Health Policy and the Politics of Evidence

National decisions on the drugs, treatments and medical devices that should be funded through public expenditure are a fundamental element of health policy. But despite a political emphasis upon evidence‐based policy, the results of rigorous clinical trials and statistical modelling techniques rarely speak for themselves. So, does the pre‐eminence traditionally accorded to quantitative data in the medical field underpin policy decisions on a consistent basis? Or are more subtle, less transparent characteristics of context and interaction evident in the shaping of attendant decisions? This article considers these questions by drawing on a study of decision‐making in the National Institute for Health and Clinical Excellence (NICE)—an organization established by the British government in 1999 to decide whether selected health technologies should be made available throughout the National Health Service in England and Wales. In broad terms, the findings point to the primacy of arguments based on quantitatively oriented, experimentally derived data but also to a discursive hegemony of clinicians and health economists in mediating, including or debarring more qualitative, experientially based evidence. A more complex, dynamic understanding of policy governance in the field of health technology appraisal—founded on a discursive appropriation of the idea of the “common good”—goes some way to explaining the persistence of this hegemony despite an avowedly inclusive, plural approach to decision‐making.     

Distance sampling methodology

We consider the method of distance sampling described by Buckland, Anderson, Burnham and Laake in 1993. We explore the properties of the methodology in simple cases chosen to allow direct and accessible comparisons of distance sampling in the design- and model-based frameworks. In particular, we obtain expressions for the bias and variance of the distance sampling estimator of object density and for the expected value of the recommended analytic variance estimator within each framework. These results enable us to clarify aspects of the performance of the methodology which may be of interest to users and potential users of distance sampling.     

The multivariate alpha-power model

The main object of this paper is to propose a multivariate extension to the alpha-power model which is an alternative to the multivariate skew-normal model (Arellano-Valle and Azzalini, 2008). It also extends the power-normal model discussed in Gupta and Gupta (2008) by making it more flexible. Inference is dealt with by using the likelihood approach and a pseudo-likelihood approach based on conditional distributions which, although slightly less efficient, is simpler to implement. An application to a real data set is used to demonstrate the usefulness of the extension.     

Bayesian outlier testing using the predictive distribution for a linear model op constant intraclass form

The problem of testing suspected outliers from a linear model with constant intraclass correlation is considered from a Bayesian viewpoint. The main objective of this paper is to develop an outlier test procedure based on the predictive distribution of suspected outlier observations given a set of existing inlier observations. The test procedure is easily performed with the usual F and t distributions.     

Re-instating the vague

From medicine and military warfare to the practices of modern management, we have come to pursue the ideal of precision in order to generate the knowledge necessary to organise our social, cultural and economic lives. Underpinning this valorisation of the ideal of precision is a repulsion for its opposite –vagueness– which is habitually treated as a synonym for chaos, uncertainty and uselessness. Yet vagueness, understood here as a condition of radical uncertainty or open possibility, is inescapably imbricated in the triumphs of precision. This paper challenges this neglect by arguing that, once stripped of its pejorative connotations, vagueness can be understood as the silent but often salient partner in a marriage of opposites. Tracing the conceptual pairing of these contrasting terms, the paper goes on to suggest that instead of prising them apart we need to recognise their mutual reciprocity. By holding vagueness and precision in dynamic tension, it is suggested, we can develop a critical exploration of contemporary thinking in public policy formulation with its predilection for ‘joined‐up thinking’ and a general ‘blurring of boundaries’.     

Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials

Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization and Monte Carlo methods for solving hybrid Bayesian networks. Any probability density function (PDF) can be approximated by an MTE potential, which can always be marginalized in closed form. This allows propagation to be done exactly using the Shenoy-Shafer architecture for computing marginals, with no restrictions on the construction of a join tree. This paper presents MTE potentials that approximate standard PDF’s and applications of these potentials for solving inference problems in hybrid Bayesian networks. These approximations will extend the types of inference problems that can be modelled with Bayesian networks, as demonstrated using three examples.