展的必然。但是因为社会法律制度和意识形态的滞后性,从福利到舆论,社会都没有为单亲家庭提供和完整家庭一样的待遇,以致给子女、父母以及社会都带来了各种严重的社会问题。     

Generating random variates from D-distributions via substitution sampling

Laud et al. (1993) describe a method for random variate generation from D-distributions. In this paper an alternative method using substitution sampling is given. An algorithm for the random variate generation from SD-distributions is also given.     

良治型政府理論與澳門經濟發展

本文從有關良治型政府理論及主要觀點的角度,探討良治型政府理論在澳門的實踐,以及在CEPA框架下澳門政府扮演的經濟角色與可能存在的風險。本文認為應該從增強澳門經濟多元性以及改善制度建設來完善澳門經濟政策。