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.     

More than Face Value: Context and Age Differences in Negative Emotion Discrimination

Journal of Nonverbal Behavior - Age-related deficits are often observed in emotion categorization tasks that include negative emotional expressions like anger, fear, and sadness. Stimulus...     

Can Children Recognize Bodily Expressions of Emotion?

Journal of Nonverbal Behavior - Past research has demonstrated that children understand distinct emotion concepts and can accurately recognize facial expressions of distinct emotions by a young...     

The Effect of Mergers and Acquisitions on Employees: Wealth Transfer,Gain‐Sharing or Pain‐Sharing?

This paper considers whether gains made by shareholders from corporate takeovers are achieved at the expense of employees, as proposed by the ‘wealth transfer’ perspective. It analyses the contribution of employee lay‐offs, along with employment and wage changes, to the takeover premium and abnormal share price movements. The analysis draws on a unique dataset of British takeovers, combining documentary, share price and accounting data. The results show that lay‐offs planned at the takeover have either no effect or adverse effects on shareholder returns. Wages growth is positively, not inversely, related to shareholder returns from the second year after the takeover, whilst positive employment changes have a similar effect in the following year. Closer scrutiny indicates that labour and shareholders share gains when the firm does well, but share pain when it does not. There is evidence, therefore, that labour and shareholder interests can be complementary, rather than antagonistic, after takeovers.     

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     

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.