Title Evidence synthesis for decision making in healthcare / Nicky J. Welton [and others]

Published Chichester, West Sussex : Wiley, 2012

Click on the following:
ProQuest Ebook Central

Copies

Description 1 online resource Series Statistics in practice Statistics in practice. Contents 880-01 Introduction -- Bayesian methods and winBUGS -- Introduction to decision models -- Meta-analysis using Bayesian methods -- Exploring between study heterogeneity -- Model critique and evidence consistency in random effects meta-analysis -- Evidence synthesis in a decision modelling framework -- Multi-parameter evidence synthesis in epidemiological models -- Mixed treatment comparisons -- Markov models -- Generalised evidence synthesis -- Expected value of information for research prioritisation and study design 880-01/(S Machine generated contents note: 1.1. rise of health economics -- 1.2. Decision making under uncertainty -- 1.2.1. Deterministic models -- 1.2.2. Probabilistic decision modelling -- 1.3. Evidence-based medicine -- 1.4. Bayesian statistics -- 1.5. NICE -- 1.6. Structure of the book -- 1.7. Summary key points -- 1.8. Further reading -- References -- 2.1. Introduction to Bayesian methods -- 2.1.1. What is a Bayesian approach-- 2.1.2. Likelihood -- 2.1.3. Bayes' theorem and Bayesian updating -- 2.1.4. Prior distributions -- 2.1.5. Summarising the posterior distribution -- 2.1.6. Prediction -- 2.1.7. More realistic and complex models -- 2.1.8. MCMC and Gibbs sampling -- 2.2. Introduction to WinBUGS -- 2.2.1. BUGS language -- 2.2.2. Graphical representation -- 2.2.3. Running WinBUGS -- 2.2.4. Assessing convergence in WinBUGS -- 2.2.5. Statistical inference in WinBUGS -- 2.2.6. Practical aspects of using WinBUGS -- 2.3. Advantages and disadvantages of a Bayesian approach -- 2.4. Summary key points -- 2.5. Further reading -- 2.6. Exercises -- References -- 3.1. Introduction -- 3.2. Decision tree models -- 3.3. Model parameters -- 3.3.1. Effects of interventions -- 3.3.2. Quantities relating to the clinical epidemiology of the clinical condition being treated -- 3.3.3. Utilities -- 3.3.4. Resource use and costs -- 3.4. Deterministic decision tree -- 3.5. Stochastic decision tree -- 3.5.1. Presenting the results of stochastic economic decision models -- 3.6. Sources of evidence -- 3.7. Principles of synthesis for decision models (motivation for the rest of the book) -- 3.8. Summary key points -- 3.9. Further reading -- 3.10. Exercises -- References -- 4.1. Introduction -- 4.2. Fixed Effect model -- 4.3. Random Effects model -- 4.3.1. predictive distribution -- 4.3.2. Prior specification for τ -- 4.3.3. 'Exact' Random Effects model for Odds Ratios based on a Binomial likelihood -- 4.3.4. Shrunken study level estimates -- 4.4. Publication bias -- 4.5. Study validity -- 4.6. Summary key points -- 4.7. Further reading -- 4.8. Exercises -- References -- 5.1. Introduction -- 5.2. Random effects meta-regression models -- 5.2.1. Generic random effect meta-regression model -- 5.2.2. Random effects meta-regression model for Odds Ratio (OR) outcomes using a Binomial likelihood -- 5.2.3. Autocorrelation and centring covariates -- 5.3. Limitations of meta-regression -- 5.4. Baseline risk -- 5.4.1. Model for including baseline risk in a meta-regression on the (log) OR scale -- 5.4.2. Final comments on including baseline risk as a covariate -- 5.5. Summary key points -- 5.6. Further reading -- 5.7. Exercises -- References -- 6.1. Introduction -- 6.2. Random Effects model revisited -- 6.3. Assessing model fit -- 6.3.1. Deviance -- 6.3.2. Residual deviance -- 6.4. Model comparison -- 6.4.1. Effective number of parameters, pD -- 6.4.2. Deviance Information Criteria -- 6.5. Exploring inconsistency -- 6.5.1. Cross-validation -- 6.5.2. Mixed predictive checks -- 6.6. Summary key points -- 6.7. Further reading -- 6.8. Exercises -- References -- 7.1. Introduction -- 7.2. Evaluation of decision models: One-stage vs two-stage approach -- 7.3. Sensitivity analyses (of model inputs and model specifications) -- 7.4. Summary key points -- 7.5. Further reading -- 7.6. Exercises -- References -- 8.1. Introduction -- 8.2. Prior and posterior simulation in a probabilistic model: Maple Syrup Urine Disease (MSUD) -- 8.3. model for prenatal HIV testing -- 8.4. Model criticism in multi-parameter models -- 8.5. Evidence-based policy -- 8.6. Summary key points -- 8.7. Further reading -- 8.8. Exercises -- References -- 9.1. Why go beyond 'direct' head-to-head trials-- 9.2. fixed treatment effects model for MTC -- 9.2.1. Absolute treatment effects -- 9.2.2. Relative treatment efficacy and ranking -- 9.3. Random Effects MTC models -- 9.4. Model choice and consistency of MTC evidence -- 9.4.1. Techniques for presenting and understanding the results of MTC -- 9.5. Multi-arm trials -- 9.6. Assumptions made in mixed treatment comparisons -- 9.7. Embedding an MTC within a cost-effectiveness analysis -- 9.8. Extension to continuous, rate and other outcomes -- 9.9. Summary key points -- 9.10. Further reading -- 9.11. Exercises -- References -- 10.1. Introduction -- 10.2. Continuous and discrete time Markov models -- 10.3. Decision analysis with Markov models -- 10.3.1. Evaluating Markov models -- 10.4. Estimating transition parameters from a single study -- 10.4.1. Likelihood -- 10.4.2. Priors and posteriors for multinomial probabilities -- 10.5. Propagating uncertainty in Markov parameters into a decision model -- 10.6. Estimating transition parameters from a synthesis of several studies -- 10.6.1. Challenges for meta-analysis of evidence on Markov transition parameters -- 10.6.2. relationship between probabilities and rates -- 10.6.3. Modelling study effects -- 10.6.4. Synthesis of studies reporting aggregate data -- 10.6.5. Incorporating studies that provide event history data -- 10.6.6. Reporting results from a Random Effects model -- 10.6.7. Incorporating treatment effects -- 10.7. Summary key points -- 10.8. Further reading -- 10.9. Exercises -- References -- 11.1. Introduction -- 11.2. Deriving a prior distribution from observational evidence -- 11.3. Bias allowance model for the observational data -- 11.4. Hierarchical models for evidence from different study designs -- 11.5. Discussion -- 11.6. Summary key points -- 11.7. Further reading -- 11.8. Exercises -- References -- 12.1. Introduction -- 12.2. Expected value of perfect information -- 12.3. Expected value of partial perfect information -- 12.3.1. Computation -- 12.3.2. Notes on EVPPI -- 12.4. Expected value of sample information -- 12.4.1. Computation -- 12.5. Expected net benefit of sampling -- 12.6. Summary key points -- 12.7. Further reading -- 12.8. Exercises -- References -- A2.1. Normal distribution -- A2.2. Binomial distribution -- A2.3. Multinomial distribution -- A2.4. Uniform distribution -- A2.5. Exponential distribution -- A2.6. Gamma distribution -- A2.7. Beta distribution -- A2.8. Dirichlet distribution Summary In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish interventions that are both effective and cost-effective. Usually a single study will not fully address these issues and it is desirable to synthesize evidence from multiple sources. This book aims to provide a practical guide to evidence synthesis for the purpose of decision making, starting with a simple single parameter model, where all studies estimate the same quantity (pairwise meta-analysis) and progressing to more complex multi-parameter structures (including meta-regression, mixe Bibliography Includes bibliographical references and index Notes Print version record and CIP data provided by publisher Subject Health services administration. Evidence-based medicine. Bayesian statistical decision theory. Decision making -- Mathematical models. Statistics. Decision Support Techniques Statistics as Topic Evidence-Based Medicine -- economics Models, Statistical Patient Care Management -- methods Patient Care Management Health Services Administration Evidence-Based Medicine Evidence-Based Practice statistics. MEDICAL -- Administration. MEDICAL -- Practice Management & Reimbursement. Statistics Decision making -- Mathematical models Bayesian statistical decision theory Evidence-based medicine Health services administration Form Electronic book Author Welton, Nicky J. LC no. 2012000005 ISBN 9781118305409 111830540X 9781118305416 1118305418 9781119942979 1119942977 9781119942986 1119942985 047006109X 9780470061091 9781280590948 1280590947

  Permalink