The Bayesian approach involves synthesising data and judgement in order to reach conclusions about unknown quantities and make predictions. Bayesian methods have become increasingly popular in recent years, notably in medical research, and although there are a number of books on Bayesian analysis, few cover clinical trials and biostatistical applications in any detail. Bayesian Approaches to Clinical Trials and Health-Care Evaluation provides a valuable overview of this rapidly evolving field, including basic Bayesian ideas, prior distributions, clinical trials, observational studies, evidence synthesis and cost-effectiveness analysis. Covers a broad array of essential topics, building from the basics to more advanced techniques.

Bayesian Approaches to Clinical Trials and Health-Care Evaluation: Spiegelhalter/Clinical Trials and Health-Care Evaluation

Risk adjustment for measuring health care outcomes

Summary. A striking feature of UK public services in the 1990s was the rise of performance monitoring (PM), which records, analyses and publishes data in order to give the public a better idea of how Government policies change the public services and to improve their effectiveness.



PM done well is broadly productive for those concerned. Done badly, it can be very costly and not merely ineffective but harmful and indeed destructive.



Performance indicators (PIs) for the public services have typically been designed to assess the impact of Government policies on those services, or to identify well performing or underperforming institutions and public servants. PIs’ third role, which is the public accountability of Ministers for their stewardship of the public services, deserves equal recognition. Hence, Government is both monitoring the public services and being monitored by PIs.



Especially because of the Government's dual role, PM must be done with integrity and shielded from undue political influence, in the way that National Statistics are shielded. It is in everyone's interest that Ministers, Parliament, the professions, practitioners and the wider public can have confidence in the PM process, and find the conclusions from it convincing.



Before introducing PM in any public service, a PM protocol should be written. This is an orderly record not only of decisions made but also of the reasoning or calculations that led to those decisions. A PM protocol should cover objectives, design considerations and the definition of PIs, sampling versus complete enumeration, the information to be collected about context, the likely perverse behaviours or side-effects that might be induced as a reaction to the monitoring process, and also the practicalities of implementation. Procedures for data collection, analysis, presentation of uncertainty and adjustment for context, together with dissemination rules, should be explicitly defined and reflect good statistical practice. Because of their usually tentative nature, PIs should be seen as ‘screening devices’ and not overinterpreted. If quantitative performance targets are to be set, they need to have a sound basis, take account of prior (and emerging) knowledge about key sources of variation, and be integral to the PM design.



Aspirational targets have a distinctive role, but one which is largely irrelevant in the design of a PM procedure; motivational targets which are not rationally based may demoralize and distort. Anticipated and actual side-effects of PM, including on individuals’ behaviour and priorities, may need to be monitored as an intrinsic part of the PM process.



Independent scrutiny of PM schemes for the public services should be set up and must report publicly. The extent and nature of this scrutiny should be related to the assessed drawbacks and benefits, reflect ethical concerns, and conform with good statistical practice.



Research is needed into the merits of different strategies for identifying institutions or individuals in the public release of PM data, into how new PM schemes should be evaluated, and into efficient designs for evaluating a series of new policies which are monitored by PIs.



The Royal Statistical Society considers that attempts to educate the wider public, as well as policy makers, about the issues surrounding the use of PIs are very important. High priority should be given to sponsoring well-informed public debate, and to disseminating good practices by implementing them across Government.

/pdf/performance-indicators-good-bad-and-ugly-200viwkudy.pdf

Performance indicators: good, bad, and ugly

https://www.annalsthoracicsurgery.org/article/S0003-4975(01)03222-2/pdf

Cardiac surgery report cards: comprehensive review and statistical critique

From individual level data for an entire cohort of undergraduate students in the 'old' universities in the UK, we use a binomial probit model to estimate the probability that an individual will 'drop out' of university before the completion of their degree course. We examine the cohort of students enrolling full time for a 3- or 4-year degree in the academic year 1989-1990. We find evidence to support both the hypothesis that the completion of courses by students is influenced by the extent of prior academic preparedness and the hypothesis that social integration at university is important. We also find an influence of unemployment in the county of prior residence, especially for poorer male students. Finally, we draw conclusions regarding the public policy of constructing university performance indicators in this area.

https://www.jstor.org/stable/pdfplus/2680559.pdf

Dropping out of university: A statistical analysis of the probability of withdrawal for UK university students

League tables and their limitations: Statistical issues in comparisons of institutional performance - Discussion

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League tables and their limitations: Statistical issues in comparisons of institutional performance - Discussion