References

Akaike, H., 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723.
Alvares, D., Lázaro, E., Gómez-Rubio, V., Armero, C., 2021. Bayesian survival analysis with BUGS. Statistics in Medicine 40, 2975–3020.
Bailey, J., Webster, R., Hunter, R., Griffin, M., Freemantle, N., Rait, G., et al., 2016. The Men’s Safer Sex project: intervention development and feasibility randomised controlled trial of an interactive digital intervention to increase condom use in men. Health Technology Assessment 20, 1.
Baio, G., 2025. Introduction to statistical modelling, in: Baio, G., Thom, H., Pechivanoglou, P. (Eds.), R for Health Technology Assessment. Chapman and Hall/CRC.
Baio, G., 2020. survHE: survival analysis for health economic evaluation and cost-effectiveness modeling. Journal of Statistical Software 95, 1–47.
Baio, G., 2012. Bayesian Methods in Health Economics. CRC Press, Boca Raton, FL.
Baio, G., Heath, A., Berardi, A., Green, N., 2025a. Bayesian Cost-Effectiveness Analysis with the R Package BCEA, 2nd ed. Springer.
Baio, G., Thom, H., Pechlivanoglou, P. eds, 2025b. R for Health Technology Assessment. Chapman and Hall/CRC.
Barnsley, P., Towse, A., Sussex, J., et al., 2013. Critique of CHE research paper 81: Methods for the estimation of the NICE cost effectiveness threshold. Office of Health Economics Research Papers 81.
Bayes, T., 1763. Essay Towards Solving a Problem in the Doctrine of Chances. Philosophical Transactions of the Royal Society of London 53, 370–418.
Beeken, R., Leurent, B., Vickerstaff, V., Wilson, R., Croker, H., Morris, S., et al., 2017. A brief intervention for weight control based on habit-formation theory delivered through primary care: results from a randomised controlled trial. International Journal of Obesity 41, 246–254.
Benaglia, T., Jackson, C., Sharples, L.D., 2015. Survival extrapolation in the presence of cause specific hazards. Statistics in Medicine 34, 796–811.
Bernardo, J., Smith, A., 1999. Bayesian Theory. John Wiley and Sons, New York, NY.
Bertsch McGrayne, S., 2011. The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy. Yale University Press, New Haven, CT.
Betancourt, M., 2018. A Conceptual Introduction to Hamiltonian Monte Carlo. URL https://arxiv.org/abs/1701.02434
Black, W.C., 1990. The CE plane: A graphic representation of cost-effectiveness. Medical Decision Making 10, 212–214.
Blangiardo, M., Cameletti, M., 2015. Spatial and Spatio-temporal Bayesian Models with R-INLA. John Wiley and Sons, Chichester, UK.
Blangiardo, M., Cameletti, M., Baio, G., Rue, H., 2013. Spatial and spatio-temporal models with R-INLA. Spatial and Spatio-temporal Epidemiology 7, 39–55.
Box, G., 1979. Robustness in the strategy of scientific model building, in: Robustness in Statistics. Elsevier, pp. 201–236.
Brennan, A., Kharroubi, S., O’Hagan, A., Chilcott, J., 2007. Calculating partial expected value of perfect information via Monte Carlo sampling algorithms. Medical Decision Making 27, 448–470.
Briggs, A, Sculpher, M., Claxton, K., 2006. Decision Modelling for Health Economic Evaluation. Oxford University Press.
Brooks, S., Gelman, A., 1998. General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7, 434–455.
Brooks, S., Gelman, A., Jones, G., Meng, X., 2011. Handbook of Markov Chain Monte Carlo. Chapman and Hall/CRC.
Bürkner, P.C., 2017. brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software 80, 1–28.
Burnham, K., Anderson, D., 2002. Model Selection and Multimodel Inference, 2nd ed. Springer.
Burnham, K., Anderson, D., 1998. Model Selection and Inference. Springer.
Campbell, H., Margossian, C., Jansen, J., Gustafson, P., 2026. Don’t Disregard the Data for Lack of a Likelihood: Bayesian Synthetic Likelihood for Enhanced Multilevel Network Meta-Regression. URL https://arxiv.org/abs/2603.11019
Carlin, B., Herring, A., 2015. A conversation with Alan Gelfand. Statistical Science 413–422.
Caro, J., Ishak, J., 2010. No head-to-head trial? Simulate the missing arms. PharmacoEconomics 28, 957–967.
Carpenter, B., Gelman, A., Hoffman, M., Lee, D., Goodrich, B., Betancourt, M., et al., 2017. Stan: A probabilistic programming language. Journal of Statistical Software 76.
Carpenter, J., Bartlett, J., Morris, T., Wood, A., Quartagno, M., Kenward, M., 2023. Multiple Imputation and Its Application. John Wiley and Sons, Chichester, UK.
Chancellor, J.V., Hill, A.M., Sabin, C.A., Simpson, K.N., Youle, M., 1997. Modelling the cost effectiveness of lamivudine/zidovudine combination therapy in HIV infection. PharmacoEconomics 12, 54–66.
Che, Z., Green, N., Baio, G., 2023. Blended survival curves: A new approach to extrapolation for time-to-event outcomes from clinical trials in health technology assessment. Medical Decision Making 43, 299–310.
Chipman, H.A., George, E., McCulloch, R., 2010. BART: Bayesian additive regression trees. Annals of Applied Statistics 266–298.
Claxton, K., Lacey, L.F., Walker, S.G., 2000. Selecting treatments: A decision theoretic approach. Journal of the Royal Statistical Society: Series A 163, 211–225.
Claxton, K., Martin, S., Soares, M., Rice, N., Spackman, E., Hinde, S., et al., 2015. Implications for a policy threshold, in: Methods for the Estimation of the National Institute for Health and Care Excellence Cost-Effectiveness Threshold. NIHR Journals Library.
Claxton, K., Sculpher, M., 2025. Agreeing to the demands for higher branded drug prices in the UK will have significant negative effects on public health, social care and economic growth (Open Letter to the UK Prime Minister). Centre for Health Economics, University of York.
Cook, R., Forzani, L., 2008. Principal fitted components for dimension reduction in regression. Statistical Science 23, 485–501.
Cooper, N., Sutton, A., Abrams, K., Turner, D., Wailoo, A., 2004. Comprehensive decision analytical modelling in economic evaluation: A Bayesian approach. Health Economics 13, 203–226.
Cox, C., 2008. The generalized F distribution: an umbrella for parametric survival analysis. Statistics in Medicine 27, 4301–4312.
Cox, D., 1972. Regression models and life-tables. Journal of the Royal Statistical Society: Series B 34, 187–202.
Csárdi, G., Hester, J., Wickham, H., Chang, W., Morgan, M., Tenenbaum, D., 2024. remotes: R Package Installation from Remote Repositories, Including ’GitHub’. URL https://remotes.r-lib.org
Daniels, M., Hogan, J., 2008. Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis. Chapman and Hall/CRC.
Daniels, M., Xu, D., 2020. Bayesian Methods for Longitudinal Data with Missingness, in: Lesaffre, E., Baio, G., Boulanger, B. (Eds.), Bayesian Methods in Pharmaceutical Research. Chapman and Hall/CRC.
De Angelis, R., Capocaccia, R., Hakulinen, T., Soderman, B., Verdecchia, A., 1999. Mixture models for cancer survival analysis: Application to population-based data with covariates. Statistics in Medicine 18, 441–454.
de Finetti, B., 1974. Theory of Probability. John Wiley and Sons, New York, NY.
Degeling, K., Clements, M., Koffijberg, E., O’Mahony, J., Sadatsafavi, M., Pechlivanoglou, P., 2025. Discrete Event Simulations in R, in: Baio, G., Thom, H., Pechivanoglou, P. (Eds.), R for Health Technology Assessment. Chapman and Hall/CRC.
DeGroot, M., 1970. Optimal Statistical Decisions. John Wiley and Sons, New York, NY.
Demiris, N., Lunn, D., Sharples, L.D., 2015. Survival extrapolation using the poly-Weibull model. Statistical Methods in Medical Research 24, 287–301.
Dias, S., Ades, A., Welton, N., Jansen, J., Sutton, A., 2018. Network Meta-analysis for Decision-making. John Wiley and Sons, Chichester, UK.
Dias, S., Welton, N., Sutton, A., Caldwell, D., Lu, G., Ades, A., 2013. Evidence synthesis for decision making 4: Inconsistency in networks of evidence based on randomized controlled trials. Medical Decision Making 33, 641–656.
Diggle, P., Kenward, M., 1994. Informative drop-out in longitudinal data analysis. Journal of the Royal Statistical Society Series C 43, 49–73.
Donaldson, C., Farrar, S., Mapp, T., Walker, A., Macphee, S., 1997. Assessing community values in health care: Is the “willingness to pay” method feasible? Health Care Analysis 5, 7–29.
Donegan, S., Williamson, P., D’Alessandro, U., Garner, P., Tudur Smith, C., 2013. Combining individual patient data and aggregate data in mixed treatment comparison meta-analysis: Individual patient data may be beneficial if only for a subset of trials. Statistics in Medicine 32, 914–930.
Drummond, M.F., Sculpher, M.J., Claxton, K., Stoddart, G.L., Torrance, G.W., 2015. Methods for the Economic Evaluation of Health Care Programmes. Oxford University Press.
Duane, S., Kennedy, A.D., Pendelton, B.J., Roweth, D., 1987. Hybrid Monte Carlo. Physics Letter B 195, 216–222.
Edney, L., Haji Ali Afzali, H., Cheng, T., Karnon, J., 2018. Estimating the reference incremental cost-effectiveness ratio for the Australian health system. PharmacoEconomics 36, 239–252.
Efron, B., Tibshirani, R., 1994. An introduction to the bootstrap. Chapman and Hall/CRC.
Fenwick, E., Claxton, K., Sculpher, M., 2001. Representing uncertainty: The role of cost-effectiveness acceptability curves. Health Economics 10, 779–787.
Fienberg, S., 2006. When Did Bayesian Inference Become Bayesian? Bayesian Analysis 1, 1–40.
Friedman, H.S., 2021. Ultimate Price: The Value We Place on Life. University of California Press.
Gabrio, A., 2024. missingHE: Missing Outcome Data in Health Economic Evaluation. URL https://cran.r-project.org/web/packages/missingHE
Gabrio, A., Daniels, M., Baio, G., 2020. A Bayesian parametric approach to handle missing longitudinal outcome data in trial-based health economic evaluations. Journal of the Royal Statistical Society Series A 183, 607–629.
Gabrio, A., Mason, A., Baio, G., 2019. A full Bayesian model to handle structural ones and missingness in economic evaluations from individual-level data. Statistics in Medicine 38, 1399–1420.
Gabrio, A., Mason, A., Leurent, B., Gomes, G., 2025. Missing Data, in: Baio, G., Thom, H., Pechivanoglou, P. (Eds.), R for Health Technology Assessment. Chapman and Hall/CRC.
Gamerman, D., 1997. Markov Chain Monte Carlo. Chapman and Hall/CRC.
Gao, C., Heath, A., Baio, G., 2025. Regression augmented weighting adjustment for indirect comparisons in health decision modelling. Research Synthesis Methods 16, 900–921.
Gelfand, A., Smith, A., 1990. Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85, 398–409.
Gelman, A., 2006. Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 515–533.
Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D., Vehtari, A., Rubin, D., 2013. Bayesian Data Analysis, 3rd ed. Chapman and Hall/CRC, New York, NY.
Gelman, A., Hill, J., 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, Cambridge, UK.
Gelman, A., Rubin, D., 1992. Inference from iterative simulation using multiple sequences. Statistical Science 7, 457–472.
Geman, S., Geman, D., 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–741.
Gilks, W., Richardson, S., Spiegelhalter, D., 1996. Markov Chain Monte Carlo in Practice. Chapman and Hall/CRC.
Gómez-Rubio, V., 2020. Bayesian Inference with INLA. Chapman and Hall/CRC.
Goodman, S.N., 1999. Toward evidence-based medical statistics. 1: The P value fallacy. Annals of Internal Medicine 130, 995–1004.
Goudie, R., Turner, R., De Angelis, D., Thomas, A., 2020. MultiBUGS: a parallel implementation of the BUGS modelling framework for faster Bayesian inference. Journal of Statistical Software 95.
Grant, R., Di Tanna, G.L., 2025. Bayesian meta-analysis: A practical introduction. CRC Press, Boca Raton, FL.
Green, N., Gao, C., Remiro Azócar, A., 2026. outstandR: Model-Based Standardisation for Indirect Treatment Comparison with Limited Subject-Level Data. URL https://StatisticsHealthEconomics.github.io/outstandR/
Green, N., Krijkamp, E., Thom, H., Dixon, P., 2025a. Decision Tree Models, in: Baio, G., Thom, H., Pechivanoglou, P. (Eds.), R for Health Technology Assessment. Chapman and Hall/CRC.
Green, N., Kurt, M., Moshyk, A., Larkin, J., Baio, G., 2025b. A Bayesian hierarchical mixture cure modelling framework to utilize multiple survival datasets for long-term survivorship estimates: A case study from previously untreated metastatic melanoma. Statistics in Medicine 44, e70132.
Greenland, S., Pearl, J., Robins, J., 1999. Confounding and collapsibility in causal inference. Statistical Science 14, 29–46.
Guyot, P., Ades, A., Ouwens, M., Welton, N., 2012. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Medical Research Methodology 12, 1–13.
Hallek, M., Fischer, K., Fingerle-Rowson, G., Fink, A., Busch, R., Mayer, J., et al., 2010. Addition of rituximab to fludarabine and cyclophosphamide in patients with chronic lymphocytic leukaemia: A randomised, open-label, phase 3 trial. The Lancet 376, 1164–1174.
Hardcastle, L., Livingstone, S., Baio, G., 2025. Averaging polyhazard models using piecewise deterministic Monte Carlo with applications to data with long-term survivors. Annals of Applied Statistics 19, 3179–3202.
Hasselblad, V., 1998. Meta-analysis of multitreatment studies. Medical Decision Making 18, 37–43.
Hastie, T., Tibishirani, R., 1990. Generalized Additive Models. CRC Press.
Hastings, W., 1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 97–59.
Heath, A., Kunst, N., Jackson, C., 2024. Value of information for healthcare decision-making. CRC Press.
Heath, A., Manolopoulou, I., Baio, G., 2019. Estimating the expected value of sample information across different sample sizes using moment matching and nonlinear regression. Medical Decision Making 39, 347–359.
Heath, A., Manolopoulou, I., 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.
Hernán, M.A., 2010. The hazards of hazard ratios. Epidemiology 21, 13–15.
Hill, J., Linero, A., Murray, J., 2020. Bayesian additive regression trees: A review and look forward. Annual Review of Statistics and Its Application 7, 251–278.
Hoffman, M.D., Gelman, A., 2013. The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research 15, 1351–1385.
Howard, R., 1966. Information Value Theory, in: IEEE Transactions on System Science and Cybernetics. SCC-2, (1) 22-26.
Incerti, D., Jansen, J.P., 2021. hesim: Health Economic Simulation Modeling and Decision Analysis. URL https://arxiv.org/abs/2102.09437
Ishak, J., Proskorovsky, I., Benedict, A., 2015. Simulation and matching-based approaches for indirect comparison of treatments. PharmacoEconomics 33, 537–549.
Jackman, S., 2009. Bayesian Analysis for the Social Sciences. John Wiley and Sons, New York, NY.
Jackson, C., 2023. Survextrap: A package for flexible and transparent survival extrapolation. BMC Medical Research Methodology 23, 282.
Jackson, C., 2016. flexsurv: a platform for parametric survival modeling in R. Journal of Statistical Software 70.
Jackson, C., 2015. Slides for the Summer School “Bayesian methods in health economics”’. URL https://gianluca.statistica.it/teaching/summer-school/
Jackson, C., Heath, A., 2024. voi: Expected Value of Information. URL https://cran.r-project.org/web/packages/voi
Jackson, C.H., Thompson, S.G., Sharples, L.D., 2009. Accounting for uncertainty in health economic decision models by using model averaging. Journal of the Royal Statistical Society: Series A 172, 383–404.
Jackson, C., Latimer, N., Jansen, J., Pechlivanoglou, P., Baio, G., 2025. Introduction to survival analysis in HTA, in: Baio, G., Thom, H., Pechivanoglou, P. (Eds.), R for Health Technology Assessment. Chapman and Hall/CRC.
Jackson, C., Sharples, L., Thompson, S., 2010. Survival models in health economic evaluations: Balancing fit and parsimony to improve prediction. The International Journal of Biostatistics 6.
Jalal, H., Alarid-Escudero, F., 2018. A gaussian approximation approach for value of information analysis. Medical Decision Making 38, 174–188.
Jalal, H., Goldhaber-Fiebert, J., Kuntz, K., 2015. Computing expected value of partial sample information from probabilistic sensitivity analysis using linear regression metamodeling. Medical Decision Making 35, 584–595.
Jansen, J.P., 2011. Network meta-analysis of survival data with fractional polynomials. BMC Medical Research Methodology 11, 1–14.
Jansen, J.P., Vieira, M., Cope, S., 2015. Network meta-analysis of longitudinal data using fractional polynomials. Statistics in Medicine 34, 2294–2311.
Jeffreys, H., 1961. Theory of Probability. Clarendon Press, Oxford, UK.
Joe, H., 2014. Dependence modeling with copulas. CRC Press, Boca Raton, FL.
Kaplan, E., Meier, P., 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53, 457–481.
Keng, M.J., Schlackow, I., Pullenayegum, E., 2025. Individual level data, in: Baio, G., Thom, H., Pechivanoglou, P. (Eds.), R for Health Technology Assessment. Chapman and Hall/CRC.
Krainski, E., Gómez-Rubio, V., Bakka, H., Lenzi, A., Castro-Camilo, D., Simpson, D., et al., 2019. Advanced Spatial Modeling with Stochastic Partial Differential Equations using R and INLA. Chapman and Hall/CRC.
Kruschke, J., 2015. Doing Bayesian Data Analysis: A Tutorial with R and JAGS and Stan, 2nd ed. Academic Press.
Laplace, P., 1812. Thorie analytique des probabilités. Veuve Courcier, Paris, France.
Laplace, P., 1774. Mémoires sur la probabilité des causes par les événemens. Mémoires de mathématique et de physique presentés à l’Académie royale des sciences, par divers sçavans & lûs dans ses assemblées 6, 621–656.
Latimer, N., 2011. NICE DSU Technical Support Document 14. Undertaking survival analysis for economic evaluations alongside clinical trials - extrapolation with patient-level data. URL https://sheffield.ac.uk/media/34225/download?attachment
Latimer, N., Henshall, C., Siebert, U., Bell, H., 2016. Treatment switching: Statistical and decision-making challenges and approaches. International Journal of Technology Assessment in Health Care 32, 160–166.
Latimer, N., Rutherford, M.J., 2024. Mixture and non-mixture cure models for health technology assessment: What you need to know. PharmacoEconomics 42, 1073–1090.
Lindgren, F., Rue, H., Lindström, J., 2011. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society: Series B 73, 423–498.
Lindley, D., 1972. Bayesian statistics: A review. SIAM.
Little, R., 1993. Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association 88, 125–134.
Little, R., Rubin, D.B., 2019. Statistical Analysis with Missing Data. John Wiley and Sons, New York, NY.
Loomes, G., McKenzie, L., 1989. The use of QALYs in health care decision making. Social Science and Medicine 28, 299–308.
Lu, G., Ades, A., 2006. Assessing evidence inconsistency in mixed treatment comparisons. Journal of the American Statistical Association 101, 447–459.
Lu, G., Ades, A., 2004. Combination of direct and indirect evidence in mixed treatment comparisons. Statistics in Medicine 23, 3105–3124.
Luce, B.R., Drummond, M., Jönsson, B., Neumann, P.J., Schwartz, J.S., Siebert, U., et al., 2010. EBM, HTA, and CER: clearing the confusion. The Milbank Quarterly 88, 256–276.
Lunn, D., Jackson, C., Best, N., Thomas, A., Spiegelhalter, D., 2013. The BUGS Book. Chapman and Hall/CRC.
Lunn, D., Spiegelhalter, D., Thomas, A., Best, N., 2009. The BUGS project: Evolution, critique and future directions. Statistics in Medicine 28(25), 3049–3067.
Mamun, A.A., Peeters, A., Barendregt, J., Willekens, F., Nusselder, W., Bonneux, L., 2004. Smoking decreases the duration of life lived with and without cardiovascular disease: a life course analysis of the Framingham Heart Study. European Heart Journal 25, 409–415.
Martin, O., Kumar, R., Lao, J., 2021. Bayesian Modeling and Computation in Python. Chapman and Hall/CRC.
Mason, A., Gomes, M., Carpenter, J., Grieve, R., 2021. Flexible Bayesian longitudinal models for cost-effectiveness analyses with informative missing data. Health Economics 30, 3138–3158.
McCabe, C., Claxton, K., Culyer, A., 2008. The NICE cost-effectiveness threshold: what it is and what that means. PharmacoEconomics 26, 733–744.
McElreath, R., 2015. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Chapman and Hall/CRC, Boca Raton, FL.
Meng, X.-L., 1994. Multiple-imputation inferences with uncongenial sources of input. Statistical Science 538–558.
Menzies, N., 2016. An efficient estimator for the expected value of sample information. Medical Decision Making 36, 308–320.
Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., Teller, E., 1953. Equation of state calculations by fast computing machines. The Journal of Chemical Physics 21, 1087–1092.
Milborrow, S., 2011. Earth: Multivariate adaptive regression splines. URL https://cran.r-project.org/web/packages/earth
Mullahy, J., 1986. Specification and testing of some modified count data models. Journal of Econometrics 33, 341–365.
Müller, K., Wickham, H., 2025. tibble: Simple Data Frames. URL https://tibble.tidyverse.org/
National Institute for Health and Care Excellence [NICE], 2025. Changes to NICE’s cost-effectiveness thresholds confirmed. URL https://www.nice.org.uk/news/articles/changes-to-nice-s-cost-effectiveness-thresholds-confirmed
National Institute for Health and Care Excellence [NICE], 2016. The Social Care Guidance Manual. URL https://www.nice.org.uk/process/pmg10
National Institute for Health and Care Excellence [NICE], 2009. Rituximab for the first-line treatment of chronic lymphocytic leukaemia. URL https://www.nice.org.uk/guidance/ta174/resources/rituximab-for-the-firstline-treatment-of-chronic-lymphocytic-leukaemia-pdf-82598435675845
Neal, R., 2011. MCMC Using Hamiltonian Dynamics, in: Brooks, S., Gelman, A., Jones, G., Meng, X. (Eds.), Handbook of Markov Chain Monte Carlo. Chapman and Hall/CRC.
Neumann, P.J., Cohen, J.T., 2018. QALYs in 2018—advantages and concerns. Journal of the American Medical Association 319, 2473–2474.
NIMBLE Development Team, 2016. NIMBLE: An R Package for Programming with BUGS models, Version 0.5-1. URL http://r-nimble.org
O’Mahony, J., Paulden, M., 2014. NICE’s selective application of differential discounting: ambiguous, inconsistent, and unjustified. Value in Health 17, 493–496.
Ochalek, J., Wang, H., Gu, Y., Lomas, J., Cutler, H., Jin, C., 2020. Informing a cost-effectiveness threshold for health technology assessment in China: a marginal productivity approach. PharmacoEconomics 38, 1319–1331.
Office for National Statistics [ONS], 2024. Adult Smoking Habits in the UK: 2023. URL https://www.ons.gov.uk/releases/adultsmokinghabitsintheuk2023
Phillippo, D., 2024. multinma: Bayesian Network Meta-Analysis of Individual and Aggregate Data. URL https://dmphillippo.github.io/multinma/
Phillippo, D., Ades, A., Dias, S., Palmer, S., Abrams, K., Welton, N., 2018. Methods for population-adjusted indirect comparisons in health technology appraisal. Medical Decision Making 38, 200–211.
Phillippo, D., Ades, A., Palmer, S., Abrams, K., Welton, N., 2016. NICE DSU Technical Support Document 18. Methods for population-adjusted indirect comparisons in submissions to NICE. URL https://sheffield.ac.uk/media/34216/download?attachment
Phillippo, D., Dias, S., Ades, A., Belger, M., Brnabic, A., Schacht, A., et al., 2020. Multilevel network meta-regression for population-adjusted treatment comparisons. Journal of the Royal Statistical Society: Series A 183, 1189–1210.
Phillippo, D., Dias, S., Ades, A., Welton, N., 2023. Calibration of treatment effects in network meta-analysis using individual patient data. Statistics in Medicine 42, 2376–2397.
Phillippo, D., Dias, S., Welton, N., Ades, A., 2024. Multilevel network meta-regression for general likelihoods: Synthesis of individual and aggregate data with applications to survival analysis. URL https://arxiv.org/abs/2401.12640
Phillippo, D., Jansen, J., Remiro Azócar, A., Thom, H., 2025a. Population-Adjusted Indirect Comparisons, in: Baio, G., Thom, H., Pechivanoglou, P. (Eds.), R for Health Technology Assessment. Chapman and Hall/CRC.
Phillippo, D., Remiro Azócar, A., Heath, A., Baio, G., Dias, S., Ades, A., et al., 2025b. Effect modification and non-collapsibility together may lead to conflicting treatment decisions: A review of marginal and conditional estimands and recommendations for decision-making. Research Synthesis Methods 1–27.
Plummer, M., 2023. rjags: Bayesian Graphical Models using MCMC. URL https://cran.r-project.org/web/packages/rjags
Plummer, M., 2010. JAGS: Just Another Gibbs Sampler. URL https://sourceforge.net/projects/mcmc-jags/files/Manuals/4.x/jags_user_manual.pdf/download
Plummer, M., 2008. Penalized loss functions for Bayesian model comparison. Biostatistics 9, 523–539.
Polack, F., Thomas, S., Kitchin, N., Absalon, J., Gurtman, A., Lockhart, S., et al., 2020. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. New England Journal of Medicine 383, 2603–2615.
Raftery, A., Lewis, S., 1995. The number of iterations, convergence diagnostics and generic Metropolis algorithms, in: Gilks, W., Spiegelhalter, D., Richardson, S. (Eds.), Markov Chain Monte Carlo in Practice. Chapman and Hall/CRC.
Raftery, A., Lewis, S., 1992. [Practical Markov Chain Monte Carlo]: comment: one long run with diagnostics: implementation strategies for Markov Chain Monte Carlo. Statistical Science 7, 493–497.
Raiffa, H., 1968. Decision Analysis: Introductory Lectures on Choices under Uncertainty. Addison-Wesley.
Raiffa, H., Schlaifer, H., 1961. Applied Statistical Decision Theory. Harvard University Press, Boston, MA.
Ramsay, J., 1988. Monotone regression splines in action. Statistical Science 425–441.
Rawlins, M., Culyer, A., 2004. National Institute for Clinical Excellence and its value judgments. British Medical Journal 329, 224–227.
Remiro Azócar, A., 2025. Slides for the Summer School “Bayesian methods in health economics”. URL https://gianluca.statistica.it/teaching/summer-school/
Remiro Azócar, A., Heath, A., Baio, G., 2024. Model-based standardization using multiple imputation. BMC Medical Research Methodology 24, 32.
Remiro Azócar, A., Heath, A., Baio, G., 2022. Parametric G-computation for compatible indirect treatment comparisons with limited individual patient data. Research synthesis methods 13, 716–744.
Remiro Azócar, A., Heath, A., Baio, G., 2021. Methods for population adjustment with limited access to individual patient data: A review and simulation study. Research synthesis methods 12, 750–775.
Robert, C., Casella, G., 2011. A short history of Markov Chain Monte Carlo: Subjective recollections from incomplete data. Statistical Science 26, 102–115.
Robert, C., Casella, G., 2010. Introducing Monte Carlo Methods with R. Springer Verlag, New York, NY.
Robert, C., Casella, G., 2004. Monte Carlo Statistical Methods, 2nd ed. Springer Verlag, New York, NY.
Robins, J., 1986. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. Mathematical Modelling 7, 1393–1512.
Robins, J., Hernán, M., Brumback, B., 2000. Marginal structural models and causal inference in epidemiology. Epidemiology 11, 550–560.
Rosenbaum, P., Rubin, D., 1984. Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association 79, 516–524.
Rosenbaum, P., Rubin, D., 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55.
Royston, P., Altman, D.G., 1994. Regression using fractional polynomials of continuous covariates: Parsimonious parametric modelling. Journal of the Royal Statistical Society Series C 43, 429–453.
Rubin, D., 1987. Multiple Imputation for Non-response in Surveys. Wiley.
Rubin, D., 1977. Formalizing subjective notions about the effect of nonrespondents in sample surveys. Journal of the American Statistical Association 72, 538–543.
Rubin, D., 1976. Inference and missing data. Biometrika 63, 581–592.
Rue, H., Martino, S., Chopin, N., 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B 71, 319–392.
Russo, P., Zanuzzi, M., Carletto, A., Sammarco, A., Romano, F., Manca, A., 2023. Role of economic evaluations on pricing of medicines reimbursed by the Italian National Health Service. PharmacoEconomics 41, 107–117.
Sampson, C., Zamora, B., Watson, S., Cairns, J., Chalkidou, K., Cubi-Molla, P., et al., 2022. Supply-side cost-effectiveness thresholds: Questions for evidence-based policy. Applied Health Economics and Health Policy 20, 651–667.
Saramago, P., Sutton, A., Cooper, N., Manca, A., 2012. Mixed treatment comparisons using aggregate and individual participant level data. Statistics in Medicine 31, 3516–3536.
Schneider, P., 2022. The QALY is ableist: on the unethical implications of health states worse than dead. Quality of Life Research 31, 1545–1552.
Schwarz, G., 1978. Estimating the dimension of a model. The Annals of Statistics 461–464.
Sharples, L.D., Demiris, N., 2020. Survival analysis and censored data, in: Bayesian Methods in Pharmaceutical Research. Chapman and Hall/CRC, pp. 207–222.
Signorovitch, J., Sikirica, V., Erder, M., Xie, J., Lu, M., Hodgkins, P., et al., 2012. Matching-adjusted indirect comparisons: A new tool for timely comparative effectiveness research. Value in Health 15, 940–947.
Signorovitch, J., Wu, E., Yu, A., Gerrits, C., Kantor, E., Bao, Y., et al., 2010. Comparative effectiveness without head-to-head trials: A method for matching-adjusted indirect comparisons applied to psoriasis treatment with adalimumab or etanercept. Pharmacoeconomics 28, 935–945.
Simpson, D., Rue, H., Riebler, A., Martins, T.G., Sørbye, S.H., 2017. Penalising model component complexity: A principled, practical approach to constructing priors. Statistical Science 32, 1–28.
Siverskog, J., Henriksson, M., 2019. Estimating the marginal cost of a life year in Sweden’s public healthcare sector. The European Journal of Health Economics 20, 751–762.
Sklar, M., 1959. Fonctions de répartition à n dimensions et leurs marges, in: Annales de l’institut de Statistique de l’université de Paris. pp. 229–231.
Spiegelhalter, D., Abrams, K., Myles, J., 2004. Bayesian Approaches to Clinical Trials and Health-Care Evaluation. John Wiley and Sons, Chichester, UK.
Spiegelhalter, D., Best, N., Carlin, B., Van Der Linde, A., 2002a. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B 64, 583–639.
Spiegelhalter, D., Thomas, A., Best, N., 2002b. WinBUGS version 1.4. MRC Biostatistics Unit, Cambridge, UK.
Spiegelhalter, D., Thomas, A., Best, N., Gilks, W., 1996. BUGS 0.5: Bayesian inference using Gibbs sampling manual (version ii). MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK 1–59.
Stadhouders, N., Koolman, X., Dijk, C. van, Jeurissen, P., Adang, E., 2019. The marginal benefits of healthcare spending in the Netherlands: Estimating cost-effectiveness thresholds using a translog production function. Health Economics 28, 1331–1344.
Stan development team, 2024. Stan Modeling Language Users Guide and Reference Manual. URL https://mc-stan.org
Stigler, S., 1982. Thomas Bayes’s Bayesian inference. Journal of the Royal Statistical Society: Series A 145, 250–258.
Strong, M., 2018. Slides for the Summer School “Bayesian methods in health economics”. URL https://gianluca.statistica.it/teaching/summer-school/
Strong, M., Oakley, J., 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.
Sturtz, S., Ligges, U., Gelman, A., 2019. R2OpenBUGS: a package for running OpenBUGS from R. URL https://cran.r-project.org/web/packages/R2OpenBUGS
Sturtz, S., Ligges, U., Gelman, A., 2005. R2WinBUGS: A package for running WinBUGS from R. Journal of Statistical Software 12, 1–16.
Su, Y., Yajima, M., 2010. R2jags User manual: A Package to call jags from R. URL https://cran.r-project.org/web/packages/R2jags
Sutton, A., Cooper, N., Jones, D., Lambert, P., Thompson, J., Abrams, K., 2008. Evidence synthesis as the key to more coherent and efficient research. BMC Medical Research Methodology 8, 3.
The EuroQol Group, 1990. EuroQol-a new facility for the measurement of health-related quality of life. Health Policy 16, 199–208.
Thom, H., Incerti, D., Jackson, C., Lamrock, F., C., W., 2025a. Continuous Time Multistate Models, in: Baio, G., Thom, H., Pechivanoglou, P. (Eds.), R for Health Technology Assessment. Chapman and Hall/CRC.
Thom, H., Saramago, P., Soares, M., Krijkamp, E., Lamrock, F., 2025b. Cohort Markov models in discrete time, in: Baio, G., Thom, H., Pechivanoglou, P. (Eds.), R for Health Technology Assessment. Chapman and Hall/CRC.
Thompson, S., Nixon, R., 2005. How sensitive are cost-effectiveness analysis to choice of parametric distributions? Medical Decision Making 14, 421–428.
Turner, D., Wailoo, A., Nicholson, K., Cooper, N., Sutton, A., Abrams, K., 2003. Systematic review and economic decision modelling for the prevention and treatment of influenza A and B: neuraminidase inhibitors for treatment. Health Technology Assessment 7.
Vallejo-Torres, L., García-Lorenzo, B., Serrano-Aguilar, P., 2018. Estimating a cost-effectiveness threshold for the Spanish NHS. Health Economics 27, 746–761.
van Baal, P., Perry-Duxbury, M., Bakx, P., Versteegh, M., Van Doorslaer, E., Brouwer, W., 2019. A cost-effectiveness threshold based on the marginal returns of cardiovascular hospital spending. Health Economics 28, 87–100.
van Buuren, S., 2018. Flexible Imputation of Missing Data, 2nd ed. Chapman and Hall/CRC, Boca Raton, FL, USA.
Van Hout, B.A., Al, M.J., Gordon, G.S., Rutten, F.F., 1994. Costs, effects and C/E-ratios alongside a clinical trial. Health Economics 3, 309–319.
Vanness, D.J., Lomas, J., Ahn, H., 2021. A health opportunity cost threshold for cost-effectiveness analysis in the united states. Annals of Internal Medicine 174, 25–32.
Vehtari, A., Gabry, J., Magnusson, M., Yao, Y., Bürkner, P., Paananen, T., et al., 2024. loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models. URL https://mc-stan.org/loo/
Vehtari, A., Gelman, A., Gabry, J., 2017. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing 27, 1413–1432.
Wabersich, D., Vandekerckhove, J., 2014. Extending JAGS: A tutorial on adding custom distributions to JAGS (with a diffusion model example). Behavior Research Methods 46, 15–28.
Wang, W., Yan, J., 2021. Shape-Restricted Regression Splines with R Package splines2. Journal of Data Science 19.
Wang, X., Yue, Y.R., Faraway, J., 2018. Bayesian regression modeling with INLA. Chapman and Hall/CRC.
Ware, J., Sherbourne, D., 1992. The MOS 36-item short-form health survey (SF-36). Conceptual Framework. Medical Care 30, 473–83.
Welton, N., 2018. Slides for the Summer School “Bayesian methods in health economics”. URL https://gianluca.statistica.it/teaching/summer-school/
Welton, N., Sutton, A., Cooper, N., Abrams, K., Ades, A., 2012. Evidence Synthesis for Decision Making in Healthcare. John Wiley and Sons, Chichester, UK.
Wickham, H., 2016. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, New York, NY.
Wickham, H., Averick, M., Bryan, J., Chang, W., D’Agostino McGowan, L., François, R., et al., 2019. Welcome to the tidyverse. Journal of Open Source Software 4, 1686.
Wickham, H., François, R., Henry, L., Müller, K., Vaughan, D., 2023. dplyr: A Grammar of Data Manipulation. URL https://dplyr.tidyverse.org
Willan, A., Briggs, A., 2006. The Statistical Analysis of Cost-effectiveness Data. John Wiley and Sons, Chichester, UK.
Williams, C., Lewsey, J., Briggs, A., Mackay, D., 2017a. Cost-effectiveness analysis in R using a multi-state modeling survival analysis framework: a tutorial. Medical Decision Making 37, 340–352.
Williams, C., Lewsey, J., Mackay, D., Briggs, A., 2017b. Estimation of survival probabilities for use in cost-effectiveness analyses: a comparison of a multi-state modeling survival analysis approach with partitioned survival and Markov decision-analytic modeling. Medical Decision Making 37, 427–439.
Williams, C., Rasmussen, E., 2006. Gaussian Processes for Machine Learning. MIT press, Cambridge, MA.
Wood, S., 2017. Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC.
Woods, B., Sideris, E., Palmer, S., Latimer, N., Soares, M., 2017. NICE DSU Technical Support Document 19. Partitioned survival analysis for decision modelling in health care: a critical review. URL https://sheffield.ac.uk/media/34205/download?attachment
Zellner, A., 1962. An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association 57, 348–368.
Zhang, J., Rios, J., Pechlivanoglou, T., Yang, A., Zhang, Q., Deris, D., et al., 2024. SurvdigitizeR: an algorithm for automated survival curve digitization. BMC Medical Research Methodology 24, 147.