References

Achana, F.A., Cooper, N.J., Bujkiewicz, S., Hubbard, S.J., Kendrick, D., Jones, D.R., Sutton, A.J., 2014. Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes. BMC Medical Research Methodology 14. https://doi.org/10.1186/1471-2288-14-92
Ades, A.E., 2003. A chain of evidence with mixed comparisons: models for multi-parameter synthesis and consistency of evidence. Statistics in Medicine 22, 2995–3016. https://doi.org/10.1002/sim.1566
Ades, A., Lu, G., Claxton, K., 2004. Expected Value of Sample Information Calculations in Medical Decision Modeling. Medical Decision Making 24, 207–227.
Alarid-Escudero, F., Krijkamp, E., Enns, E.A., Yang, A., Hunink, M.G.M., Pechlivanoglou, P., Jalal, H., 2023a. An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example. Medical Decision Making 43, 3–20. https://doi.org/10.1177/0272989X221103163
Alarid-Escudero, F., Krijkamp, E., Enns, E.A., Yang, A., Hunink, M.G.M., Pechlivanoglou, P., Jalal, H., 2023b. A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example. Medical Decision Making 43, 21–41. https://doi.org/10.1177/0272989x221121747
Alarid-Escudero, F., Krijkamp, E.M., Pechlivanoglou, P., Jalal, H., Kao, S.-Y.Z., Yang, A., Enns, E.A., 2019. A need for change! A coding framework for improving transparency in decision modeling. PharmacoEconomics 37, 1329–1339.
Allaire, J.J., Teague, C., Scheidegger, C., Xie, Y., Dervieux, C., 2024. Quarto. https://doi.org/10.5281/zenodo.5960048
Allaire, J., Xie, Y., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., Wickham, H., Cheng, J., Chang, W., Iannone, R., 2021. rmarkdown: Dynamic Documents for R. R package version 2.8.
Amdahl, J., 2022. Flexsurvcure: Flexible parametric cure models.
Andronis, L., Barton, P., Bryan, S., 2009. Sensitivity analysis in economic evaluation: an audit of NICE current practice and a review of its use and value in decision-making.
Aponte Ribero, V., Sanchez Alvarez, J., 2022. Descem: Discrete event simulation for cost-effectiveness modelling.
Ara, R., Brazier, J.E., 2011. Using health state utility values from the general population to approximate baselines in decision analytic models when condition-specific data are not available. Value in Health 14, 539–545.
Ataga, A.K., Kenneth I.; Kutlar, 2017. Crizanlizumab for the prevention of pain crises in sickle cell disease. New England Journal of Medicine 376, 429–439. https://doi.org/https://dx.doi.org/10.1056/NEJMoa1611770
Attema, A.E., Bleichrodt, H., L’Haridon, O., Peretti-Watel, P., Seror, V., 2018. Discounting health and money: New evidence using a more robust method. Journal of Risk and Uncertainty 56, 117–140. https://doi.org/10.1007/s11166-018-9279-1
Australian Government Department of Health and Aged Care, 2023. Australian HTA review HTA methods: Economic evaluation.
Baharnoori, M., Bhan, V., Clift, F., Thomas, K., Mouallif, S., Adlard, N., Cooney, P., Blanchette, F., Patel, B.P., Grima, D., 2022. Cost-Effectiveness Analysis of Ofatumumab for the Treatment of Relapsing-Remitting Multiple Sclerosis in Canada. Pharmacoecon Open 6, 859–870. https://doi.org/10.1007/s41669-022-00363-1
Baio, G., 2020. survHE: survival analysis for health economic evaluation and cost-effectiveness modeling. Journal of Statistical Software 95, 1–47.
Baio, G., 2014. Bayesian models for cost-effectiveness analysis in the presence of structural zero costs. Statistics in Medicine 33, 1900–1913.
Baio, G., 2012. Bayesian methods in Health Economics. CRC Press, Boca Raton, FL.
Baio, G., Berardi, A., Heath, A., 2017. Bayesian Cost-Effectiveness Analysis with the R package BCEA. Springer, New York, NY. https://doi.org/10.1007/978-3-319-55718-2
Baio, G., Dawid, P., 2011. Probabilistic sensitivity analysis in health economics. Statistical methods in medical research 24, 615–634.
Balestroni, G., Bertolotti, G., 2012. EuroQol-5D (EQ-5D): an instrument for measuring quality of life. Monaldi Archives for Chest Disease 78.
Barton, P., Bryan, S., Robinson, S., 2004. Modelling in the economic evaluation of health care: selecting the appropriate approach. Journal of Health Services Research & Policy 9, 110–118. https://doi.org/10.1258/135581904322987535
Basu, A., Manca, A., 2012. Regression estimators for generic health-related quality of life and quality-adjusted life years. Medical Decision Making 32, 56–69.
Bayes, T., 1763. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, FRS communicated by Mr. Price, in a letter to John Canton, AMFR S. Philosophical transactions of the Royal Society of London 370–418.
Beecham, J., Knapp, M., others, 2001. Costing psychiatric interventions. Measuring mental health needs 2, 200–224.
Beeken, R., Leurent, B., Vickerstaff, V., Wilson, R., Croker, H., Morris, S., Omar, R., Nazareth, I., Wardle, J., 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.
Belger, M., Brnabic, A., Kadziola, Z., Petto, H., Faries, D., 2015. Alternative weighting approaches for matching adjusted indirect comparisons (MAIC). Value in Health 18, A31–A32. https://doi.org/10.1016/j.jval.2015.03.190
Berlin, J.A., Santanna, J., Schmid, C.H., Szczech, L.A., Feldman, H.I., 2002. Individual patient- versus group-level data meta-regressions for the investigation of treatment effect modifiers: Ecological bias rears its ugly head. Statistics in Medicine 21, 371–387. https://doi.org/10.1002/sim.1023
Beyersmann, J., Allignol, A., Schumacher, M., 2012. Competing Risks and Multistate Models with R, Use R! Springer.
Blaser, N., Salazar Vizcaya, L., Estill, J., Zahnd, C., Kalesan, B., Egger, M., Keiser, O., Gsponer, T., 2015. gems: An R Package for Simulating from Disease Progression Models 64.
Borenstein, M., Hedges, L.V., Higgins, J.P., Rothstein, H.R., 2010. A basic introduction to fixed-effect and random-effects models for meta-analysis. Research Synthesis Methods 1, 97–111. https://doi.org/10.1002/jrsm.12
Brennan, A., Chick, S.E., Davies, R., 2006. A taxonomy of model structures for economic evaluation of health technologies. Health Econ 15, 1295–310. https://doi.org/10.1002/hec.1148
Briggs, A., Clark, T., Wolstenholme, J., Clarke, P., 2003. Missing.... presumed at random: cost-analysis of incomplete data. Health economics 12, 377–392.
Briggs, A., Claxton, K., Sculpher, M., 2006. Decision Modelling for Health Economic Evaluation. Oxford University Press.
Briggs, A., Weinstein, M., Fenwick, E., Karnon, J., Sculpher, M., Paltiel, A., 2012. Model Parameter Estimation and Uncertainty Analysis. Medical Decision Making 32, 722–732. https://doi.org/10.1177/0272989x12458348
Brooks, S., Gelman, A., Jones, G., Meng, X.-L., 2011. Handbook of Markov Chain Monte Carlo. CRC press.
Bucher, H.C., Guyatt, G.H., Griffith, L.E., Walter, S.D., 1997. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. Journal of Clinical Epidemiology 50, 683–91. https://doi.org/10.1016/s0895-4356(97)00049-8
Burnham, K.P., Anderson, D.R., 2002. Model selection and multimodel inference. Springer.
Burton, A., Billingham, L.J., Bryan, S., 2007. Cost-effectiveness in clinical trials: using multiple imputation to deal with incomplete cost data. Clinical Trials 4, 154–161.
CADTH, 2023. Procedures for CADTH Reimbursement Reviews.
CADTH, 2019. Guidelines for the economic evaluation of health technologies: Canada [4th Edition].
CADTH, 2006. Guidelines for economic evaluation of health technologies (Report). The Canadian Coordinating Office for Health Technology Assessment.
Campbell, H., Karnon, J., Dowie, R., 2001. Cost analysis of a hospital-at-home initiative using discrete event simulation. Journal of Health Services Research & Policy 6, 14–22. https://doi.org/10.1258/1355819011927152
Caro, J.J., Briggs, A.H., Siebert, U., Kuntz, K.M., 2012. Modeling good research practices–overviewn: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–1. 15, 796–803. https://doi.org/10.1016/j.jval.2012.06.012
Caro, J.J., Ishak, K.J., 2010. No head-to-head trial? Simulate the missing arms. Pharmacoeconomics 28, 957–967.
Carpenter, B., Gelman, A., Hoffman, M.D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M.A., Guo, J., Li, P., Riddell, A., 2017. Stan: A probabilistic programming language. J. Stat. Softw. 76, 1–32. https://doi.org/10.18637/jss.v076.i01
Center for Evaluation of Value and Risk in Health, 2019.
Chacon, S., Straub, B., 2014. Pro git. Springer Nature.
Chandler, C., Proskorovsky, I., 2023. MSR25 uncertain about uncertainty in matching-adjusted indirect comparisons (MAIC)? A simulation study to compare methods for variance estimation. Value in Health 26, S398. https://doi.org/10.1016/j.jval.2023.09.2084
Chang, W., Borges Ribeiro, B., 2018. shinydashboard: Create Dashboards with ’Shiny’.
Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., Dipert, A., Borges, B., 2021. shiny: Web Application Framework for R.
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.
Cheng, D., Tchetgen, E.T., Signorovitch, J., 2023. On the double‐robustness and semiparametric efficiency of matching‐adjusted indirect comparisons. Research Synthesis Methods 14, 438–442. https://doi.org/10.1002/jrsm.1616
Claxton, K., 1999a. Bayesian approaches to the value of information: implications for the regulation of new pharmaceutical. Health Economics 8, 269–274.
Claxton, K., 1999b. The irrelevance of inference: A decision-making approach to the stochastic evaluation of health care technologies. Journal of health economics 18, 341–364.
Claxton, K., Sculpher, M., McCabe, C., Briggs, A., Akehurst, R., Buxton, M., Brazier, J., O’Hagan, A., 2005. Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra. Health Economics 14, 339–347.
Conti, S., Claxton, K., 2009. Dimensions of design space: a decision-theoretic approach to optimal research design. Medical Decision Making 29, 643–660.
Cooper, N.J., Spiegelhalter, D., Bujkiewicz, S., Dequen, P., Sutton, A.J., 2013. Use of implicit and explicit bayesian methods in health technology assessment. International Journal of Technology Assessment in Health Care 29, 336–342. https://doi.org/10.1017/S0266462313000354; 10.1017/S0266462313000354
Cope, S., Chan, K., Campbell, H., Chen, J., Borrill, J., May, J.R., Malcolm, W., Branchoux, S., Kupas, K., Jansen, J.P., 2023. A comparison of alternative network meta-analysis methods in the presence of nonproportional hazards: A case study in first-line advanced or metastatic renal cell carcinoma. Value Health 26, 465–476. https://doi.org/10.1016/j.jval.2022.11.017
Cope, S., Chan, K., Jansen, J.P., 2020. Multivariate network meta-analysis of survival function parameters. Research Synthesis Methods 11, 443–456. https://doi.org/10.1002/jrsm.1405
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C., 2001. Introduction to algorithms, 2nd ed. The MIT Press.
Cowles, M.K., Carlin, B.P., 1996. Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review. Journal of the American Statistical Association 91, 883–904.
Cox, D.R., Miller, H.D., 1965. The theory of stochastic processes. Methuen, London.
Crowther, M.J., Lambert, P.C., 2014. A general framework for parametric survival analysis. Stat. Med. 33, 5280–5297.
Csardi, G., Nepusz, T., 2006. The igraph software package for complex network research. InterJournal Complex Systems, 1695.
Daniels, M.J., Hogan, J.W., 2008. Missing data in longitudinal studies: Strategies for Bayesian modeling and sensitivity analysis. CRC press.
DataCamp, 2020.
Davis, S., Stevenson, M., Tappenden, P., Wailoo, A., 2014., in: NICE DSU Technical Support Document 15: Cost-Effectiveness Modelling Using Patient-Level Simulation, NICE Decision Support Unit Technical Support Documents. London.
Deeks, J.J., Higgins, J.P.T., Altman, D.G., 2023. Chapter 10: Analysing data and undertaking meta-analyses. In: Higgins JPT, thomas j, chandler j, cumpston m, li t, page MJ, welch VA (editors). Cochrane handbook for systematic reviews of interventions version 6.4 (updated august 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook.
Degeling, K., Franken, M.D., May, A.M., Oijen, M.G.H. van, Koopman, M., Punt, C.J.A., IJzerman, M.J., Koffijberg, H., 2018a. Matching the model with the evidence: comparing discrete event simulation and state-transition modeling for time-to-event predictions in a cost-effectiveness analysis of treatment in metastatic colorectal cancer patients. Cancer Epidemiology 57, 60–67. https://doi.org/10.1016/j.canep.2018.09.008
Degeling, K., IJzerman, M.J., Groothuis-Oudshoorn, C.G.M., Franken, M.D., Koopman, M., Clements, M.S., Koffijberg, H., 2022. Comparing Modeling Approaches for Discrete Event Simulations With Competing Risks Based on Censored Individual Patient Data: A Simulation Study and Illustration in Colorectal Cancer. Value in Health 25, 104–115. https://doi.org/10.1016/j.jval.2021.07.016
Degeling, K., IJzerman, M.J., Koopman, M., Koffijberg, H., 2017. Accounting for parameter uncertainty in the definition of parametric distributions used to describe individual patient variation in health economic models. BMC Medical Research Methodology 17. https://doi.org/10.1186/s12874-017-0437-y
Degeling, K., Koffijberg, H., Franken, M.D., Koopman, M., IJzerman, M.J., 2018b. Comparing Strategies for Modeling Competing Risks in Discrete-Event Simulations: A Simulation Study and Illustration in Colorectal Cancer. Medical Decision Making 39, 57–73. https://doi.org/10.1177/0272989x18814770
Degeling, K., To, Y.H., Trapani, K., Athan, S., Gibbs, P., IJzerman, M.J., Franchini, F., 2024. Predicting the population health economic impact of current and new cancer treatments for colorectal cancer: A data-driven whole disease simulation model for predicting the number of patients with colorectal cancer by stage and treatment line in australia. Value Health 27, 1382–1392.
Department of Health and Ageing, 2008. Guidelines for preparing submissions to the Pharmaceutical Benefits Advisory Committee: Version 4.3.
Dias, S., Ades, A., Welton, N.J., Jansen, J.P., Sutton, A., 2018. Network meta-analysis for decision-making, Statistics in practice. Wiley.
Dias, S., Sutton, A.J., Ades, A.E., Welton, N.J., 2013a. Evidence synthesis for decision making 2: A generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Medical Decision Making 33, 607–17. https://doi.org/10.1177/0272989X12458724
Dias, S., Sutton, A.J., Welton, N.J., Ades, A.E., 2013b. Evidence synthesis for decision making 3: Heterogeneity–subgroups, meta-regression, bias, and bias-adjustment. Medical Decision Making 33, 618–40. https://doi.org/10.1177/0272989X13485157
Dias, S., Welton, N.J., Sutton, A., Ades, A., 2011 (last updated September 2016). NICE DSU technical support document 2: A generalised linear modelling framework for pairwise and network meta-analysis of randomised controlled trials. Report by the Decision Support Unit.
Dias, S., Welton, N.J., Sutton, A.J., Caldwell, D.M., Lu, G., Ades, A.E., 2013c. Evidence synthesis for decision making 4: Inconsistency in networks of evidence based on randomized controlled trials. Medical Decision Making 33, 641–56. https://doi.org/10.1177/0272989X12455847
Diaz-Ordaz, K., Kenward, M., Cohen, A., Coleman, C., Eldridge, S., 2014a. Are missing data adequately handled in cluster randomised trials? A systematic review and guidelines. Clinical Trials 11, 590–600.
Diaz-Ordaz, K., Kenward, M.G., Grieve, R., 2014b. Handling missing values in cost effectiveness analyses that use data from cluster randomized trials. Journal of the Royal Statistical Society. Series A (Statistics in Society) 457–474.
Dilla, T., Möller, J., O’Donohoe, P., Álvarez, M., Sacristán, J.A., Happich, M., Tockhorn, A., 2014. Long-acting olanzapine versus long-acting risperidone for schizophrenia in spain - a cost-effectiveness comparison. BMC Psychiatry 14, 298.
Dolan, P., 1997. Modeling Valuations for EuroQol Health States: Medical Care 35, 1095–1108. https://doi.org/10.1097/00005650-199711000-00002
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. https://doi.org/10.1002/sim.5584
Drummond, M., Sculpher, M.J., K, C., G, S., Torrance G, 2015. Methods for the Economic Evaluation of Health Care Programmes (Oxford Medical Publications), 4th ed. Oxford University Press (OUP).
Dziak, J.J., Coffman, D.L., Lanza, S.T., Li, R., Jermiin, L.S., 2019. Sensitivity and specificity of information criteria. Briefings in Bioinformatics 21, 553–565. https://doi.org/10.1093/bib/bbz016
Efron, B., Tibshirani, R.J., 1993. An introduction to the bootstrap.
Efthimiou, O., White, I.R., 2020. The dark side of the force: Multiplicity issues in network meta-analysis and how to address them. Research Synthesis Methods 11, 105–122. https://doi.org/10.1002/jrsm.1377
Elwenspoek, M.M.C., Thom, H., Sheppard, A.L., Keeney, E., O’Donnell, R., Jackson, J., Roadevin, C., Dawson, S., Lane, D., Stubbs, J., Everitt, H., Watson, J.C., Hay, A.D., Gillett, P., Robins, G., Jones, H.E., Mallett, S., Whiting, P.F., 2022. Defining the optimum strategy for identifying adults and children with coeliac disease: systematic review and economic modelling. Health Technology Assessment 26, vii–164. https://doi.org/10.3310/ZUCE8371
Ethgen, O., Standaert, B., 2012. Population- versus cohort-based modelling approaches. PharmacoEconomics 30, 171–81. https://doi.org/10.2165/11593050-000000000-00000
EUnetHTA, 2014. Methods for health economic evaluations: A guideline based on current practices in Europe - second draft.
Faria, R., Gomes, M., Epstein, D., White, I.R., 2014. A guide to handling missing data in cost-effectiveness analysis conducted within randomised controlled trials. PharmacoEconomics 32, 1157–1170.
Faria, R., Hernandez Alava, M., Manca, A., Wailoo, A.J., 2015. Technical Support Document 17: The use of observational data to inform estimates of treatment effectiveness in technology appraisal: Methods for comparative individual patient data. NICE Decision Support Unit, Sheffield, UK.
Felli, J., Hazen, G., 1998. Sensitivity analysis and the expected value of perfect information. Medical Decision Making 18, 95–109.
Fiocco, M., Putter, H., Houwelingen, H.C. van, 2008. Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models. Stat Med 27, 4340–58. https://doi.org/10.1002/sim.3305
Frank, A., Beck, J.R., Sonnenberg, F.A., Beck, J.R., 1993. Markov Models in Medical Decision Making: A Practical Guide. Medical Decision Making 13, 322–338. https://doi.org/10.1177/0272989X9301300409
Freeman, S.C., Carpenter, J.R., 2017. Bayesian one-step IPD network meta-analysis of time-to-event data using royston-parmar models. Research Synthesis Methods 8, 451–464. https://doi.org/10.1002/jrsm.1253
Freeman, S.C., Cooper, N.J., Sutton, A.J., Crowther, M.J., Carpenter, J.R., Hawkins, N., 2022. Challenges of modelling approaches for network meta-analysis of time-to-event outcomes in the presence of non-proportional hazards to aid decision making: Application to a melanoma network. Statistical Methods in Medical Research 31, 839–861. https://doi.org/10.1177/09622802211070253
Gabrio, A., Daniels, M.J., 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 (Statistics in Society) 183, 607–629.
Gabrio, A., Hunter, R., Mason, A.J., Baio, G., 2021. Joint longitudinal models for dealing with missing at random data in trial-based economic evaluations. Value in Health 24, 699–706.
Gabrio, A., Mason, A.J., 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.J., Baio, G., 2017. Handling missing data in within-trial cost-effectiveness analysis: a review with future recommendations. PharmacoEconomics-open 1, 79–97.
G-BA, 2011. The benefit assessment of medicinal products in accordance with the german social code, book five (SGB v), section 35a https://www.g-ba.de/english/benefitassessment/ (accessed 28-mar-2024).
Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B., 2013. Bayesian data analysis. Chapman; Hall/CRC, Boca Raton, FL, US.
Gelman, A., Hill, J., 2007. Data analysis using regression and hierarchical/multilevel models.
Gestel, A. van, Severens, J.L., Webers, C.A.B., Beckers, H.J.M., Jansonius, N.M., Schouten, J.S.A.G., 2010. Modeling Complex Treatment Strategies: Construction and Validation of a Discrete Event Simulation Model for Glaucoma. Value in Health 13, 358–367. https://doi.org/10.1111/j.1524-4733.2009.00678.x
Geweke, J., 1992. Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments, in: Bernardo, J.M., Berger, J., Dawid, A.P., Smith, A.F.M. (Eds.), Bayesian Statistics. Oxford University Press, Oxford, U.K.
Gomes, M., Diaz-Ordaz, K., Grieve, R., Kenward, M.G., 2013. Multiple imputation methods for handling missing data in cost-effectiveness analyses that use data from hierarchical studies: an application to cluster randomized trials. Medical Decision Making 33, 1051–1063.
Gomes, M., Kenward, M.G., Grieve, R., Carpenter, J., 2020. Estimating treatment effects under untestable assumptions with nonignorable missing data. Statistics in Medicine 39, 1658–1674.
Gomes, M., Ng, E.S.-W., Grieve, R., Nixon, R., Carpenter, J., Thompson, S.G., 2012. Developing appropriate methods for cost-effectiveness analysis of cluster randomized trials. Medical Decision Making 32, 350–361.
Grafféo, N., Latouche, A., Le Tourneau, C., Chevret, S., 2019. ipcwswitch: An R package for inverse probability of censoring weighting with an application to switches in clinical trials. Computers in Biology and Medicine 111, 103339. https://doi.org/10.1016/j.compbiomed.2019.103339
Graves, N., Walker, D., Raine, R., Hutchings, A., Roberts, J.A., 2002. Cost data for individual patients included in clinical studies: no amount of statistical analysis can compensate for inadequate costing methods. Health economics 11, 735–739.
Gray, A.M., Clarke, P.M., Wolstenholme, J.L., Wordsworth, S., 2011. Applied methods of cost-effectiveness analysis in healthcare. Oxford University Press.
Green, N., Lamrock, F., Naylor, N., Williams, J., Briggs, A., 2023. Health Economic Evaluation Using Markov Models in R for Microsoft Excel Users: A Tutorial. PharmacoEconomics 41, 5–19. https://doi.org/10.1007/s40273-022-01199-7
Gregory, J., Smith, S., Birnie, R., 2023. MAIC: Package to perform matched-adjusted indirect comparisons. https://doi.org/10.5281/zenodo.6624151
Griffin, E., Hyde, C., Long, L., Varley-Campbell, J., Coelho, H., Robinson, S., Snowsill, T., 2020. Lung cancer screening by low-dose computed tomography: a cost-effectiveness analysis of alternative programmes in the UK using a newly developed natural history-based economic model. Diagnostic and Prognostic Research 4. https://doi.org/10.1186/s41512-020-00087-y
Griffiths, J.D., Jones, M., Read, M.S., Williams, J.E., 2010. A simulation model of bed-occupancy in a critical care unit. Journal of Simulation 4, 52–59. https://doi.org/10.1057/jos.2009.22
Guideline, N., 2024. Asthma: Diagnosis, monitoring and chronic asthma management. London: National Institute for Health and Care Excellence.
Guyot, P., Ades, A., Ouwens, M.J., Welton, N.J., 2012. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Medical Research Methodology 12. https://doi.org/10.1186/1471-2288-12-9
Haacker, M., Hallett, T.B., Atun, R., 2020. On discount rates for economic evaluations in global health. Health Policy Plan 35, 107–114. https://doi.org/10.1093/heapol/czz127
Haji Ali Afzali, H., Karnon, J., 2015. Exploring Structural Uncertainty in Model-Based Economic Evaluations. PharmacoEconomics 33, 435–443. https://doi.org/10.1007/s40273-015-0256-0
Harari, O., Soltanifar, M., Cappelleri, J.C., Verhoek, A., Ouwens, M., Daly, C., Heeg, B., 2023. Network meta‐interpolation: Effect modification adjustment in network meta‐analysis using subgroup analyses. Research Synthesis Methods 14, 211–233. https://doi.org/10.1002/jrsm.1608
Hariton, E., Locascio, J.J., 2018. Randomised controlled trials - the gold standard for effectiveness research: Study design: Randomised controlled trials. BJOG 125, 1716. https://doi.org/10.1111/1471-0528.15199
Härkänen, T., Maljanen, T., Lindfors, O., Virtala, E., Knekt, P., 2013. Confounding and missing data in cost-effectiveness analysis: comparing different methods. Health economics review 3, 1–11.
Hart, R., Burns, D., Ramaekers, B., Ren, S., Gladwell, D., Sullivan, W., Davison, N., Saunders, O., Sly, I., Cain, T., al., et, 2020. R and Shiny for Cost-Effectiveness Analyses: Why and When? A Hypothetical Case Study. PharmacoEconomics 38, 765–776. https://doi.org/10.1007/s40273-020-00903-9
Hart, R., Hassan, F., Alulis, S., Patterson, K.W., Barthelmes, J.N., Boer, J.H., Lee, D., 2024. Modelling treatment sequences in immunology: Optimizing patient outcomes. Advances in Therapy 41, 2010–2027.
Hatswell, A.J., Baio, G., Berlin, J.A., Irs, A., Freemantle, N., 2016. Regulatory approval of pharmaceuticals without a randomised controlled study: Analysis of EMA and FDA approvals 19992014. BMJ Open 6, e011666. https://doi.org/10.1136/bmjopen-2016-011666
Hatswell, A.J., Bullement, A., Briggs, A., Paulden, M., Stevenson, M.D., 2018. Probabilistic Sensitivity Analysis in Cost-Effectiveness Models: Determining Model Convergence in Cohort Models. PharmacoEconomics 36, 1421–1426. https://doi.org/10.1007/s40273-018-0697-3
Hawkins, N., Sculpher, M., Epstein, D., 2005. Cost-effectiveness analysis of treatments for chronic disease: Using r to incorporate time dependency of treatment response. Med Decis Making 25, 511–9. https://doi.org/10.1177/0272989X05280562
Heath, A., Baio, G., 2018. Calculating the Expected Value of Sample Information Using Efficient Nested Monte Carlo: A Tutorial. Value in Health 21, 1299–1304.
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, 346–358.
Heath, A., Manolopoulou, I., Baio, G., 2018. Efficient Monte Carlo Estimation of the Expected Value of Sample Information Using Moment Matching. Medical Decision Making 38, 163–173.
Heath, A., Manolopoulou, I., Baio, G., 2017. A Review of Methods for Analysis of the Expected Value of Information. Medical Decision Making 37, 747–758.
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.
Heath, A., Strong, M., Glynn, D., Kunst, N., Welton, N.J., Goldhaber-Fiebert, J.D., 2022. Simulating study data to support expected value of sample information calculations: a tutorial. Medical Decision Making 42, 143–155.
Heidelberger, P., Welch, P.D., 1983. Simulation run length control in the presence of an initial transient. Operations Research 31, 1109–1144.
Hess, S. original by K., Gentleman, R. port by R., 2021. Muhaz: Hazard function estimation in survival analysis.
Higgins, J.P., Thompson, S.G., 2002. Quantifying heterogeneity in a meta-analysis. Statistics in Medicine 21, 1539–58. https://doi.org/10.1002/sim.1186
Higgins, J.P., Thompson, S.G., Deeks, J.J., Altman, D.G., 2003. Measuring inconsistency in meta-analyses. BMJ 327, 557–60. https://doi.org/10.1136/bmj.327.7414.557
HIQA, 2019. Guidelines for the economic evaluation of health technologies in ireland health information and quality authority.
Hoaglin, D.C., Hawkins, N., Jansen, J.P., Scott, D.A., Itzler, R., Cappelleri, J.C., Boersma, C., Thompson, D., Larholt, K.M., Diaz, M., Barrett, A., 2011. Conducting indirect-treatment-comparison and network-meta-analysis studies: Report of the ISPOR task force on indirect treatment comparisons good research practices: Part 2. Value Health 14, 429–37. https://doi.org/10.1016/j.jval.2011.01.011
Howard, R.A., 1960. Dynamic programming and Markov processes. Technology Press of Massachusetts Institute of Technology.
HTA CG, 2024. Practical guideline for quantitative evidence synthesis: Direct and indirect comparisons (adopted on 8 march 2024). Member State Coordination Group on Health Technology Assessment.
hta-pharma, 2024. Maicplus: Matching adjusted indirect comparison.
Hunink, M.G.M., Weinstein, M.C., Wittenberg, E., 2014. Decision Making in Health and Medicine, Cambridge medicine. Cambridge University Press.
Hunter, E., Kelleher, J.D., 2021. Using a hybrid agent-based and equation based model to test school closure policies during a measles outbreak. BMC Public Health 21. https://doi.org/10.1186/s12889-021-10513-5
Hutton, B., Salanti, G., Caldwell, D.M., Chaimani, A., Schmid, C.H., Cameron, C., Ioannidis, J.P.A., Straus, S., Thorlund, K., Jansen, J.P., Mulrow, C., Catalá-López, F., Gøtzsche, P.C., Dickersin, K., Boutron, I., Altman, D.G., Moher, D., 2015. The PRISMA Extension Statement for Reporting of Systematic Reviews Incorporating Network Meta-analyses of Health Care Interventions: Checklist and Explanations. Annals of Internal Medicine 162, 777–784. https://doi.org/10.7326/m14-2385
Ishak, K.J., Proskorovsky, I., Benedict, A., 2015. Simulation and matching-based approaches for indirect comparison of treatments. Pharmacoeconomics 33, 537–549. https://doi.org/10.1007/s40273-015-0271-1
Izadi, N., Koohi, F., Safarpour, M., Naseri, P., Rahimi, S., Khodakarim, S., 2020. Estimating the cure proportion of colorectal cancer and related factors after surgery in patients using parametric cure models. Gastroenterology, Hepatology and Bed Bench 13, 125–132.
Jackman, S., 2009. Bayesian analysis for the social sciences. John Wiley & Sons.
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. https://doi.org/10.18637/jss.v070.i08
Jackson, C., 2011. Multi-State Models for Panel Data: The msm Package for R. Journal of Statistical Software 38, 1–28. https://doi.org/10.18637/jss.v038.i08
Jackson, C., Best, N., Richardson, S., 2008. Hierarchical related regression for combining aggregate and individual data in studies of socio-economic disease risk factors. Journal of the Royal Statistical Society Series A (Statistics in Society) 171, 159–78.
Jackson, C., Best, N., Richardson, S., 2006. Improving ecological inference using individual-level data. Statistics in Medicine 25, 2136–59. https://doi.org/10.1002/sim.2370
Jackson, C., Bojke, L., Thompson, S.G., Claxton, K., Sharples, L.D., 2011. A framework for addressing structural uncertainty in decision models. Medical Decision Making 31, 662–674. https://doi.org/10.1177/0272989X11406986
Jackson, C., Stevens, J., Ren, S., Latimer, N., Bojke, L., Manca, A., Sharples, L., 2016. Extrapolating Survival from Randomized Trials Using External Data: A Review of Methods. Medical Decision Making 37, 377–390. https://doi.org/10.1177/0272989x16639900
Jackson, D., Barrett, J.K., Rice, S., White, I.R., Higgins, J.P., 2014. A design-by-treatment interaction model for network meta-analysis with random inconsistency effects. Statistics in Medicine 33, 3639–54. https://doi.org/10.1002/sim.6188
Jackson, D., Rhodes, K., Ouwens, M., 2020. Alternative weighting schemes when performing matching‐adjusted indirect comparisons. Research Synthesis Methods 12, 333–346. https://doi.org/10.1002/jrsm.1466
Jackson, D., White, I.R., Riley, R.D., 2012. Quantifying the impact of between-study heterogeneity in multivariate meta-analyses. Statistics in Medicine 31, 3805–20. https://doi.org/10.1002/sim.5453
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.
Jalal, H., Pechlivanoglou, P., Krijkamp, E., Alarid-Escudero, F., Enns, E., Hunink, M.G.M., 2017. An Overview of R in Health Decision Sciences. Medical Decision Making 37, 735–746. https://doi.org/10.1177/0272989x16686559
Jansen, J.P., 2012. Network meta-analysis of individual and aggregate level data. Research Synthesis Methods 3, 177–90. https://doi.org/10.1002/jrsm.1048
Jansen, J.P., 2011. Network meta-analysis of survival data with fractional polynomials. BMC Medical Research Methodology 11, 61. https://doi.org/10.1186/1471-2288-11-61
Jansen, J.P., Fleurence, R., Devine, B., Itzler, R., Barrett, A., Hawkins, N., Lee, K., Boersma, C., Annemans, L., Cappelleri, J.C., 2011. Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: Report of the ISPOR task force on indirect treatment comparisons good research practices: Part 1. Value Health 14, 417–28. https://doi.org/10.1016/j.jval.2011.04.002
Jansen, J.P., Incerti, D., Linthicum, M.T., 2019. Developing Open-Source Models for the US Health System: Practical Experiences and Challenges to Date with the Open-Source Value Project. PharmacoEconomics 37, 1313–1320. https://doi.org/10.1007/s40273-019-00827-z
Jansen, J.P., Incerti, D., Trikalinos, T.A., 2023. Multi-state network meta-analysis of progression and survival data. Statistics in Medicine 42, 3371–3391. https://doi.org/10.1002/sim.9810
Jensen, R.K., Clements, M., Gjaerde, L.K., Jakobsen, L.H., 2022. Fitting parametric cure models in R using the packages cuRe and rstpm2. Computational Methods Programs in Biomedicine 226, 107125. https://doi.org/10.1016/j.cmpb.2022.107125
Jun, J.B., Jacobson, S.H., Swisher, J.R., 1999. Application of discrete-event simulation in health care clinics: A survey. Journal of the Operational Research Society 50, 109–123. https://doi.org/10.1057/palgrave.jors.2600669
Kalbfleisch, J.D., Lawless, J., 1985. The analysis of panel data under a markov assumption. Journal of the American Statistical Association 80, 863–871. https://doi.org/https://doi.org/10.2307/2288545
Kalbfleisch, J.D., Prentice, R.L., 2002. The statistical analysis of failure time data, 2nd ed, Wiley series in probability and statistics. J. Wiley, Hoboken, N.J.
Kanters, S., Ford, N., Druyts, E., Thorlund, K., Mills, E.J., Bansback, N., 2016. Use of network meta-analysis in clinical guidelines. Bull World Health Organ 94, 782–784. https://doi.org/10.2471/BLT.16.174326
Karnon, J., Haji Ali Afzali, H., 2014. When to Use Discrete Event Simulation (DES) for the Economic Evaluation of Health Technologies? A Review and Critique of the Costs and Benefits of DES. PharmacoEconomics 32, 547–558. https://doi.org/10.1007/s40273-014-0147-9
Karnon, J., Stahl, J., Brennan, A., Caro, J.J., Mar, J., Möller, J., 2012. Modeling using Discrete Event Simulation: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-4. Value in Health 15, 821–827. https://doi.org/10.1016/j.jval.2012.04.013
Kassambara, A., Kosinski, M., Biecek, P., 2021. Survminer: Drawing survival curves using ’ggplot2’.
Kay, R., 1986. A markov model for analysing cancer markers and disease states in survival studies. Biometrics 42, 855–865. https://doi.org/https://doi.org/10.2307/2530699
Keynes, J.M., 1923. A tract on monetary reform. London, Macmillan.
Kleijburg, A., Lokkerbol, J., Regeer, E.J., Geerling, B., Evers, S., Kroon, H., Wijnen, B., 2022. Designing and testing of a health-economic Markov model to assess the cost-effectiveness of treatments for Bipolar disorder: TiBipoMod. Front Psychiatry 13, 1030989. https://doi.org/10.3389/fpsyt.2022.1030989
Krahn, U., Binder, H., König, J., 2013. A graphical tool for locating inconsistency in network meta-analyses. BMC medical research methodology 13, 1–18.
Krijkamp, E.M., Alarid-Escudero, F., Enns, E.A., Jalal, H.J., Hunink, M.G.M., Pechlivanoglou, P., 2018. Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial. Medical Decision Making 38, 400–422. https://doi.org/10.1177/0272989X18754513
Krijkamp, E.M., Alarid-Escudero, F., Enns, E.A., Pechlivanoglou, P., Hunink, M.G.M., Yang, A., Jalal, H.J., 2020. A Multidimensional Array Representation of State-Transition Model Dynamics. Medical Decision Making 40, 242–248. https://doi.org/10.1177/0272989X19893973
Kruschke, J., 2014. Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press, San Diego, CA, US.
Lambert, P.C., Billingham, L.J., Cooper, N.J., Sutton, A.J., Abrams, K.R., 2008. Estimating the cost-effectiveness of an intervention in a clinical trial when partial cost information is available: a Bayesian approach. Health economics 17, 67–81.
Lambert, P.C., Sutton, A.J., Abrams, K.R., Jones, D.R., 2002. A comparison of summary patient-level covariates in meta-regression with individual patient data meta-analysis. Journal of Clinical Epidemiology 55, 86–94. https://doi.org/10.1016/S0895-4356(01)00414-0
Lambert, P.C., Thompson, J.R., Weston, C.L., Dickman, P.W., 2007. Estimating and modeling the cure fraction in population-based cancer survival analysis. Biostatistics 8, 576–594.
Latimer, N.R., 2013. Survival analysis for economic evaluations alongside clinical trials–extrapolation with patient-level data: Inconsistencies, limitations, and a practical guide. Med. Decis. Making 33, 743–754.
Latimer, N.R., Rutherford, M.J., 2024. Mixture and non-mixture cure models for health technology assessment: What you need to know. Pharmacoeconomics 42, 1073–1090.
Law, W.D.K., Averill M., Kelton., W.D., 2007. Simulation modeling and analysis.
Lawson, B., Leemis, L.M., 2015. Discrete-event simulation using r, in: 2015 Winter Simulation Conference (WSC). IEEE, pp. 3502–3513.
Le Rest, K., Pinaud, D., Monestiez, P., Chadoeuf, J., Bretagnolle, V., 2014. Spatial leave-one-out cross-validation for variable selection in the presence of spatial autocorrelation. Global Ecology and Biogeography 23, 811–820. https://doi.org/10.1111/geb.12161
Leahy, J., Thom, H., Jansen, J.P., Gray, E., O’Leary, A., White, A., Walsh, C., 2019. Incorporating single-arm evidence into a network meta-analysis using aggregate level matching: Assessing the impact. Statistics in Medicine. https://doi.org/10.1002/sim.8139
Lee, D., Burns, D., Wilson, E., 2024. NICE’s pathways pilot: Pursuing good decision making in difficult circumstances. PharmacoEconomics-open 1–5.
Leemis, L., McQueston, J., 2008. Univariate Distribution Relationships. The American Statistician 62, 45–53.
Leucht, S., Chaimani, A., Cipriani, A.S., Davis, J.M., Furukawa, T.A., Salanti, G., 2016. Network meta-analyses should be the highest level of evidence in treatment guidelines. European Archive of Psychiatry Clinical Neuroscience 266, 477–80. https://doi.org/10.1007/s00406-016-0715-4
Leurent, B., Gomes, M., Carpenter, J.R., 2018a. Missing data in trial-based cost-effectiveness analysis: An incomplete journey. Health economics 27, 1024–1040.
Leurent, B., Gomes, M., Cro, S., Wiles, N., Carpenter, J.R., 2020. Reference-based multiple imputation for missing data sensitivity analyses in trial-based cost-effectiveness analysis. Health economics 29, 171–184.
Leurent, B., Gomes, M., Faria, R., Morris, S., Grieve, R., Carpenter, J.R., 2018b. Sensitivity analysis for not-at-random missing data in trial-based cost-effectiveness analysis: a tutorial. PharmacoEconomics 36, 889–901.
Lillian Yau, E.G., 2022. maicChecks: Assessing the numerical feasibility for conducting a matching-adjusted indirect comparison (MAIC).
Little, R.J., 1994. A class of pattern-mixture models for normal incomplete data. Biometrika 81, 471–483.
Little, R.J., D’Agostino, R., Cohen, M.L., Dickersin, K., Emerson, S.S., Farrar, J.T., Frangakis, C., Hogan, J.W., Molenberghs, G., Murphy, S.A., others, 2012. The prevention and treatment of missing data in clinical trials. New England Journal of Medicine 367, 1355–1360.
Little, R.J., Rubin, D.B., 2019. Statistical analysis with missing data. John Wiley & Sons.
Liu, Xing-Rong, Pawitan, Y., Clements, M., 2018. Parametric and penalized generalized survival models. Stat. Methods Med. Res. 27, 1531–1546.
Liu, X.R., Pawitan, Y., Clements, M., 2018. Parametric and penalized generalized survival models 27, 1531–1546.
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., 2009. Modeling between-trial variance structure in mixed treatment comparisons. Biostatistics 10, 792–805. https://doi.org/10.1093/biostatistics/kxp032
Lu, G., Ades, A.E., Sutton, A.J., Cooper, N.J., Briggs, A.H., Caldwell, D.M., 2007. Meta-analysis of mixed treatment comparisons at multiple follow-up times. Statistics in Medicine 26, 3681–3699.
Lumley, T., 2002. Network meta-analysis for indirect treatment comparisons. Statistics in Medicine 21, 2313–24. https://doi.org/10.1002/sim.1201
Lunn, D., Jackson, C., Best, N., Thomas, A., Spiegelhalter, D., 2013. The BUGS book : A practical introduction to bayesian analysis, Texts in statistical science. CRC Press, Boca Raton ; London.
M., B., T., H.J.P., V., H.L., R., R.H., 2017. Basics of meta-analysis: I2 is not an absolute measure of heterogeneity. Research Synthesis Methods 8, 5–18.
Maciel, D., Jansen, J.P., Klijn, S.L., Towle, K., Dhanda, D., Malcolm, B., Cope, S., 2024. Implementing multilevel network meta-regression for time-to-event outcomes: A case study in relapsed refractory multiple myeloma. Value in Health. https://doi.org/10.1016/j.jval.2024.04.017
Mainzer, R., Apajee, J., Nguyen, C.D., Carlin, J.B., Lee, K.J., 2021. A comparison of multiple imputation strategies for handling missing data in multi-item scales: Guidance for longitudinal studies. Statistics in Medicine.
Manca, A., Hawkins, N., Sculpher, M.J., 2005. Estimating mean QALYs in trial-based cost-effectiveness analysis: the importance of controlling for baseline utility. Health economics 14, 487–496.
Manca, A., Palmer, S., 2005. Handling missing data in patient-level cost-effectiveness analysis alongside randomised clinical trials. Applied health economics and health policy 4, 65–75.
Mason, A.J., Gomes, M., Carpenter, J., Grieve, R., 2021. Flexible Bayesian longitudinal models for cost-effectiveness analyses with informative missing data. Health economics.
Mason, A.J., Gomes, M., Grieve, R., Carpenter, J.R., 2018. A Bayesian framework for health economic evaluation in studies with missing data. Health economics 27, 1670–1683.
Mason, A.J., Gomes, M., Grieve, R., Ulug, P., Powell, J.T., Carpenter, J., 2017. Development of a practical approach to expert elicitation for randomised controlled trials with missing health outcomes: application to the IMPROVE trial. Clinical Trials 14, 357–367.
Matloff, N., 2017. DES: Discrete Event Simulation.
McCullagh, P., Nelder, J.A., 1989. Generalised linear models. 2nd ed. Chapman & Hall, London.
McNamara, S., Schneider, P.P., Love-Koh, J., Doran, T., Gutacker, N., 2023. Quality-adjusted life expectancy norms for the english population. Value in Health 26, 163–169.
Meira-Machado, L., Una-Alvarez, J. de, Cadarso-Suarez, C., Andersen, P.K., 2009. Multi-state models for the analysis of time-to-event data. Statistical Methods in Medical Research 18, 195–222. https://doi.org/10.1177/0962280208092301
Menn, P., Holle, R., 2009. Comparing Three Software Tools for Implementing Markov Models for Health Economic Evaluations. PharmacoEconomics 27, 745–753. https://doi.org/10.2165/11313760-000000000-00000
Menzel, P.T., 2021. How Should Willingness-to-Pay Values of Quality-Adjusted Life-Years Be Updated and According to Whom? AMA Journal of Ethics 23, E601–606. https://doi.org/10.1001/amajethics.2021.601
Menzies, N., 2016. An efficient estimator for the expected value of sample information. Medical Decision Making 36, 308–320.
Minelli, C., Baio, G., 2015. Value of information: a tool to improve research prioritization and reduce waste.
Moertel, C.G., Fleming, T.R., Macdonald, J.S., Haller, D.G., Laurie, J.A., Goodman, P.J., Ungerleider, J.S., Emerson, W.A., Tormey, D.C., Glick, J.H., Veeder, M.H., Mailliard, J.A., 1990. Levamisole and Fluorouracil for Adjuvant Therapy of Resected Colon Carcinoma. New England Journal of Medicine 322, 352–358. https://doi.org/10.1056/nejm199002083220602
Moertel, C.G., Fleming, T.R., Macdonald, J.S., Haller, D.G., Laurie, J.A., Tangen, C.M., Ungerleider, J.S., Emerson, W.A., Tormey, D.C., Glick, J.H., Veeder, M.H., Mailliard, J.A., 1995. Fluorouracil plus levamisole as effective adjuvant therapy after resection of stage III colon carcinoma: a final report. Annals of Internal Medicine 122, 321–6. https://doi.org/10.7326/0003-4819-122-5-199503010-00001
Mohd, A.R., Ghani, M.K., Awang, R.R., Su Min, J.O., Dimon, M.Z., 2010. Dermacyn irrigation in reducing infection of a median sternotomy wound. Heart Surgery Forum 13, E228–E232.
Molenberghs, G., Fitzmaurice, G., Kenward, M.G., Tsiatis, A., Verbeke, G., 2014. Handbook of missing data methodology. CRC Press.
Mood, A.M., Graybill, F.A., Boes, D.C., 1974. Introduction to the Theory of Statistics 3rd edition.
Naimark, D., Mishra, S., Barrett, K., Khan, Y.A., Mac, S., Ximenes, R., Sander, B., 2021. Simulation-Based Estimation of SARS-CoV-2 Infections Associated With School Closures and Community-Based Nonpharmaceutical Interventions in Ontario, Canada. JAMA Network Open 4, e213793. https://doi.org/10.1001/jamanetworkopen.2021.3793
Naimark, D.M.J., Bott, M., Krahn, M., 2008. The half-cycle correction explained: Two alternative pedagogical approaches. Medical Decision Making 28, 706–712. https://doi.org/10.1177/0272989X08315241
Ng, E.S., Diaz-Ordaz, K., Grieve, R., Nixon, R.M., Thompson, S.G., Carpenter, J.R., 2016. Multilevel models for cost-effectiveness analyses that use cluster randomised trial data: an approach to model choice. Statistical methods in medical research 25, 2036–2052.
NICE, 2022. NICE health technology evaluations: the manual NICE process and methods.
NICE, 2013. Guide to the methods of technology appraisal 2013. NICE Guideline (PMG9).
Niihara, S.;.R., Y.; Majumdar, 2017. Phase 3 study of l-glutamine in sickle cell disease: Analyses of time to first and second crisis and average cumulative recurrent events. Blood. Conference: 59th Annual Meeting of the American Society of Hematology, ASH 130.
Noble, S.M., Hollingworth, W., Tilling, K., 2012. Missing data in trial-based cost-effectiveness analysis: The current state of play. Health economics 21, 187–200.
O’Hagan, A., 2019. Expert knowledge elicitation: subjective but scientific. The American Statistician 73, 69–81.
O’Hagan, A., Buck, C.E., Daneshkhah, A., Eiser, J.R., Garthwaite, P.H., Jenkinson, D.J., Oakley, J.E., Rakow, T., 2006. Uncertain judgements: eliciting experts’ probabilities.
O’Hagan, A., Stevens, J.W., 2001. A framework for cost-effectiveness analysis from clinical trial data. Health Economics 10, 303–315.
Oostenbrink, J.B., Al, M.J., 2005. The analysis of incomplete cost data due to dropout. Health economics 14, 763–776.
Oostenbrink, J.B., Al, M.J., Rutten-van Mölken, M.P., 2003. Methods to analyse cost data of patients who withdraw in a clinical trial setting. PharmacoEconomics 21, 1103–1112.
Ouwens, M.J.N.M., Philips, Z., Jansen, J.P., 2010. Network meta-analysis of parametric survival curves. Research Synthesis Methods 1, 258–271. https://doi.org/10.1002/jrsm.25
Owen, J., 2022.
Pahuta, M.A., Werier, J., Wai, E.K., Patchell, R.A., Coyle, D., 2019. A technique for approximating transition rates from published survival analyses. Cost Eff Resour Alloc 17, 12. https://doi.org/10.1186/s12962-019-0182-7
Park, J.E., Campbell, H., Towle, K., Yuan, Y., Jansen, J.P., Phillippo, D.M., Cope, S., 2024. Unanchored population-adjusted indirect comparison methods for time-to-event outcomes using inverse odds weighting, regression adjustment, and doubly robust methods with either individual patient or aggregate data. Value in Health 27, 278–286. https://doi.org/10.1016/j.jval.2023.11.011
Patel, N., Beeken, R.J., Leurent, B., Omar, R.Z., Nazareth, I., Morris, S., 2018. Cost-effectiveness of habit-based advice for weight control versus usual care in general practice in the Ten Top Tips (10TT) trial: economic evaluation based on a randomised controlled trial. BMJ open 8, e017511.
Pedder, H., Dias, S., Bennetts, M., Boucher, M., Welton, N.J., 2021. Joining the dots: Linking disconnected networks of evidence using dose-response model-based network meta-analysis. Medical Decision Making 41, 194–208. https://doi.org/10.1177/0272989X20983315
Perencevich, E.N., Sands, K.E., Cosgrove, S.E., Guadagnoli, E., Meara, E., Platt, R., 2003. Health and economic impact of surgical site infections diagnosed after hospital discharge. Emerg Infect Dis 9, 196–203. https://doi.org/10.3201/eid0902.020232
Peterson, C.M., Medchill, M., Gordon, D.S., Chard, H.L., 1990. Cesarean prophylaxis: A comparison of cefamandole and cefazolin by both intravenous and lavage routes, and risk factors associated with endometritis. Obstetrics and Gynecology 75, 179–182.
Philips, Z., Claxton, K., Palmer, S., 2008. The half-life of truth: What are appropriate time horizons for research decisions? Medical Decision Making 28, 287–299.
Phillippo, D.M., 2024. Multinma: Network meta-analysis of individual and aggregate data in stan. https://doi.org/10.5281/zenodo.3904454
Phillippo, D.M., 2019. Calibration of treatment effects in network meta-analysis using individual patient data (PhD thesis). University of Bristol.
Phillippo, D.M., Ades, A., Dias, S., Palmer, S., Abrams, K., Welton, N., 2016a. NICE DSU technical support document 18: Methods for population-adjusted indirect comparisons in submissions to NICE. Report by the Decision Support Unit.
Phillippo, D.M., Ades, AE, Dias, S., Palmer, S., Abrams, K., Welton, N., 2016b. Technical Support Document 18: Methods for population-adjusted indirect comparisons in submission to NICE. NICE Decision Support Unit, Sheffield, UK.
Phillippo, D.M., Ades, A.E., Dias, S., Palmer, S., Abrams, K.R., Welton, N.J., 2018. Methods for population-adjusted indirect comparisons in health technology appraisal. Medical Decision Making 38, 200–211. https://doi.org/10.1177/0272989x17725740
Phillippo, D.M., Dias, S., Ades, A.E., Belger, M., Brnabic, A., Saure, D., Schymura, Y., Welton, N.J., 2023. Validating the assumptions of population adjustment: Application of multilevel network meta-regression to a network of treatments for plaque psoriasis. Medical Decision Making 43, 53–67. https://doi.org/10.1177/0272989X221117162
Phillippo, D.M., Dias, S., Ades, A.E., Belger, M., Brnabic, A., Schacht, A., Saure, D., Kadziola, Z., Welton, N.J., 2020a. Multilevel network meta-regression for population-adjusted treatment comparisons. Journal of the Royal Statistical Society: Series A (Statistics in Society) 183, 1189–1210. https://doi.org/10.1111/rssa.12579
Phillippo, D.M., Dias, S., Ades, A.E., Welton, N.J., 2024. Multilevel network meta-regression for general likelihoods: Synthesis of individual and aggregate data with applications to survival analysis. arXiv. https://doi.org/10.48550/arXiv.2401.12640
Phillippo, D.M., Dias, S., Ades, A.E., Welton, N.J., 2021. Target estimands for efficient decision making: Response to comments on “assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study.” Statistics in Medicine 40, 2759–2763. https://doi.org/10.1002/sim.8965
Phillippo, D.M., Dias, S., Ades, A.E., Welton, N.J., 2020b. Equivalence of entropy balancing and the method of moments for matching‐adjusted indirect comparison. Research Synthesis Methods 11, 568–572. https://doi.org/10.1002/jrsm.1416
Phillippo, D.M., Dias, S., Ades, A.E., Welton, N.J., 2020c. Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study. Statistics in Medicine. https://doi.org/10.1002/sim.8759
Phillippo, D.M., Dias, S., Elsada, A., Ades, A.E., Welton, N.J., 2019. Population adjustment methods for indirect comparisons: A review of national institute for health and care excellence technology appraisals. International Journal of Technology Assessment in Health Care. https://doi.org/10.1017/S0266462319000333
Pichon-Riviere, A., Drummond, M., Palacios, A., Garcia-Marti, S., Augustovski, F., 2023a. Determining the efficiency path to universal health coverage: Cost-effectiveness thresholds for 174 countries based on growth in life expectancy and health expenditures. Lancet Glob Health 11, e833–e842. https://doi.org/10.1016/S2214-109X(23)00162-6
Pichon-Riviere, A., Drummond, M., Palacios, A., Garcia-Marti, S., Augustovski, F., 2023b. Determining the efficiency path to universal health coverage: cost-effectiveness thresholds for 174 countries based on growth in life expectancy and health expenditures. Lancet Global Health 11, e833–e842. https://doi.org/10.1016/S2214-109X(23)00162-6
Pieters, Z., Strong, M., Pitzer, V.E., Beutels, P., Bilcke, J., 2020. A Computationally Efficient Method for Probabilistic Parameter Threshold Analysis for Health Economic Evaluations. Medical Decision Making 40, 669–679. https://doi.org/10.1177/0272989x20937253
Plummer, M., 2024. Rjags: Bayesian graphical models using MCMC.
Plummer, M., 2013. JAGS: Just another gibbs sampler. http://mcmc-jags.sourceforge.net.
Plummer, M., others, 2003. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling, in: Proceedings of the 3rd International Workshop on Distributed Statistical Computing. Vienna, Austria., pp. 1–10.
Pokharel, R., Lin, Y.-S., McFerran, E., O’Mahony, J.F., 2023. A systematic review of cost-effectiveness analyses of colorectal cancer screening in Europe: have studies included optimal screening intensities? Applied Health Economics and Health Policy 21, 701–717.
Prentice, R., 1975. Discrimination among some parametric models. Biometrika 62, 607–614.
Putter, H., Fiocco, M., Geskus, R.B., 2007. Tutorial in biostatistics: Competing risks and multi-state models. Stat Med 26, 2389–430. https://doi.org/10.1002/sim.2712
Rabin, R., de Charro, F., 2001. EQ-SD: a measure of health status from the EuroQol Group. Annals of Medicine 33, 337–343.
Raftery, A.E., Lewis, S.M., 1992. One long run with diagnostics: Implementation strategies for Markov chain Monte Carlo. Statistical Science 7, 493–497.
Raghunathan, T.E., Lepkowski, J.M., Van Hoewyk, J., Solenberger, P., others, 2001. A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey methodology 27, 85–96.
Raiffa, H., Schlaifer, H., 1961. Applied Statistical Decision Theory. Harvard University Press, Boston, MA.
Remiro-Azócar, A., 2022a. Target estimands for population‐adjusted indirect comparisons. Statistics in Medicine 41, 5558–5569. https://doi.org/10.1002/sim.9413
Remiro-Azócar, A., 2022b. Some considerations on target estimands for health technology assessment. Statistics in Medicine 41, 5592–5596. https://doi.org/10.1002/sim.9566
Remiro-Azócar, A., Heath, A., Baio, G., 2022. Parametric g-computation for compatible indirect treatment comparisons with limited individual patient data. Research Synthesis Methods. https://doi.org/10.1002/jrsm.1565
Remiro-Azócar, A., Heath, A., Baio, G., 2021a. Methods for population adjustment with limited access to individual patient data: A review and simulation study. Research Synthesis Methods 12, 750–775. https://doi.org/10.1002/jrsm.1511
Remiro-Azócar, A., Heath, A., Baio, G., 2021b. Conflating marginal and conditional treatment effects: Comments on “assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study.” Statistics in Medicine 40, 2753–2758. https://doi.org/10.1002/sim.8857
Rhodes, K.M., Turner, R.M., White, I.R., Jackson, D., Spiegelhalter, D., Higgins, J.P., 2016. Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data. Statistics in Medicine 35, 5495–5511. https://doi.org/10.1002/sim.7090
Riley, R.D., Dias, S., Donegan, S., Tierney, J.F., Stewart, L.A., Efthimiou, O., Phillippo, D.M., 2023. Using individual participant data to improve network meta-analysis projects. BMJ Evidence-Based Medicine 28, 197–203. https://doi.org/10.1136/bmjebm-2022-111931
Riley, R.D., Lambert, P.C., Abo-Zaid, G., 2010. Meta-analysis of individual participant data: Rationale, conduct, and reporting. British Medical Journal 340. https://doi.org/10.1136/bmj.c221
Roberts, M., Russell, L.B., Paltiel, A.D., Chambers, M., McEwan, P., Krahn, M., 2012. Conceptualizing a model: A report of the ISPOR-SMDM modeling good research practices task force-2. Medical Decision Making 32, 678–689. https://doi.org/10.1177/0272989X12454941
Rombach, I., Gray, A.M., Jenkinson, C., Murray, D.W., Rivero-Arias, O., 2018. Multiple imputation for patient reported outcome measures in randomised controlled trials: advantages and disadvantages of imputing at the item, subscale or composite score level. BMC medical research methodology 18, 1–16.
Rombach, I., Rivero-Arias, O., Gray, A.M., Jenkinson, C., Burke, O., 2016. The current practice of handling and reporting missing outcome data in eight widely used PROMs in RCT publications: a review of the current literature. Quality of Life Research 25, 1613–1623.
Royston, P., Parmar, M.K., 2013. Restricted mean survival time: An alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome. BMC medical research methodology 13, 1–15.
RStudio, 2020c. Create an icon.
RStudio, 2020a.
RStudio, 2020b. Lesson 3 - add control widgets.
RStudio, 2017.
RStudio Team, 2020. RStudio: Integrated Development Environment for R. RStudio, PBC., Boston, MA.
RStudioshinydashboard, 2020.
Rubin, D.B., 2004. Multiple imputation for nonresponse in surveys. John Wiley & Sons.
Rucker, G., Petropoulou, M., Schwarzer, G., 2020. Network meta-analysis of multicomponent interventions. Biom J 62, 808–821. https://doi.org/10.1002/bimj.201800167
Rucker, G., Schmitz, S., Schwarzer, G., 2021. Component network meta-analysis compared to a matching method in a disconnected network: A case study. Biom J 63, 447–461. https://doi.org/10.1002/bimj.201900339
Russek-Cohen, E., 2022. Discussion of “target estimands for population‐adjusted indirect comparisons” by antonio remiro‐azocar. Statistics in Medicine 41, 5573–5576. https://doi.org/10.1002/sim.9533
Sadeghirad, B., Foroutan, F., Zoratti, M.J., Busse, J.W., Brignardello-Petersen, R., Guyatt, G., Thabane, L., 2023. Theory and practice of bayesian and frequentist frameworks for network meta-analysis. BMJ Evid Based Med 28, 204–209. https://doi.org/10.1136/bmjebm-2022-111928
Salanti, G., Del Giovane, C., Chaimani, A., Caldwell, D.M., Higgins, J.P., 2014. Evaluating the quality of evidence from a network meta-analysis. PLoS One 9, e99682. https://doi.org/10.1371/journal.pone.0099682
Saramago, P., Sutton, A.J., Cooper, N.J., Manca, A., 2012. Mixed treatment comparisons using aggregate and individual participant level data. Statistics in Medicine 31, 3516–3536. https://doi.org/10.1002/sim.5442
Schafer, J.L., 1997. Analysis of incomplete multivariate data. CRC press.
Schafer, J.L., Graham, J.W., 2002. Missing data: our view of the state of the art. Psychological methods 7, 147.
Schiel, A., 2022. Commentary on “target estimands for population‐adjusted indirect comparisons.” Statistics in Medicine 41, 5570–5572. https://doi.org/10.1002/sim.9517
Serret-Larmande, A., Zenati, B., Dechartres, A., Lambert, J., Hajage, D., 2023. A methodological review of population-adjusted indirect comparisons reveals inconsistent reporting and suggests publication bias. Journal of Clinical Epidemiology 163, 1–10. https://doi.org/10.1016/j.jclinepi.2023.09.004
Siebert, U., Alagoz, O., Bayoumi, A.M., Jahn, B., Owens, D.K., Cohen, D.J., Kuntz, K.M., 2012. State-Transition Modelling: A report of the ISPOR-SMDM Modelling Good Research Practices Task Force-3. Value in Health 15, 812–820.
Signorovitch, J.E., Sikirica, V., Erder, M.H., Xie, J., Lu, M., Hodgkins, P.S., Betts, K.A., Wu, E.Q., 2012. Matching-adjusted indirect comparisons: A new tool for timely comparative effectiveness research. Value in Health 15, 940–947.
Signorovitch, J.E., Wu, E.Q., Yu, A.P., Gerrits, C.M., Kantor, E., Bao, Y.J., Gupta, S.R., Mulani, P.M., 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. https://doi.org/10.2165/11538370-000000000-00000
Sikirica, V., Findling, R.L., Signorovitch, J., Erder, M.H., Dammerman, R., Hodgkins, P., Lu, M., Xie, J., Wu, E.Q., 2013. Comparative efficacy of guanfacine extended release versus atomoxetine for the treatment of attention-deficit/hyperactivity disorder in children and adolescents: Applying matching-adjusted indirect comparison methodology. CNS Drugs 27, 943–53. https://doi.org/10.1007/s40263-013-0102-x
Simons, C.L., Rivero-Arias, O., Yu, L.-M., Simon, J., 2015. Multiple imputation to deal with missing EQ-5D-3L data: Should we impute individual domains or the actual index? Quality of Life Research 24, 805–815.
Simpson, K.N., Strassburger, A., Jones, W.J., Dietz, B., Rajagopalan, R., 2009. Comparison of Markov Model and Discrete-Event Simulation Techniques for HIV. PharmacoEconomics 27, 159–165. https://doi.org/10.2165/00019053-200927020-00006
Smith, R., Schneider, P., 2020. Making health economic models Shiny: A tutorial. Wellcome Open Research 5, 69. https://doi.org/10.12688/wellcomeopenres.15807.1
Soares, M.O., Castro, L.C., 2012. Continuous Time Simulation and Discretized Models for Cost-Effectiveness Analysis. PharmacoEconomics 30, 1101–17. https://doi.org/https://doi.org/10.2165/11599380-000000000-00000
Spiegelhalter, D., Best, N.G., 2003. Bayesian approaches to multiple sources of evidence and uncertainty in complex cost-effectiveness modelling. Statistics in Medicine 22, 3687–3709. https://doi.org/10.1002/sim.1586
Spiegelhalter, D., Best, N.G., Carlin, B.P., Van Der Linde, A., 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64, 583–639.
Spiegelhalter, D., R., A.K., P, M.J., 2004. Bayesian approaches to clinical trials and health-care evaluation.
Spieker, A.J., 2022. Comments on the debate between marginal and conditional estimands. Statistics in Medicine 41, 5589–5591. https://doi.org/10.1002/sim.9558
Srivastava, T., Strong, M., Stevenson, M.D., Dodd, P.J., 2020. Improving cycle corrections in discrete time markov models : A gaussian quadrature approach. Improving cycle corrections in discrete time markov models: A gaussian quadrature approach. medRxiv. https://doi.org/10.1101/2020.07.27.20162651
Stahl, J.E., Rattner, D., Wiklund, R., Lester, J., Beinfeld, M., Gazelle, G.S., 2004. Reorganizing the System of Care Surrounding Laparoscopic Surgery: A Cost-Effectiveness Analysis Using Discrete-Event Simulation. Medical Decision Making 24, 461–471. https://doi.org/10.1177/0272989x04268951
Standfield, L., Comans, T., Scuffham, P., 2014. MARKOV MODELING AND DISCRETE EVENT SIMULATION IN HEALTH CARE: A SYSTEMATIC COMPARISON. International Journal of Technology Assessment in Health Care 30, 165–172. https://doi.org/10.1017/s0266462314000117
Steuten, L., Wetering, G. van de, Groothuis-Oudshoorn, K., Retèl, V., 2013. A Systematic and Critical Review of the Evolving Methods and Applications of Value of Information in Academia and Practice. PharmacoEconomics 31, 25–48. https://doi.org/10.1007/s40273-012-0008-3
Stevens, J.W., Fletcher, C., Downey, G., Sutton, A., 2018. A review of methods for comparing treatments evaluated in studies that form disconnected networks of evidence. Research Synthesis Methods 9, 148–162. https://doi.org/10.1002/jrsm.1278
Stinnett, A., Mullahy, J., 1998. Net health benefits a new framework for the analysis of uncertainty in cost-effectiveness analysis. Medical Decision Making 18, S68–S80.
Strober, B., Brnabic, A., Schacht, A., Mallbris, L., See, K., Warren, R.B., Nast, A., 2016. Indirect comparison of ixekizumab and secukinumab using matched-adjusted indirect comparisons.
Strong, M., Breeze, P., Thomas, C., Brennan, A., 2014a. SAVI - Sheffield Accelerated Value of Information, Release version 1.013 (2014-12-11). The University of Sheffield.
Strong, M., Oakley, J., Brennan, A., 2014b. Estimating Multiparameter Partial Expected Value of Perfect Information from a Probabilistic Sensitivity Analysis Sample A Nonparametric Regression Approach. Medical Decision Making 34, 311–326.
Strong, M., Oakley, J., Brennan, A., 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, 570–583.
Strong, M., Oakley, J.E., 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. https://doi.org/10.1111/j.1467-9876.2011.01014.x
Sutton, A.J., Kendrick, D., Coupland, C.A.C., 2008. Meta-analysis of individual- and aggregate-level data. Statistics in Medicine 27, 651–669. https://doi.org/10.1002/sim.2916
Team, S.D., 2023. Stan modeling language users guide and reference manual, version 2.32.
Thaçi, D., Körber, A., Kiedrowski, R., Bachhuber, T., Melzer, N., Kasparek, T., Duetting, E., Kraehn-Senftleben, G., Amon, U., Augustin, M., 2019. Secukinumab is effective in treatment of moderate-to-severe plaque psoriasis: Real-life effectiveness and safety from the PROSPECT study. Journal of the European Academy of Dermatology and Venereology 34, 310–318. https://doi.org/10.1111/jdv.15962
The Comparative Health Outcomes Policy and Economics Institute, CHOICE, 2020.
Thokala, P., Goodacre, S., Ward, M., Penn-Ashman, J., Perkins, G., 2015. Cost-effectiveness of out-of-hospital continuous positive airway pressure for acute respiratory failure. Annals of emergency medicine 65, 556–563.
Thom, H.H., Jackson, C., Commenges, D., Sharples, L.D., 2015. State selection in Markov models for panel data with application to psoriatic arthritis. Statistics in Medicine 34, 2456–75. https://doi.org/10.1002/sim.6460
Thom, H., Jackson, C., Welton, N.J., Sharples, L., 2017. Using parameter constraints to choose state structures in cost-effectiveness modelling. PharmacoEconomics. https://doi.org/10.1007/s40273-017-0501-9
Thom, H., Jansen, J., Shafrin, J., Zhao, L., Joseph, G., Cheng, H.Y., Gupta, S., Shah, N., 2020a. Crizanlizumab and comparators for adults with sickle cell disease: A systematic review and network meta-analysis. BMJ Open 10, e034147. https://doi.org/10.1136/bmjopen-2019-034147
Thom, H., Leahy, J., Jansen, J.P., 2022. Network meta-analysis on disconnected evidence networks when only aggregate data are available: Modified methods to include disconnected trials and single-arm studies while minimizing bias. Medical Decision Making 42, 906–922. https://doi.org/10.1177/0272989x221097081
Thom, H., Norman, G., Welton, N.J., Crosbie, E.J., Blazeby, J., Dumville, J.C., 2020b. Intra-cavity lavage and wound irrigation for prevention of surgical site infection: Systematic review and network meta-analysis. Surg Infect (Larchmt). https://doi.org/10.1089/sur.2019.318
Thompson, S.G., Nixon, R.M., 2005. How sensitive are cost-effectiveness analyses to choice of parametric distributions? Medical Decision Making 25, 416–423.
Trew, G., Pistofidis, G., Pados, G., Lower, A., Mettler, L., Wallwiener, D., et, a.l., 2011. Gynaecological endoscopic evaluation of 4. Human Reproduction 26, 2015–2027.
Ucar, I., Smeets, B., Azcorra, A., 2019. simmer: Discrete-Event Simulation for R 90. https://doi.org/10.18637/jss.v090.i02
Ucar, I., Smeets, B., Azcorra, A., 2017. Simmer: Discrete-event simulation for r. arXiv preprint arXiv:1705.09746.
Van Asselt, A.D., Van Mastrigt, G.A., Dirksen, C.D., Arntz, A., Severens, J.L., Kessels, A.G., 2009. How to deal with cost differences at baseline. PharmacoEconomics 27, 519–528.
Van Buuren, S., 2007. Multiple imputation of discrete and continuous data by fully conditional specification. Statistical methods in medical research 16, 219–242.
van Rosmalen, J., Toy, M., O’Mahony, J.F., 2013. A mathematical approach for evaluating Markov models in continuous time without discrete-event simulation. Medical Decision Making 33, 767–79. https://doi.org/10.1177/0272989X13487947
Viechtbauer, W., 2010. Conducting meta-analyses in R with the metafor package. Journal of Statistical Software 36, 1–48. https://doi.org/10.18637/jss.v036.i03
Von Hippel, P.T., 2020. How many imputations do you need? A two-stage calculation using a quadratic rule. Sociological Methods & Research 49, 699–718.
Warren, R.B., Brnabic, A., Saure, D., Langley, R.G., See, K., Wu, J.J., Schacht, A., Mallbris, L., Nast, A., 2018. Matching-adjusted indirect comparison of efficacy in patients with moderate-to-severe plaque psoriasis treated with ixekizumab vs. Secukinumab. British Journal of Dermatology 178, 1064–1071. https://doi.org/10.1111/bjd.16140
Welton, N.J., McAleenan, A., Thom, H.H.Z., Davies, P., Hollingworth, W., Higgins, J.P.T., Okoli, G., Sterne, J.A.C., Feder, G., Eaton, D., Hingorani, A., Fawsitt, C., Lobban, T., Bryden, P., Richards, A., Sofat, R., 2017. Screening strategies for atrial fibrillation: A systematic review and cost-effectiveness analysis. Health Technology Assessment 21, vii–235. https://doi.org/10.3310/hta21290
Welton, N.J., Soares, M.O., Palmer, S., Ades, A.E., Harrison, D., Shankar-Hari, M., Rowan, K.M., 2015. Accounting for heterogeneity in relative treatment effects for use in cost-effectiveness models and value-of-information analyses. Medical Decision Making 35, 608–621. https://doi.org/10.1177/0272989x15570113
Welton, N.J., Sutton, A.J., Cooper, N.J., Abrams, K.R., Ades, A.E., 2012. Evidence synthesis for decision making in healthcare. John Wiley; Sons.
White, I.R., Royston, P., Wood, A.M., 2011. Multiple imputation using chained equations: issues and guidance for practice. Statistics in Medicine 30, 377–399.
Wickham, H., 2019. Advanced R. "CRC Press".
Wickham, H., 2016. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
Wickham, H., Çetinkaya-Rundel, M., Grolemund, G., 2023. R for data science. O’Reilly Media, Inc.
Willan, A., Briggs, A., 2006. The statistical analysis of cost-effectiveness data. John Wiley; Sons, Chichester, UK.
Willan, A., Pinto, E., 2005. The value of information and optimal clinical trial design. Statistics in Medicine 24, 1791–1806.
Williams, C., Lewsey, J.D., Briggs, A.H., Mackay, D.F., 2017. Cost-effectiveness Analysis in R Using a Multi-state Modeling Survival Analysis Framework: A Tutorial. Medical Decision Making 37, 340–352. https://doi.org/10.1177/0272989X16651869
Wilson, E.C.F., 2021. Methodological Note: Reporting Deterministic versus Probabilistic Results of Markov, Partitioned Survival and Other Non-Linear Models. Applied Health Economics and Health Policy 19, 789–795. https://doi.org/10.1007/s40258-021-00664-2
Woods, B.S., Hawkins, N., Scott, D.A., 2010. Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: A tutorial. BMC Medical Research Methodology 10, 54. https://doi.org/10.1186/1471-2288-10-54
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. Report by the Decision Support Unit.
Woods, B.S., Sideris, E., Palmer, S., Latimer, N., Soares, M., 2020. Partitioned survival and state transition models for healthcare decision making in oncology: Where are we now? Value Health 23, 1613–1621. https://doi.org/10.1016/j.jval.2020.08.2094
Wreede, LC., Fiocco, M., Putter, H., 2011. Mstate: An r package for the analysis of competing risks and multi-state models. Journal of Statistical Software 38.
Wullink, G., Van Houdenhoven, M., Hans, E.W., Oostrum, J.M. van, Lans, M. van der, Kazemier, G., 2007. Closing Emergency Operating Rooms Improves Efficiency. Journal of Medical Systems 31, 543–546. https://doi.org/10.1007/s10916-007-9096-6
Xenakis, J.G., Kinter, E.T., Ishak, K.J., Ward, A.J., Marton, J.P., Willke, R.J., Davies, S., Caro, J.J., 2011. A discrete-event simulation of smoking-cessation strategies based on varenicline pivotal trial data. Pharmacoeconomics 29, 497–510.
Xie, Y., Allaire, J., Grolemund, G., 2018. R Markdown: The Definitive Guide.
Xie, Y., Cheng, J., Tan, X., 2021. DT: A Wrapper of the JavaScript Library ’DataTables’.
Yang, Y., Abel, L., Buchanan, J., Fanshawe, T., Shinkins, B., 2019. Use of Decision Modelling in Economic Evaluations of Diagnostic Tests: An Appraisal and Review of Health Technology Assessments in the UK. PharmacoEconomics - Open 3, 281–291. https://doi.org/10.1007/s41669-018-0109-9
Young, R., 2022. Maic: Matching-adjusted indirect comparison.
Zabor, E.C., Kaizer, A.M., Hobbs, B.P., 2020. Randomized controlled trials. Chest 158, S79–S87. https://doi.org/10.1016/j.chest.2020.03.013
Zeileis, A., Köll, S., Graham, N., 2020. Various versatile variances: An object-oriented implementation of clustered covariances in r. Journal of Statistical Software 95. https://doi.org/10.18637/jss.v095.i01
Zhang, J.Z., Rios, J.D., Pechlivanoglou, T., Yang, A., Zhang, Q., Deris, D., Cromwell, I., Pechlivanoglou, P., 2024. SurvdigitizeR: an algorithm for automated survival curve digitization. BMC Medical Research Methodology 24. https://doi.org/10.1186/s12874-024-02273-8
Zhang, X., 2018. Application of discrete event simulation in health care: a systematic review. BMC Health Services Research 18. https://doi.org/10.1186/s12913-018-3456-4
ZiN, 2024. Guideline for economic evaluation in healthcare.
Zubizarreta, J.R., 2015. Stable weights that balance covariates for estimation with incomplete outcome data. Journal of the American Statistical Association 110, 910–922. https://doi.org/10.1080/01621459.2015.1023805