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.     

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     

Self-management of Initiations by Students Diagnosed with Autism

During prebaseline observations, three students diagnosed with autism were unable to make social initiations to another individual. The ability to make initiations would be considered a “pivotal response” in that it would allow an individual to come into contact with a wide variety of social reinforcement. A multiple-baseline design was implemented to measure the effects of a using a self-management package to teach the students to make social initiations. Two interventionists worked with each student to prompt and reinforce initiations. All students acquired social initiations during externally-determined reinforcement. The initiations were maintained when reinforcement changed from externally determined to a self-management system. The prompts to initiate and self-reinforce were faded over the course of the sessions. The use of self-management by the student led to less need for staff intervention. Implications from the literature are discussed.     

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.     

A simultaneous-equations model of the relationship between public sector bargaining legislation and unionization

This paper estimates a simultaneous-equations model with public sector bargaining laws and union membership treated as jointly-determined variables. The extent of public sector unionization has a significant positive influence on the passage of prolabor bargaining legislation and bargaining legislation has strong, independent effects on the extent of public sector unionization. We gratefully acknowledge the research support provided by Minbo Kim and Parisun Chantonahom.     

Optimal orthogonal-array-based latin hypercubes

The use of optimal orthogonal array latin hypercube designs is proposed. Orthogonal arrays were proposed for constructing latin hypercube designs by Tang (1993). Such designs generally have better space filling properties than random latin hypercube designs. Even so, these designs do not necessarily fill the space particularly well. As a result, we consider orthogonal-array-based latin hypercube designs that try to achieve optimality in some sense. Optimization is performed by adapting strategies found in Morris & Mitchell (1995) and Ye et al. (2000). The strategies here search only orthogonal-array-based latin hypercube designs and, as a result, optimal designs are found in a more efficient fashion. The designs found are in general agreement with existing optimal designs reported elsewhere.