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Opportunities for Bayesian analysis in evaluation of health-care interventions David Spiegelhalter

Opportunities for Bayesian analysis in evaluation of health-care interventions David Spiegelhalter MRC Biostatistics Unit Cambridge david.spiegelhalter@mrc-bsu.cam.ac.uk. Summary. What is the Bayesian approach? Example: CHART Why is it relevant to evaluation in health-care?

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Opportunities for Bayesian analysis in evaluation of health-care interventions David Spiegelhalter

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  1. Opportunities for Bayesian analysis in evaluation of health-care interventions David Spiegelhalter MRC Biostatistics Unit Cambridge david.spiegelhalter@mrc-bsu.cam.ac.uk

  2. Summary • What is the Bayesian approach? • Example: CHART • Why is it relevant to evaluation in health-care? • Example: HIPS • What areas might benefit most? • Example: ASTIN • What are key challenges?

  3. What is the Bayesian approach? A possible definition. ‘the explicit quantitative use of externalevidence in the design, monitoring, analysis, interpretation andreporting of a health-care evaluation’ But what does this mean?

  4. Basic Bayesian ideas • Uncertainty about unknown quantities expressed as a probability distribution • This ‘prior’ distribution is a judgement based on all available evidence • Bayes theorem provides a formal way of revising this distribution as more evidence accumulates “Posterior  prior x likelihood”

  5. CHART trial in non small-cell lung cancer The Data Monitoring Committee met annually and was presented with full data. Date No patients No deaths Observed hazard ratio 95% CI 2-sided P-value 1992 256 78 0.55 (0.35 to 0.86) 0.007 1993 380 192 0.63 (0.47 to 0.83) 0.001 1994 460 275 0.70 (0.55 to 0.90) 0.003 1995 563 379 0.75 (0.61 to 0.93) 0.004 1996 563 444 0.76 (0.63 to 0.90) 0.003

  6. CHART Lung trial results

  7. Why is it relevant to evaluation in health-care? • Can incorporate all relevant evidence in an incremental way • Can model potential biases in studies • Answers question: how should new evidence change our opinions? • Directly make statements such as: “Probability that X is cost-effective is 92%” • Inference feeds naturally into decision-making and planning further studies • Requires explicit, accountable judgments, recognising context and multiple stakeholders

  8. Comparison of Charnley and Stanmore hip prosthesis (NICE, 2000)

  9. What areas might benefit most? • Planning and monitoring development programmes • Selection of compounds for further investigation • Data monitoring within studies • Adaptive designs in proof-of-concept studies • Evidence synthesis • Cost-effectiveness analysis • Value-of-information (payback) models

  10. ASTIN study • Adaptive dose-response study of UK-279,276 in acute ischaemic stroke (Krams, Lees, Hacke, Grieve, Orgogozo, Ford etc (2003) • 15 doses available: placebo, 10 - 120 mg • Primary outcome: increase in Scandinavian Stroke Scale (SSS) at 90 days (adjusted for baseline) • Next dose suggested is that which minimises the expected variance of the response at the ED95 (minimal dose near maximal efficacy) • Randomisation: 15\% to placebo, 85\% `near' suggested dose • Fits smoothly flexible curve: no imposed shape • IDMC examined data every week • Stop for efficacy when 90% probability that effect at ED95 > 2 • Stop for futility when 90% probability that effect at ED95 < 1 • Design approved by FDA (based on simulation studies) • Stopped by IDMC for futility after 966 patients randomised

  11. Changing dosing pattern

  12. Final dose-effect curve

  13. Doses finally given

  14. Monitoring changing probabilities

  15. What are key challenges? • Marshalling appropriate evidence • Robust, rigorous modeling with appropriate sensitivity analysis • Presentation in persuasive way to decision-makers in companies and regulatory authorities • Integration of cost-effectiveness ideas into product-development programmes BUT Cannot make silk purse …., so need good studies and good data

  16. References Berry DA, Mueller P, Grieve AP, Smith M, Parke T, Blazek R, Mitchard N and Krams M (2001) Adaptive Bayesian designs for dose-ranging drug trials. Case Studies in Bayesian Statistics, Volume V. Eds Gatsonis C, Carlin B and Carriquiry A. Springer-Verlag, New York. p 99-181 O'Hagan A Luce BR (2003) APrimer on Bayesian Statistics in Health Economics. Centre for Bayesian Statistics in Health Economics, Sheffield Parmar MKB, Griffiths GO, Spiegelhalter DJ, Souhami RL, Altman DG and van der Scheuren E (2001) Monitoring large randomised clinical trials - a new approach using Bayesian methods, Lancet, 358, 375—381 Spiegelhalter DJ, Abrams K, and Myles JP. Bayesian Approaches to Clinical Trials and Health Care Evaluation. Wiley, Chichester, 2004. Spiegelhalter DJ and Best NG (2003) Bayesian methods for evidence synthesis and complex cost-effectiveness models: an example in hip prostheses. Statistics in Medicine,22, 000-000

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