Bayesian Approaches to Clinical Trials and Health Care Evaluation [Other digital carrier]

Preface. List of examples.
1. Introduction. 1.1 What are Bayesian
methods? 1.2 What do we mean by 'health-care evaluation'? 1.3 A Bayesian
approach to evaluation. 1.4 The aim of this book and the intended audience.
1.5 Structure of the book.
2. Basic Concepts from Traditional Statistical
Analysis. 2.1 Probability. 2.1.1 What is probability? 2.1.2 Odds and
log-odds. 2.1.3 Bayes theorem for simple events. 2.2 Random variables,
parameters and likelihood. 2.2.1 Random variables and their distributions.
2.2.2 Expectation, variance, covariance and correlation. 2.2.3 Parametric
distributions and conditional independence. 2.2.4 Likelihoods. 2.3 The normal
distribution. 2.4 Normal likelihoods. 2.4.1 Normal approximations for binary
data. 2.4.2 Normal likelihoods for survival data. 2.4.3 Normal likelihoods
for count responses. 2.4.4 Normal likelihoods for continuous responses. 2.5
Classical inference. 2.6 A catalogue of useful distributions. 2.6.1 Binomial
and Bernoulli. 2.6.2 Poisson. 2.6.3 Beta. 2.6.4 Uniform. 2.6.5 Gamma. 2.6.6
Root-inverse-gamma. 2.6.7 Half-normal. 2.6.8 Log-normal. 2.6.9 Student's t.
2.6.10 Bivariate normal. 2.7 Key points. Exercises.
3. An Overview of the
Bayesian Approach. 3.1 Subjectivity and context. 3.2 Bayes theorem for two
hypotheses. 3.3 Comparing simple hypotheses: likelihood ratios and Bayes
factors. 3.4 Exchangeability and parametric modelling. 3.5 Bayes theorem for
general quantities. 3.6 Bayesian analysis with binary data. 3.6.1 Binary data
with a discrete prior distribution. 3.6.2 Conjugate analysis for binary data.
3.7 Bayesian analysis with normal distributions. 3.8 Point estimation,
interval estimation and interval hypotheses. 3.9 The prior distribution. 3.10
How to use Bayes theorem to interpret trial results. 3.11 The 'credibility'
of significant trial results. 3.12 Sequential use of Bayes theorem. 3.13
Predictions. 3.13.1 Predictions in the Bayesian framework. 3.13.2 Predictions
for binary data. 3.13.3 Predictions for normal data. 3.14 Decision-making.
3.15 Design. 3.16 Use of historical data. 3.17 Multiplicity, exchangeability
and hierarchical models. 3.18 Dealing with nuisance parameters. 3.18.1
Alternative methods for eliminating nuisance parameters. 3.18.2 Profile
likelihood in a hierarchical model. 3.19 Computational issues. 3.19.1 Monte
Carlo methods. 3.19.2 Markov chain Monte Carlo methods. 3.19.3 WinBUGS. 3.20
Schools of Bayesians. 3.21 A Bayesian checklist. 3.22 Further reading. 3.23
Key points. Exercises.
4. Comparison of Alternative Approaches to Inference.
4.1 A structure for alternative approaches. 4.2 Conventional statistical
methods used in health-care evaluation. 4.3 The likelihood principle,
sequential analysis and types of error. 4.3.1 The likelihood principle. 4.3.2
Sequential analysis. 4.3.3 Type I and Type II error. 4.4 P-values and Bayes
factors. 4.4.1 Criticism of P-values. 4.4.2 Bayes factors as an alternative
to P-values: simple hypotheses. 4.4.3 Bayes factors as an alternative to
P-values: composite hypotheses. 4.4.4 Bayes factors in preference studies.
4.4.5 Lindley's paradox. 4.5 Key points. Exercises.
5. Prior Distributions.
5.1 Introduction. 5.2 Elicitation of opinion: a brief review. 5.2.1
Background to elicitation. 5.2.2 Elicitation techniques. 5.2.3 Elicitation
from multiple experts. 5.3 Critique of prior elicitation. 5.4 Summary of
external evidence. 5.5 Default priors. 5.5.1 'Non-informative' or 'reference'
priors: 5.5.2 'Sceptical' priors. 5.5.3 'Enthusiastic' priors. 5.5.4 Priors
with a point mass at the null hypothesis ('lump-and-smear' priors). 5.6
Sensitivity analysis and 'robust' priors. 5.7 Hierarchical priors. 5.7.1 The
judgement of exchangeability. 5.7.2 The form for the random-effects
distribution. 5.7.3 The prior for the standard deviation of the random
effects. 5.8 Empirical criticism of priors. 5.9 Key points. Exercises.
6.
Randomised Controlled Trials. 6.1 Introduction. 6.2 Use of a loss function:
is a clinical trial for inference or decision? 6.3 Specification of null
hypotheses. 6.4 Ethics and randomisation: a brief review. 6.4.1 Is
randomisation necessary? 6.4.2 When is it ethical to randomise? 6.5 Sample
size of non-sequential trials. 6.5.1 Alternative approaches to sample-size
assessment. 6.5.2 'Classical power': hybrid classical-Bayesian methods
assuming normality. 6.5.3 'Bayesian power'. 6.5.4 Adjusting formulae for
different hypotheses. 6.5.5 Predictive distribution of power and necessary
sample size. 6.6 Monitoring of sequential trials. 6.6.1 Introduction. 6.6.2
Monitoring using the posterior distribution. 6.6.3 Monitoring using
predictions: 'interim power'. 6.6.4 Monitoring using a formal loss function.
6.6.5 Frequentist properties of sequential Bayesian methods. 6.6.6 Bayesian
methods and data monitoring committees. 6.7 The role of 'scepticism' in
confirmatory studies. 6.8 Multiplicity in randomised trials. 6.8.1 Subset
analysis. 6.8.2 Multi-centre analysis. 6.8.3 Cluster randomization. 6.8.4
Multiple endpoints and treatments. 6.9 Using historical controls. 6.10
Data-dependent allocation. 6.11 Trial designs other than two parallel groups.
6.12 Other aspects of drug development. 6.13 Further reading. 6.14 Key
points. Exercises.
7. Observational Studies. 7.1 Introduction. 7.2
Alternative study designs. 7.3 Explicit modelling of biases. 7.4
Institutional comparisons. 7.5 Key points. Exercises.
8. Evidence Synthesis.
8.1 Introduction. 8.2 'Standard' meta-analysis. 8.2.1 A Bayesian perspective.
8.2.2 Some delicate issues in Bayesian meta-analysis. 8.2.3 The relationship
between treatment effect and underlying risk. 8.3 Indirect comparison
studies. 8.4 Generalised evidence synthesis. 8.5 Further reading. 8.6 Key
points. Exercises.
9. Cost-effectiveness, Policy-Making and Regulation. 9.1
Introduction. 9.2 Contexts. 9.3 'Standard' cost-effectiveness analysis
without uncertainty. 9.4 'Two-stage' and integrated approaches to uncertainty
in cost-effectiveness modeling. 9.5 Probabilistic analysis of sensitivity to
uncertainty about parameters: two-stage approach. 9.6 Cost-effectiveness
analyses of a single study: integrated approach. 9.7 Levels of uncertainty in
cost-effectiveness models. 9.8 Complex cost-effectiveness models. 9.8.1
Discrete-time, discrete-state Markov models. 9.8.2 Micro-simulation in
cost-effectiveness models. 9.8.3 Micro-simulation and probabilistic
sensitivity analysis. 9.8.4 Comprehensive decision modeling. 9.9 Simultaneous
evidence synthesis and complex cost-effectiveness modeling. 9.9.1 Generalised
meta-analysis of evidence. 9.9.2 Comparison of integrated Bayesian and
two-stage approach. 9.10 Cost-effectiveness of carrying out research: payback
models. 9.10.1 Research planning in the public sector. 9.10.2 Research
planning in the pharmaceutical industry. 9.10.3 Value of information. 9.11
Decision theory in cost-effectiveness analysis, regulation and policy. 9.12
Regulation and health policy. 9.12.1 The regulatory context. 9.12.2
Regulation of pharmaceuticals. 9.12.3 Regulation of medical devices. 9.13
Conclusions. 9.14 Key points. Exercises.
10. Conclusions and Implications for
Future Research. 10.1 Introduction. 10.2 General advantages and problems of a
Bayesian approach. 10.3 Future research and development. Appendix: Websites
and Software. A.1 The site for this book. A.2 Bayesian methods in health-care
evaluation. A.3 Bayesian software. A.4 General Bayesian sites. References.
Index.