Modelling and simulation in the pharmaceutical industry--some reflections

Modelling and simulation (M&S) is increasingly being applied in (clinical) drug development. It provides an opportune area for the community of pharmaceutical statisticians to pursue. In this article, we highlight useful principles behind the application of M&S. We claim that M&S should be focussed on decisions, tailored to its purpose and based in applied sciences, not relying entirely on data-driven statistical analysis. Further, M&S should be a continuous process making use of diverse information sources and applying Bayesian and frequentist methodology, as appropriate. In addition to forming a basis for analysing decision options, M&S provides a framework that can facilitate communication between stakeholders. Besides the discussion on modelling philosophy, we also describe how standard simulation practice can be ineffective and how simulation efficiency can often be greatly improved.     

Minding the Gap: Positivism, Psychology, and the Politics of Qualitative Methods

This article explores the politics and practices of qualitative research, in particular by interrogating the "gap" between qualitative and quantitative approaches to research. Two main claims are elaborated: first, that despite being recommended on grounds of ethical superiority (such as of nonmanipulation and instrumentality), qualitative research is not immune from such problems and may indeed harbor its own practices of exploitation; second, and correspondingly, that the moral high ground associated with qualitative research arises from the ways it is used rather than from any intrinsic features of the methodology. The argument is made that it is important to maintain this distinction in order to counter the tendency toward methodological fetishism so prevalent in scientistic (i.e., aspiring to "natural" scientific status) disciplines such as psychology. After framing these issues with some comments on the current popularity of qualitative methods, the article illustrates these claims by exploring 5 key ways that the positivist agenda of the discovery and control of a natural world can return in qualitative research, especially when this is mistakenly evaluated according to criteria formulated for quantitative analyses. The article concludes with a consideration of the epistemological and political status of attempts either to bridge or exacerbate the gap between qualitative and quantitative approaches.     

The response of youth to racial discrimination: implications for resilience theory

This paper presents a qualitative participatory study of Canadian young people who identified themselves as visible minorities and who have experienced discrimination based on their skin colour or ethnicity. Eighteen participants aged 15–24 (12 girls and 6 boys), representing four ethnic minority groups, participated in focus groups and in-depth interviews and shared their responses to racial discrimination against them. Analysis of the data from the four discussion groups reveals that racism occurs in everyday situations and places, a lot of times manifesting itself as subtle forms of discrimination. Our findings also suggest that most of the participants tend to employ non-confrontational approaches when dealing with racial violence against them, and provide us with the rationale behind their intentions. Further, youth are not uniformly impacted by racialized events, and therefore the coping strategies they use vary based on individual and contextual factors. Three common strategies for healing can be derived from the participants’ narratives: expressive-emotional, spiritual-forgiving and communicative-relational. We discuss our findings in the light of theoretical frameworks of resilience and coping approaches and argue that young people’s subjective appraisals of racialized events as well as contextual factors need to be addressed in any discussion on coping and resilience within minority youth populations.     

Selection bias,investment decisions and treatment effect distributions

When making decisions regarding the investment and design for a Phase 3 programme in the development of a new drug, the results from preceding Phase 2 trials are an important source of information. However, only projects in which the Phase 2 results show promising treatment effects will typically be considered for a Phase 3 investment decision. This implies that, for those projects where Phase 3 is pursued, the underlying Phase 2 estimates are subject to selection bias. We will in this article investigate the nature of this selection bias based on a selection of distributions for the treatment effect. We illustrate some properties of Bayesian estimates, providing shrinkage of the Phase 2 estimate to counteract the selection bias. We further give some empirical guidance regarding the choice of prior distribution and comment on the consequences for decision-making in investment and planning for Phase 3 programmes.