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.     

LOG-PERIODOGRAM ESTIMATION OF LONG MEMORY VOLATILITY DEPENDENCIES WITH CONDITIONALLY HEAVY TAILED RETURNS

Many recent papers have used semiparametric methods, especially the log-periodogram regression, to detect and estimate long memory in the volatility of asset returns. In these papers, the volatility is proxied by measures such as squared, log-squared, and absolute returns. While the evidence for the existence of long memory is strong using any of these measures, the actual long memory parameter estimates can be sensitive to which measure is used. In Monte-Carlo simulations, I find that if the data is conditionally leptokurtic, the log-periodogram regression estimator using squared returns has a large downward bias, which is avoided by using other volatility measures. In United States stock return data, I find that squared returns give much lower estimates of the long memory parameter than the alternative volatility measures, which is consistent with the simulation results. I conclude that researchers should avoid using the squared returns in the semiparametric estimation of long memory volatility dependencies.     

Stjepan Metrović and Michel Maffesoli's 'implosive' defence of the Durkheimian tradition: theoretical convergences around Baudrillard's thesis on the 'end' of the social

This paper critically examines Metrovi? and Maffesoli's attempt to understand postmodernity through Émile Durkheim's nonrational link between society, religion and morality. Metrovi? (1991, 1997) and Maffesoli's (1996) work draws upon this emotional element when attempting to refute Baudrillard's (1983) cognitively focused, if implicit, critique of the Durkheimian tradition. Despite their best intentions, Metrovi? and Maffesoli still fail to exploit the partialities of Baudrillard's critique to the full. While both have some appreciation of the link between emotion and religion as found in Durkheim's The Elementary Forms of Religious Life (1912) they, nevertheless, fall short of grasping its full conceptual importance. This leads them to an implicit acceptance of Baudrillard's thesis on the ‘end’ of the social. This pitfall could have been avoided if Metrovi? and Maffesoli had built their respective analyses of the postmodern condition on a closer reading of The Elementary Forms. Reading this text alongside those other insights on emotion and social life as contained within Durkheim and Mauss's Primitive Classification (1903) and Talcott Parsons's subsequent writings on the sociological problem of religion, would have helped to distance the work of Metrovi? and Maffesoli from that of Baudrillard, and allowed them to offer a stronger and more comprehensive defence of the said tradition.     

Secondary analysis of dangerousness among death sentenced capital murderers

The theory of incapacitation involves reducing an offender's ability or capacity to commit further crimes. Capital punishment accomplishes this goal. An executed murderer never murders again. However, we do not execute all murderers, only capital murderers. This policy produces several research questions. Do capital murderers present a special risk to society? Are capital murderers more likely to murder or commit other violent crimes again than other murderers or the average citizen? To answer these questions, many states require a prediction of future dangerousness of a newly convicted murderer. To what extent has the judgment of future dangerousness matched actuarial data of subsequent murders and serious crimes? Using a secondary analysis, this investigation attempted to assemble available data of postconviction dangerousness of death sentenced capital murderers to create a more comprehensive actuarial account of subsequent dangerousness and to present the data in a common format used by the Federal Bureau of Investigation and the Bureau of Justice Statistics. Across 14 studies identified with relevant data, there were 13 instances of subsequent murder and 462 serious crime or prison rule violations.     

Refugee Children in Wales: Coping and Adaptation in the Face of Adversity

This article aims to makes a contribution to redressing a methodological and theoretical imbalance in existing research on refugee and asylum‐seeking children. It draws on two qualitative studies from Wales with children and young people. The samples include unaccompanied minors and children living with families as well as both children who are supported in the asylum system and those who have been granted refugee status. The focus on ecological post‐migratory stressors and coping processes that young people identify as being integral to their well‐being and, once granted status, to how they adapt to settlement includes a selective study of some of the sources of resilience referred to in the children's talk. Recognising the interplay between individual and environment leads to a discussion of both personal attributes and collective cultural identities.     

Diagnostics for dependence within time series extremes

Summary. The analysis of extreme values within a stationary time series entails various assumptions concerning its long- and short-range dependence. We present a range of new diagnostic tools for assessing whether these assumptions are appropriate and for identifying structure within extreme events. These tools are based on tail characteristics of joint survivor functions but can be implemented by using existing estimation methods for extremes of univariate independent and identically distributed variables. Our diagnostic aids are illustrated through theoretical examples, simulation studies and by application to rainfall and exchange rate data. On the basis of these diagnostics we can explain characteristics that are found in the observed extreme events of these series and also gain insight into the properties of events that are more extreme than those observed.