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David Spiegelhalter

Publications and source records attributed to David Spiegelhalter.

7 recordsLinked to original sources
PubMed2005

Incorporation of uncertainty in health economic modelling studies.

In a recent leading article in PharmacoEconomics, Nuijten described some methods for incorporating uncertainty into health economic models and for utilising the information on uncertainty regarding the cost effectiveness of a therapy in resource allocation decision-making. His proposals are found to suffer from serious flaws in statistical and health economic reasoning.Nuijten's suggestions for incorporating uncertainty: (a) wrongly interpret the p-value as the probability that the null hypothesis is true; (b) represent this probability wrongly by truncating the input distribution; and (c) in the specific example of an antiparkinsonian drug uses a completely inappropriate p-value of 0.05 when the null hypothesis would, in reality, be emphatically disproved by the data.His suggestions regarding minimum important differences in cost effectiveness: (a) introduce areas of indifference that suggest inappropriate reliance on cost minimisation while failing to recognise that decisions should be based on expected costs versus benefits; and (b) offer no guidance on how the probabilities associated with these areas could be used in decision-making. Furthermore, Nuijten's model for Parkinson's disease is over-simplified to the point of providing a bad example of modelling practice, which may mislead the readers of PharmacoEconomics. The rationale for this paper is to ensure that readers do not apply inappropriate analyses as a result of following the proposals contained in Nuijten's paper. In addition to a detailed critique of Nuijten's proposals, we provide brief summaries of the currently accepted best practice in cost-effectiveness decision-making under uncertainty.

Antiparkinson Agents↗
PubMed2004

The ProActive trial protocol - a randomised controlled trial of the efficacy of a family-based, domiciliary intervention programme to increase physical activity among individuals at high risk of diabetes [ISRCTN61323766].

BACKGROUND: Increasing prevalence of obesity and disorders associated with sedentary living constitute a major global public health problem. While previous evaluations of interventions to increase physical activity have involved communities or individuals with established disease, less attention has been given to interventions for individuals at risk of disease. METHODS/DESIGN: ProActive aims to evaluate the efficacy of a theoretical, evidence- and family-based intervention programme to increase physical activity in a sedentary population, defined as being at-risk through having a parental family history of diabetes. Primary care diabetes or family history registers were used to recruit 365 individuals aged 30-50 years, screened for activity level. Participants were assigned by central randomisation to three intervention programmes: brief written advice (comparison group), or a psychologically based behavioural change programme, delivered either by telephone (distance group) or face-to-face in the family home over one year. The protocol-driven intervention programme is delivered by trained facilitators, and aims to support increases in physical activity through the introduction and facilitation of a range of self-regulatory skills (e.g. goal setting). The primary outcome is daytime energy expenditure and its ratio to resting energy expenditure, measured at baseline and one year using individually calibrated heart rate monitoring. Secondary measures include self-report of individual and family activity, psychological mediators of behaviour change, physiological and biochemical correlates, acceptability, and costs, measured at baseline, six months and one year. The primary intention to treat analysis will compare groups at one-year post randomisation. Estimation of the impact on diabetes incidence will be modelled using data from a parallel ten-year cohort study using similar measures. DISCUSSION: ProActive is the first efficacy trial of an intervention programme to promote physical activity in a defined high-risk group accessible through primary care. The intervention programme is based on psychological theory and evidence; it introduces and facilitates the use of self-regulatory skills to support behaviour change and maintenance. The trial addresses a range of methodological weaknesses in the field by careful specification and quality assurance of the intervention programme, precise characterisation of participants, year-long follow-up and objective measurement of physical activity. Due to report in 2005, ProActive will provide estimates of the extent to which this approach could assist at-risk groups who could benefit from changes in behaviours affecting health, and inform future pragmatic trials.

Adult↗
PubMed2004

Use and misuse of process and outcome data in managing performance of acute medical care: avoiding institutional stigma.

The history of monitoring the outcomes of health care by external agencies can be traced to ancient times. However, the danger, now as then, is that in the search for improvement, comparative measures of mortality and morbidity are often overinterpreted, resulting in judgments about the underlying quality of care. Such judgments can translate into performance management strategies in the form of capricious sanctions (such as star ratings) and unjustified rewards (such as special freedoms or financial allocations). The resulting risk of stigmatising an entire institution injects huge tensions into health-care organisations and can divert attention from genuine improvement towards superficial improvement or even gaming behaviour (ie, manipulating the system). These dangers apply particularly to measures of outcome and throughput. We argue that comparative outcome data (league tables) should not be used by external agents to make judgments about quality of hospital care. Although they might provide a reasonable measure of quality in some high-risk surgical situations, they have little validity in acute medical settings. Their use to support a system of reward and punishment is unfair and, unsurprisingly, often resisted by clinicians and managers. We argue further that although outcome data are useful for research and monitoring trends within an organisation, those who wish to improve care for patients and not penalise doctors and managers, should concentrate on direct measurement of adherence to clinical and managerial standards.

Critical Care↗
PubMed2003

Ranking institutions.

Explore the source record for details and available documents.

Academies and Institutes↗
PubMed2003

Risk-adjusted sequential probability ratio tests: applications to Bristol, Shipman and adult cardiac surgery.

OBJECTIVE: To investigate the use of the risk-adjusted sequential probability ratio test in monitoring the cumulative occurrence of adverse clinical outcomes. DESIGN: Retrospective analysis of three longitudinal datasets. SUBJECTS: Patients aged 65 years and over under the care of Harold Shipman between 1979 and 1997, patients under 1 year of age undergoing paediatric heart surgery in Bristol Royal Infirmary between 1984 and 1995, adult patients receiving cardiac surgery from a team of cardiac surgeons in London,UK. MAIN OUTCOME MEASURE: Annual and 30-day mortality rates. RESULTS: Using reasonable boundaries, the procedure could have indicated an 'alarm' in Bristol after publication of the 1991 Cardiac Surgical Register, and in 1985 or 1997 for Harold Shipman depending on the data source and the comparator. The cardiac surgeons showed no significant deviation from expected performance. CONCLUSIONS: The risk-adjusted sequential probability test is simple to implement, can be applied in a variety of contexts, and might have been useful to detect specific instances of past divergent performance. The use of this and related techniques deserves further attention in the context of prospectively monitoring adverse clinical outcomes.

Adult↗
PubMed2002

Bayesian analysis of population PK/PD models: general concepts and software.

Markov chain Monte Carlo (MCMC) techniques have revolutionized the field of Bayesian statistics by enabling posterior inference for arbitrarily complex models. The now widely used WinBUGS software has, over the years, made the methodology accessible to a great many applied scientists, in all fields of research. Despite this, serious application of MCMC methods within the field of population PK/PD has been comparatively limited. We appreciate that for many applied pharmacokineticists the prospect of conducting a Bayesian analysis will require numerous alien concepts to be taken on board and it may be difficult to justify investing the time and effort required in order to understand them (especially since the approach is so computer-intensive). For this reason we provide here a thorough (but often informal) discussion of all aspects of Bayesian inference as they apply specifically to population PK/PD. We also acknowledge that while the WinBUGS software is general purpose, model specification for some types of problem, population PK/PD being a prime example, can be very difficult, to the extent that a specialized interface for describing the problem at hand is often a practical necessity. In the latter part of this paper we describe such an interface, namely PKBugs. A principal aim of the paper is to offer sufficient technical background, in an easy to follow format, that the reader may develop both the confidence and know-how to make appropriate use of the PKBugs/WinBUGS framework (or similar software) for their own data analysis needs, should they choose to adopt a Bayesian approach.

Bayes Theorem↗