References
- Ades, A. , Lu, G. , and Claxton, K . (2004), “Expected Value of Sample Information Calculations in Medical Decision Modeling,” Medical Decision Making , 24, 207–227. DOI:https://doi.org/10.1177/0272989X04263162.
- Ades, A. E. , and Sutton, A. J . (2006), “Multiparameter Evidence Synthesis in Epidemiology and Medical Decision-Making: Current Approaches,” Journal of the Royal Statistical Society, Series A, 169, 5–35. DOI:https://doi.org/10.1111/j.1467-985X.2005.00377.x.
- Aghaizu, A. , Wayal, S. , Nardone, A. , Parsons, V. , Copas, A. , Mercey, D. , Hart, G. , Gilson, R. , and Johnson, A . (2016), “Sexual Behaviours, HIV Testing, and the Proportion of Men at Risk of Transmitting and Acquiring HIV in London, UK, 2000–13: A Serial Cross-Sectional Study,” The Lancet HIV , 3, e431–e440. DOI:https://doi.org/10.1016/S2352-3018(16)30037-6.
- Baggaley, R. F. , Irvine, M. A. , Leber, W. , Cambiano, V. , Figueroa, J. , McMullen, H. , Anderson, J. , Santos, A. C. , Terris-Prestholt, F. , Miners, A. , Hollingsworth, D. , and Griffiths, C. J . (2017), “Cost-effectiveness of Screening for HIV in Primary Care: A Health Economics Modelling Analysis,” The Lancet HIV , 4, e465–e474. DOI:https://doi.org/10.1016/S2352-3018(17)30123-6.
- Berger, J. O . (2013), Statistical Decision Theory and Bayesian Analysis , New York, NY: Springer.
- Bernardo, J. M. , and Smith, A. F. M . (1994), Bayesian Theory , Chichester: Wiley.
- Briggs, A. , Sculpher, M. , and Claxton, K . (2006), Decision Modelling for Health Economic Evaluation, Handbooks in Health Economic Evaluation. Oxford: Oxford University Press.
- Carmona, C. , O’Rourke, D. , and Robinson, S . (2016), “HIV Testing: Increasing Uptake Among People Who May Have Undiagnosed HIV. Evidence Review on the Most Cost Effective Ways to Increase the Uptake of HIV Testing to Reduce Undiagnosed HIV Among People Who May Have Been Exposed to It,” available at https://www.nice.org.uk/guidance/ng60/documents/evidence-review-5
- Chaloner, K. , and Verdinelli, I . (1995), “Bayesian Experimental Design: A Review,” Statistical Science , 273–304. DOI:https://doi.org/10.1214/ss/1177009939.
- Claxton, K. P. , and Sculpher, M. J . (2006), “Using Value of Information Analysis to Prioritise Health Research,” Pharmacoeconomics , 24, 1055–1068. DOI:https://doi.org/10.2165/00019053-200624110-00003.
- De Angelis, D. , Presanis, A. M. , Conti, S. , and Ades, A. E . (2014), “Estimation of HIV Burden Through Bayesian Evidence Synthesis,” Statistical Science, 29, 9–17. DOI:https://doi.org/10.1214/13-STS428.
- Felli, J. C. , and Hazen, G. B . (1998), “Sensitivity Analysis and the Expected Value of Perfect Information,” Medical Decision Making , 18, 95–109. DOI:https://doi.org/10.1177/0272989X9801800117.
- Friedman, J. H . (1991), “Multivariate Adaptive Regression Splines,” The Annals of Statistics , 19, 1–67. DOI:https://doi.org/10.1214/aos/1176347963.
- Goubar, A. , Ades, A. E. , DeAngelis, D. , McGarrigle, C. A. , Mercer, C. H. , Tookey, P. A. , Fenton, K. , and Gill, O. N . (2008), “Estimates of Human Immunodeficiency Virus Prevalence and Proportion Diagnosed Based on Bayesian Multiparameter Synthesis of Surveillance Data,” Journal of the Royal Statistical Society, Series A, 171, 541–580. DOI:https://doi.org/10.1111/j.1467-985X.2007.00537.x.
- Han, C. , and Chaloner, K . (2004), “Bayesian Experimental Design for Nonlinear Mixed-Effects Models With Application to HIV Dynamics,” Biometrics , 60, 25–33. DOI:https://doi.org/10.1111/j.0006-341X.2004.00148.x.
- Heath, A. , Manolopoulou, I. , and Baio, G . (2016), “Estimating the Expected Value of Partial Perfect Information in Health Economic Evaluations Using Integrated Nested Laplace Approximation,” Statistics in Medicine , 35, 4264–4280. DOI:https://doi.org/10.1002/sim.6983.
- Kirwan, P. , Chau, C. , Brown, A. , Gill, O. , Delpech, V. , and contributors (2016), “HIV in the UK — 2016 Report,” Technical report, Public Health England, London.
- Lamboni, M. , Monod, H. , and Makowski, D . (2011), “Multivariate Sensitivity Analysis to Measure Global Contribution of Input Factors in Dynamic Models,” Reliability Engineering & System Safety , 96, 450–459. DOI:https://doi.org/10.1016/j.ress.2010.12.002.
- Lauritzen, S. L . (1996), Graphical Models (Vol. 17), Oxford, UK: Clarendon Press.
- Lindley, D. V . (1956), “On a Measure of the Information Provided by an Experiment,” The Annals of Mathematical Statistics , 986–1005. DOI:https://doi.org/10.1214/aoms/1177728069.
- Madan, J. , Ades, A. E. , Price, M. , Maitland, K. , Jemutai, J. , Revill, P. , and Welton, N. J . (2014), “Strategies for Efficient Computation of the Expected Value of Partial Perfect Information,” Medical Decision Making , 34, 327–342. DOI:https://doi.org/10.1177/0272989X13514774.
- Mandel, M . (2013), “Simulation-based Confidence Intervals for Functions With Complicated Derivatives,” The American Statistician , 67(2), 76–81. DOI:https://doi.org/10.1080/00031305.2013.783880.
- Menzies, N. A . (2016), “An Efficient Estimator for the Expected Value of Sample Information,” Medical Decision Making , 36, 308–320. DOI:https://doi.org/10.1177/0272989X15583495.
- Mercer, C. , Tanton, C. , Prah, P. , Erens, B. , Sonnenberg, P. , Clifton, S. , Macdowall, W. , Lewis, R. , Field, N. , Datta, J. , Copas, A. , Phelps, A. , Wellings, K. , and Johnson, A . (2013), “Changes in Sexual Attitudes and Lifestyles in Britain Through the Life Course and Over Time: Findings From the National Surveys of Sexual Attitudes and Lifestyles (Natsal),” Lancet , 382, 1781–1794. DOI:https://doi.org/10.1016/S0140-6736(13)62035-8.
- Milborrow, S . (2011), earth: Multivariate Adaptive Regression Splines. R package. Derived from mda:mars by T. Hastie and R. Tibshirani. Available at http://CRAN.R-project.org/package=earth
- Neuenschwander, B. , Branson, M. , and Spiegelhalter, D. J . (2009), “A Note on the Power Prior,” Statistics in Medicine , 28, 3562–3566. DOI:https://doi.org/10.1002/sim.3722.
- Oakley, J. E. , and O’Hagan, A . (2004), “Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach,” Journal of the Royal Statistical Society, Series B, 66, 751–769. DOI:https://doi.org/10.1111/j.1467-9868.2004.05304.x.
- Office for National Statistics (2012), “Mid-year Population Estimates,” available at https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates
- Parmigiani, G. , and Inoue, L . (2009), Decision Theory: Principles and Approaches , Chichester, UK: Wiley.
- Presanis, A. M. , Gill, O. N. , Chadborn, T. R. , Hill, C. , Hope, V. , Logan, L. , Rice, B. D. , Delpech, V. C. , Ades, A. E. , and De Angelis, D . (2010), “Insights Into the Rise in HIV Infections, 2001 to 2008: A Bayesian Synthesis of Prevalence Evidence,” AIDS (London, England) , 24, 2849–2858. DOI:https://doi.org/10.1097/QAD.0b013e32834021ed.
- Public Health England, London (2012), “UA Survey of Genitourinary Medicine (GUM) Clinic Attendees (GUM Anon Survey),” available at https://www.gov.uk/guidance/hiv-overall-prevalence\#ua-survey-of-genitourinary-medicine-gum-clinic-attendees-gum-anon-survey
- Raiffa, H. , and Schlaifer, R . (1961), Applied Statistical Decision Theory , Cambridge, MA: Harvard University.
- Roos, M. , Martins, T. G. , Held, L. , and Rue, H . (2015), “Sensitivity Analysis for Bayesian Hierarchical Models,” Bayesian Analysis , 10, 321–349. DOI:https://doi.org/10.1214/14-BA909.
- Ryan, E. G. , Drovandi, C. C. , McGree, J. M. , and Pettitt, A. N . (2016), “A Review of Modern Computational Algorithms for Bayesian Optimal Design,” International Statistical Review , 84, 128–154. DOI:https://doi.org/10.1111/insr.12107.
- Saltelli, A. , Tarantola, S. , Campolongo, F. , and Ratto, M . (2004), Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , Chichester, UK: Wiley.
- Sobol’, I. M . (2001), “Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates,” Mathematics and Computers in Simulation , 55, 271–280.
- Stan Development Team (2016), Stan Modeling Language Users Guide and Reference Manual, Version 2.14.0. Available at http://mc-stan.org
- Strong, M. , and Oakley, J. E . (2014), “When is a Model Good Enough? Deriving the Expected Value of Model Improvement Via Specifying Internal Model Discrepancies,” SIAM/ASA Journal on Uncertainty Quantification , 2, 106–125. DOI:https://doi.org/10.1137/120889563.
- Strong, M. , Oakley, J. , and Chilcott, J . (2012), “Managing Structural Uncertainty in Health Economic Decision Models: A Discrepancy Approach,” Journal of the Royal Statistical Society, Series C, 61, 25–45. DOI:https://doi.org/10.1111/j.1467-9876.2011.01014.x.
- Strong, M. , Oakley, J. E. , and Brennan, A . (2014), “Estimating Multiparameter Partial Expected Value of Perfect Information From a Probabilistic Sensitivity Analysis Sample: A Nonparametric Regression Approach,” Medical Decision Making , 34, 311–326. DOI:https://doi.org/10.1177/0272989X13505910.
- Strong, M. , Oakley, J. E. , Brennan, A. , and Breeze, P . (2015), “Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample: A Fast, Nonparametric Regression-based Method,” Medical Decision Making , 35, 570–83. DOI:https://doi.org/10.1177/0272989X15575286.
- Welton, N. , Ades, A. , Caldwell, D. , and Peters, T . (2008), “Research Prioritization Based on Expected Value of Partial Perfect Information: A Case-Study on Interventions to Increase Uptake of Breast Cancer Screening,” Journal of the Royal Statistical Society, Series A, 171, 807–841. DOI:https://doi.org/10.1111/j.1467-985X.2008.00558.x.
- Willan, A. R. , and Pinto, E. M . (2005), “The Value of Information and Optimal Clinical Trial Design,” Statistics in Medicine , 24, 1791–1806. DOI:https://doi.org/10.1002/sim.2069.