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Review
. 1999 Aug 21;319(7208):508-12.
doi: 10.1136/bmj.319.7208.508.

Methods in health service research. An introduction to bayesian methods in health technology assessment

D J Spiegelhalter  1 , J P Myles, D R Jones, K R Abrams
Affiliations
Review

Methods in health service research. An introduction to bayesian methods in health technology assessment

D J Spiegelhalter et al. BMJ. .
No abstract available

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Figures

Figure 1
Figure 1
Prior (a), likelihood (b), and posterior (c) distributions arising from reanalysis by Pocock and Spiegelhalter of the GREAT trial of home thrombolysis. The prior distribution represents a summary of evidence external to the trial, the likelihood expresses evidence from the trial itself, and the posterior distribution pools these two sources by multiplying the two curves together
Figure 2
Figure 2
Prior, likelihood, and posterior distributions arising from Cancer and Leukaemia Group B trial of standard radiotherapy versus additional chemotherapy in advanced lung cancer. Dashed lines give boundaries of range of clinical equivalence, taken to be 0 and 4 months median improvement in survival. Numbers by each graph show probabilities of lying below, within, and above the range of equivalence
Figure 3
Figure 3
Traditional and bayesian estimates of standardised treatment effects in a randomised trial of treatments for cancer. The bayesian estimates are pulled towards the overall treatment effect by a degree determined by the empirical heterogeneity of the subset results
See this image and copyright information in PMC

Comment in

References

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