يعرض 1 - 10 نتائج من 245 نتيجة بحث عن '"Sroczynski, G."', وقت الاستعلام: 0.88s تنقيح النتائج
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    دورية أكاديمية
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    دورية أكاديمية
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    دورية أكاديمية
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    دورية أكاديمية

    المصدر: GMS German Medical Science; VOL: 21; DOC06 /20230623/

    العلاقة: Abola MV, Fennimore TF, Chen MM, Chen Z, Sheth AK, Cooper G, Li L. Stool DNA-based versus colonoscopy-based colorectal cancer screening: Patient perceptions and preferences. Fam Med Community Health. 2015;3(3):2-8. DOI:10.15212/FMCH.2015.0125; Allameh Z, Davari M, Emami M. Sensitivity and Specificity of Colorectal Cancer Mass Screening Methods: A Systematic Review of the Literature. Int J Cancer Manag. 2011 Jun;4(2):e80736.; How to get Cologuard. [last accessed 2019 Jul 2]. Available from: https://www.cologuardtest.com/how-to-get-cologuardTest; Cologuard Discussion Guide. Let's talk. 2019.; Cologuardtest. FAQs. [last accessed 2020 Jun 19]. Available from: http://www.cologuardtest.com/faq/costTest; ColoAlert Stuhltest. Coloalert; [last accessed 2022 Jul 27]. Available from: https://coloalert.de/products/coloalert-stuhltestTest; Coloalert basic. Medsalus; [last accessed 2022 Jul 27]. Available from: https://medsalus.eu/shopTest/; Asselineau J, Paye A, Bessède E, Perez P, Proust-Lima C. Different latent class models were used and evaluated for assessing the accuracy of campylobacter diagnostic tests: overcoming imperfect reference standards? Epidemiol Infect. 2018 Sep;146(12):1556-64. DOI:10.1017/S0950268818001723; Bailey JR, Aggarwal A, Imperiale TF. Colorectal Cancer Screening: Stool DNA and Other Noninvasive Modalities. Gut Liver. 2016 Mar;10(2):204-11. DOI:10.5009/gnl15420; Berger BM, Schroy PC 3rd, Rosenberg JL, Lai-Goldman M, Eisenberg M, Brown T, Rochelle RB, Billings PR. Colorectal cancer screening using stool DNA analysis in clinical practice: early clinical experience with respect to patient acceptance and colonoscopic follow-up of abnormal tests. Clin Colorectal Cancer. 2006 Jan;5(5):338-43. DOI:10.3816/CCC.2006.n.003; Bénard F, Barkun AN, Martel M, von Renteln D. Systematic review of colorectal cancer screening guidelines for average-risk adults: Summarizing the current global recommendations. World J Gastroenterol. 2018 Jan;24(1):124-38. DOI:10.3748/wjg.v24.i1.124; Brenner H, Chen H. Fecal occult blood versus DNA testing: indirect comparison in a colorectal cancer screening population. Clin Epidemiol. 2017 Jul;9:377-84. DOI:10.2147/CLEP.S136565; Calderwood AH, Wasan SK, Heeren TC, Schroy PC 3rd. Patient and Provider Preferences for Colorectal Cancer Screening: How Does CT Colonography Compare to Other Modalities? Int J Canc Prev. 2011;4(4):307-38.; Cappell MS. From colonic polyps to colon cancer: pathophysiology, clinical presentation, and diagnosis. Clin Lab Med. 2005 Mar;25(1):135-77. DOI:10.1016/j.cll.2004.12.010; Danalioglu A. Can "DNA-based stool tests" replace colonoscopy in screening for colon cancer? Turk J Gastroenterol. 2014 Feb;25(1):122-3. DOI:10.5152/tjg.2014.0004; Dollinger M, Hiemer S, Behl S, Schinköthe T, Fleig W. Frühdetektion kolorektaler Karzinome: Multizentrische Phase II-Studie zur Validierung eines neuen DNA-basierten Stuhltest. Internist. 2016:S53.; Dollinger MM, Behl S, Fleig WE. Early Detection of Colorectal Cancer: a Multi-Center Pre-Clinical Case Cohort Study for Validation of a Combined DNA Stool Test. Clin Lab. 2018 Oct;64(10):1719-30. DOI:10.7754/Clin.Lab.2018.180521; European Colorectal Cancer Screening Guidelines Working Group, von Karsa L, Patnick J, Segnan N, Atkin W, Halloran S, et al. European guidelines for quality assurance in colorectal cancer screening and diagnosis: overview and introduction to the full supplement publication. Endoscopy. 2013;45(1):51-9. DOI:10.1055/s-0032-1325997; European Network for Health Technology Assessment. Joint Action on HTA 2012-2015. HTA Core Model for Rapid Relative Effectiveness. Version 4.2. 2015. Available from: https://www.eunethta.eu/wp-content/uploads/2018/06/HTACoreModel_ForRapidREAs4.2-3.pdf?x69613Test; European Network for Health Technology Assessment. Joint Action on HTA 2012-2015: HTA core model. Version 3.0. 2016. Available from: https://www.eunethta.eu/wp-content/uploads/2018/03/HTACoreModel3.0-1.pdf?x69613Test; Exact Sciences Corporation. Cologuard. FAQs. [last accessed 2022 Jul 27]. Available from: https://www.cologuard.com/faqTest; Exact Sciences Corporation. Cologuard. Insurance. [last accessed 2022 Jul 27]. Available from: https://www.cologuard.com/insuranceTest; Fleming M, Ravula S, Tatishchev SF, Wang HL. Colorectal carcinoma: Pathologic aspects. J Gastrointest Oncol. 2012 Sep;3(3):153-73. DOI:10.3978/j.issn.2078-6891.2012.030; Garcia M. Addressing overuse and overdiagnosis in colorectal cancer screening for average-risk individuals. Colorectal Cancer. 2015;4(1):27-35. DOI:10.2217/crc.15.4; Goetz G. Stool DNA testing for colorectal cancer (CRC) screening. Vienna: Austrian Institute for Health Technology Assessment GmbH; 2021. (AIHTA Policy Brief; 11). Available from: https://eprints.aihta.at/1335Test/; Imperiale TF, Ransohoff DF, Itzkowitz SH, Levin TR, Lavin P, Lidgard GP, Ahlquist DA, Berger BM. Multitarget stool DNA testing for colorectal-cancer screening. N Engl J Med. 2014 Apr;370(14):1287-97. DOI:10.1056/NEJMoa1311194; Jefferson T, Cerbo M, Vicari N, editors. Fecal Immunochemical Test (FIT) versus guaiac-based fecal occult blood test (FOBT) for colorectal cancer screening - Core HTA. Rome: Agenas - Agenzia nazionale per i servizi sanitari regionali; 2014. Available from: https://corehta.info/ViewCover.aspx?id=206Test; Kalager M, Wieszczy P, Lansdorp-Vogelaar I, Corley DA, Bretthauer M, Kaminski MF. Overdiagnosis in Colorectal Cancer Screening: Time to Acknowledge a Blind Spot. Gastroenterology. 2018 Sep;155(3):592-5. DOI:10.1053/j.gastro.2018.07.037; Lauby-Secretan B, Vilahur N, Bianchini F, Guha N, Straif K; International Agency for Research on Cancer Handbook Working Group. The IARC Perspective on Colorectal Cancer Screening. N Engl J Med. 2018 May;378(18):1734-40. DOI:10.1056/NEJMsr1714643; Leitlinienprogramm Onkologie. S3-Leitlinie Kolorektales Karzinom. Langversion 2.1. AWMF-Registernr. 021/007OL. Berlin: AWMF; 2019. Available from: https://www.awmf.org/uploads/tx_szleitlinien/021-007OLl_S3_Kolorektales-Karzinom-KRK_2019-01.pdfTest; Lin JS, Piper MA, Perdue LA, Rutter CM, Webber EM, O'Connor E, Smith N, Whitlock EP. Screening for Colorectal Cancer: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA. 2016;315(23):2576-94. DOI:10.1001/jama.2016.3332; Nikolaou S, Qiu S, Fiorentino F, Rasheed S, Tekkis P, Kontovounisios C. Systematic review of blood diagnostic markers in colorectal cancer. Tech Coloproctol. 2018 Jul;22(7):481-98. DOI:10.1007/s10151-018-1820-3; Phalguni A, Seaman H, Routh K, Halloran S, Simpson S. Tests detecting biomarkers for screening of colorectal cancer: What is on the horizon? GMS Health Technol Assess. 2015 Jun 10;11:Doc01. DOI:10.3205/hta000122; Ponti A, Anttila A, Ronco G, Senore C. Against Cancer. Cancer Screening in the European Union (2017). Report on the implementation of the Council Recommendation on cancer screening. Brussels: European Commission, Directorate-General for Health and Food Safety; 2017. Available from: https://ec.europa.eu/health/system/files/2017-05/2017_cancerscreening_2ndreportimplementation_en_0.pdfTest; Reitsma JB, Rutjes AW, Khan KS, Coomarasamy A, Bossuyt PM. A review of solutions for diagnostic accuracy studies with an imperfect or missing reference standard. J Clin Epidemiol. 2009 Aug;62(8):797-806. DOI:10.1016/j.jclinepi.2009.02.005; Rex DK, Boland CR, Dominitz JA, Giardiello FM, Johnson DA, Kaltenbach T, Levin TR, Lieberman D, Robertson DJ. Colorectal Cancer Screening: Recommendations for Physicians and Patients From the U.S. Multi-Society Task Force on Colorectal Cancer. Gastroenterology. 2017 Jul;153(1):307-23. DOI:10.1053/j.gastro.2017.05.013; Schreuders EH, Grobbee EJ, Spaander MC, Kuipers EJ. Advances in Fecal Tests for Colorectal Cancer Screening. Curr Treat Options Gastroenterol. 2016 Mar;14(1):152-62. DOI:10.1007/s11938-016-0076-0; Schroy PC 3rd, Heeren TC. Patient perceptions of stool-based DNA testing for colorectal cancer screening. Am J Prev Med. 2005 Feb;28(2):208-14. DOI:10.1016/j.amepre.2004.10.008; Schroy PC 3rd, Lal S, Glick JT, Robinson PA, Zamor P, Heeren TC. Patient preferences for colorectal cancer screening: how does stool DNA testing fare? Am J Manag Care. 2007 Jul;13(7):393-400.; Segnan N, Patnick J, von Karsa L, editors. European guidelines for quality assurance in colorectal cancer screening and diagnosis. First edition. Luxembourg: Publications Office of the European Union; 2010. DOI:10.2772/1458; Sofic A, Beslic S, Kocijancic I, Sehovic N. CT colonography in detection of colorectal carcinoma. Radiol Oncol. 2010;44(1):19-23. DOI:10.2478/v10019-010-0012-1; Stürzlinger H, Conrads-Frank A, Eisenmann A, Ivansits S, Jahn B, Janzic A, Jelenc M, Kostnapfel T, Mencej Bedrac S, Mühlberger N, Rochau U, Siebert U, Schnell-Inderst P, Sroczynski G. Stool DNA testing for early detection of colorectal cancer. Joint Assessment. Report No. OTJA10. Vienna: EUnetHTA; 2019.; The Regence Group. Analysis of Human DNA in Stool Samples as a Technique for Colorectal Cancer Screening. 2021 Dec 1. Available from: https://blue.regence.com/trgmedpol/geneticTesting/gt12.pdfTest; Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global Cancer Statistics, 2012. CA Cancer J Clin. 2015;65(2):87-108. DOI:10.3322/caac.21262; van Rijn JC, Reitsma JB, Stoker J, Bossuyt PM, van Deventer SJ, Dekker E. Polyp miss rate determined by tandem colonoscopy: a systematic review. Am J Gastroenterol. 2006 Feb;101(2):343-50. DOI:10.1111/j.1572-0241.2006.00390.x; Waldmann E, Regula J, Ferlitsch M. How can screening colonoscopy be optimized? Dig Dis. 2015;33(1):19-27. DOI:10.1159/000366033; Wang X, Kuang YY, Hu XT. Advances in epigenetic biomarker research in colorectal cancer. World J Gastroenterol. 2014 Apr;20(15):4276-87. DOI:10.3748/wjg.v20.i15.4276; Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, Leeflang MM, Sterne JA, Bossuyt PM; QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011 Oct;155(8):529-36. DOI:10.7326/0003-4819-155-8-201110180-00009; Wolf AMD, Fontham ETH, Church TR, Flowers CR, Guerra CE, LaMonte SJ, Etzioni R, McKenna MT, Oeffinger KC, Shih YT, Walter LC, Andrews KS, Brawley OW, Brooks D, Fedewa SA, Manassaram-Baptiste D, Siegel RL, Wender RC, Smith RA. Colorectal cancer screening for average-risk adults: 2018 guideline update from the American Cancer Society. CA Cancer J Clin. 2018 Jul;68(4):250-81. DOI:10.3322/caac.21457; http://dx.doi.org/10.3205/000320Test; http://nbn-resolving.de/urn:nbn:de:0183-0003205Test; http://www.egms.de/en/journals/gms/2023-21/000320.shtmlTest

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    دورية أكاديمية
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    مؤتمر
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    مؤتمر

    المصدر: 67. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 13. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF); 20220821-20220825; sine loco [digital]; DOCAbstr. 99 /20220819/

    مصطلحات موضوعية: decision analysis, mammography, screening, ddc: 610

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    دورية أكاديمية

    المصدر: GMS German Medical Science; VOL: 20; DOC12 /20221221/

    العلاقة: Alarid-Escudero F, Krijkamp EM, Pechlivanoglou P, Jalal H, Kao SZ, Yang A, Enns EA. A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling. Pharmacoeconomics. 2019 Nov;37(11):1329-39. DOI:10.1007/s40273-019-00837-x; Allen JC, Lewis JB, Tagliaferro AR. Cost-effectiveness of health risk reduction after lifestyle education in the small workplace. Prev Chronic Dis. 2012;9:E96. DOI:10.5888/pcd9.110169; Angrist JD, Pischke JS. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: University Press; 2008.; darthpack. Available from: https://darth-git.github.io/darthpackTest; Arnold KF, Harrison WJ, Heppenstall AJ, Gilthorpe MS. DAG-informed regression modelling, agent-based modelling and microsimulation modelling: a critical comparison of methods for causal inference. Int J Epidemiol. 2019 Feb;48(1):243-53. DOI:10.1093/ije/dyy260; Banack HR, Kaufman JS. The obesity paradox: understanding the effect of obesity on mortality among individuals with cardiovascular disease. Prev Med. 2014 May;62:96-102. DOI:10.1016/j.ypmed.2014.02.003; Baumann P, Schomaker M, Rossi E. Estimating the Effect of Central Bank Independence on Inflation Using Longitudinal Targeted Maximum Likelihood Estimation. Arxiv. 2020;arXiv:2003.02208. DOI:10.48550/arXiv.2003.02208; Beck JR, Pauker SG. The Markov process in medical prognosis. Med Decis Making. 1983;3(4):419-58. DOI:10.1177/0272989X8300300403; Benkeser D, van der Laan M. The Highly Adaptive Lasso Estimator. Proc Int Conf Data Sci Adv Anal. 2016;2016:689-96. DOI:10.1109/DSAA.2016.93; Braithwaite RS, Kozal MJ, Chang CC, Roberts MS, Fultz SL, Goetz MB, Gibert C, Rodriguez-Barradas M, Mole L, Justice AC. Adherence, virological and immunological outcomes for HIV-infected veterans starting combination antiretroviral therapies. AIDS. 2007 Jul;21(12):1579-89. DOI:10.1097/QAD.0b013e3281532b31; Briggs A, Claxton K, Sculpher M. Making decision models probabalistic. In: Briggs A, Claxton K, Sculpher M, editors. Decision Modeling for Health Economic Evaluation. New York: Oxford University Press; 2006. p. 77-120; Briggs AH, Weinstein MC, Fenwick EA, Karnon J, Sculpher MJ, Paltiel AD; ISPOR-SMDM Modeling Good Research Practices Task Force. Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-6. Med Decis Making. 2012 Sep-Oct;32(5):722-32. DOI:10.1177/0272989X12458348; Briggs AH, Weinstein MC, Fenwick EA, Karnon J, Sculpher MJ, Paltiel AD; ISPOR-SMDM Modeling Good Research Practices Task Force. Model parameter estimation and uncertainty: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6. Value Health. 2012 Sep-Oct;15(6):835-42. DOI:10.1016/j.jval.2012.04.014; Buxton MJ, Drummond MF, Van Hout BA, Prince RL, Sheldon TA, Szucs T, Vray M. Modelling in economic evaluation: an unavoidable fact of life. Health Econ. 1997 May-Jun;6(3):217-27. DOI:10.1002/(sici)1099-1050(199705)6:33.0.co;2-w; Cain LE, Saag MS, Petersen M, May MT, Ingle SM, Logan R, Robins JM, Abgrall S, Shepherd BE, Deeks SG, John Gill M, Touloumi G, Vourli G, Dabis F, Vandenhende MA, Reiss P, van Sighem A, Samji H, Hogg RS, Rybniker J, Sabin CA, Jose S, Del Amo J, Moreno S, Rodríguez B, Cozzi-Lepri A, Boswell SL, Stephan C, Pérez-Hoyos S, Jarrin I, Guest JL, D'Arminio Monforte A, Antinori A, Moore R, Campbell CN, Casabona J, Meyer L, Seng R, Phillips AN, Bucher HC, Egger M, Mugavero MJ, Haubrich R, Geng EH, Olson A, Eron JJ, Napravnik S, Kitahata MM, Van Rompaey SE, Teira R, Justice AC, Tate JP, Costagliola D, Sterne JA, Hernán MA; Antiretroviral Therapy Cohort Collaboration; Centers for AIDS Research Network of Integrated Clinical Systems; HIV-CAUSAL Collaboration. Using observational data to emulate a randomized trial of dynamic treatment-switching strategies: an application to antiretroviral therapy. Int J Epidemiol. 2016 12;45(6):2038-49. DOI:10.1093/ije/dyv295; Cain LE, Robins JM, Lanoy E, Logan R, Costagliola D, Hernán MA. When to start treatment? A systematic approach to the comparison of dynamic regimes using observational data. Int J Biostat. 2010;6(2):Article 18. DOI:10.2202/1557-4679.1212; Caro JJ, Briggs AH, Siebert U, Kuntz KM; ISPOR-SMDM Modeling Good Research Practices Task Force. Modeling good research practices - overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-1. Value Health. 2012 Sep-Oct;15(6):796-803. DOI:10.1016/j.jval.2012.06.012; Caro JJ. Pharmacoeconomic analyses using discrete event simulation. Pharmacoeconomics. 2005;23(4):323-32. DOI:10.2165/00019053-200523040-00003; Caro JJ, Briggs AH, Siebert U, Kuntz KM; ISPOR-SMDM Modeling Good Research Practices Task Force. Modeling good research practices--overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-1. Med Decis Making. 2012 Sep-Oct;32(5):667-77. DOI:10.1177/0272989X12454577; Cerdá M, Keyes KM. Systems Modeling to Advance the Promise of Data Science in Epidemiology. Am J Epidemiol. 2019 May;188(5):862-5. DOI:10.1093/aje/kwy262; Chancellor J, Kuehne F, Weinstein M, Mollon P, editors. Microsimulation or cohort modeling? A comparative case study in HIV infection. ISPOR 12th Annual International Meeting; 2007; Arlington, VA.; Clare PJ, Dobbins TA, Mattick RP. Causal models adjusting for time-varying confounding - a systematic review of the literature. Int J Epidemiol. 2019 Feb;48(1):254-65. DOI:10.1093/ije/dyy218; Clarke PM, Gray AM, Briggs A, Farmer AJ, Fenn P, Stevens RJ, Matthews DR, Stratton IM, Holman RR; UK Prospective Diabetes Study (UKDPS) Group. A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68). Diabetologia. 2004 Oct;47(10):1747-59. DOI:10.1007/s00125-004-1527-z; COCHTA. Guidelines for the economic evaluation of health technologies. 3rd ed. Ottawa: Canadian Agency for Drugs and Technologies in Health; 2006.; Cole SR, Frangakis CE. The consistency statement in causal inference: a definition or an assumption? Epidemiology. 2009 Jan;20(1):3-5. DOI:10.1097/EDE.0b013e31818ef366; Collett D. Modelling survival data in medical research. New York: Chapman & Hall; 1994.; Contreras-Hernandez I, Becker D, Chancellor J, Kühne F, Mould-Quevedo J, Vega G, Marfatia S. Cost-effectiveness of maraviroc for antiretroviral treatment-experienced HIV-infected individuals in Mexico. Value Health. 2010 Dec;13(8):903-14. DOI:10.1111/j.1524-4733.2010.00798.x; Corzillius M, Mühlberger N, Sroczynski G, Peeters J, Siebert U, Jäger H, Wasem J. Wertigkeit des Einsatzes der genotypischen und phänotypischen HIV-Resistenzbestimmung im Rahmen der Behandlung von HIV-infizierten Patienten. St. Augustin: Asgard; 2003. (Health Technology Assessment; 28).; Cox E, Martin BC, Van Staa T, Garbe E, Siebert U, Johnson ML. Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: the International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report - Part II. Value Health. 2009 Nov-Dec;12(8):1053-61. DOI:10.1111/j.1524-4733.2009.00601.x; Daniel RM, Cousens SN, De Stavola BL, Kenward MG, Sterne JA. Methods for dealing with time-dependent confounding. Stat Med. 2013 Apr;32(9):1584-618. DOI:10.1002/sim.5686; Daniel RM, De Stavola BL, Cousens SN. gformula: Estimating causal effects in the presence of time-varying confounding or mediation using the g-computation formula. Stata J. 2011;11(4):479-517.; Dawber TR, Meadors GF, Moore FE Jr. Epidemiological approaches to heart disease: the Framingham Study. Am J Public Health Nations Health. 1951 Mar;41(3):279-81. DOI:10.2105/ajph.41.3.279; Drummond MF, Jefferson TO. Guidelines for authors and peer reviewers of economic submissions to the BMJ. The BMJ Economic Evaluation Working Party. BMJ. 1996 Aug;313(7052):275-83. DOI:10.1136/bmj.313.7052.275; Drummond MF, Schwartz JS, Jönsson B, Luce BR, Neumann PJ, Siebert U, Sullivan SD. Key principles for the improved conduct of health technology assessments for resource allocation decisions. Int J Technol Assess Health Care. 2008;24(3):244-58; discussion 362-8. DOI:10.1017/S0266462308080343; Eddy DM, Hollingworth W, Caro JJ, Tsevat J, McDonald KM, Wong JB; ISPOR-SMDM Modeling Good Research Practices Task Force. Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7. Value Health. 2012 Sep-Oct;15(6):843-50. DOI:10.1016/j.jval.2012.04.012; Eddy DM, Hollingworth W, Caro JJ, Tsevat J, McDonald KM, Wong JB; ISPOR-SMDM Modeling Good Research Practices Task Force. Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7. Med Decis Making. 2012 Sep-Oct;32(5):733-43. DOI:10.1177/0272989X12454579; Ewald H, Ioannidis JPA, Ladanie A, Mc Cord K, Bucher HC, Hemkens LG. Nonrandomized studies using causal-modeling may give different answers than RCTs: a meta-epidemiological study. J Clin Epidemiol. 2020 Feb;118:29-41. DOI:10.1016/j.jclinepi.2019.10.012; Faries DE, Kadziola ZA. Analysis of longitudinal observational data using marginal structural models. In: Faries DE, Leon AC, Haro JM, Obenchain RL, editors. Analysis of observational health care data using SAS. Cary, NC: SAS Institute; 2010. p. 211-30.; Fewell Z, Hernan MA, Wolfe F, Tilling K, Choi H, Sterne JAC. Controlling for time-dependent confounding using marginal structural models. Stata J. 2004;4:402-20.; Fewell Z, Hernan MA, Wolfe F, Tilling K, Choi H, Sterne JAC. Controlling for time-dependent confounding using marginal structural models. Stata J. 2004;4(4):402-20.; Fleurence RL, Hollenbeak CS. Rates and probabilities in economic modelling: transformation, translation and appropriate application. Pharmacoeconomics. 2007;25(1):3-6. DOI:10.2165/00019053-200725010-00002; Galea S, Riddle M, Kaplan GA. Causal thinking and complex system approaches in epidemiology. Int J Epidemiol. 2010 Feb;39(1):97-106. DOI:10.1093/ije/dyp296; Ganiats TG, Neumann PJ, Russell LB, Sanders GD, Siegel JE. Cost effectiveness in health and medicine. Oxford: Oxford University Press; 2017.; Gehringer C, Rode H, Schomaker M. The Effect of Electrical Load Shedding on Pediatric Hospital Admissions in South Africa. Epidemiology. 2018 Nov;29(6):841-7. DOI:10.1097/EDE.0000000000000905; Gold M, Siegel J, Russell L, Weinstein M. Cost-effectiveness in Health and Medicine. New York: Oxford University Press; 1996.; Graf von der Schulenburg JM, Greiner W, Jost F, Klusen N, Kubin M, Leidl R, Mittendorf T, Rebscher H, Schoeffski O, Vauth C, Volmer T, Wahler S, Wasem J, Weber C; Hanover Consensus Group. German recommendations on health economic evaluation: third and updated version of the Hanover Consensus. Value Health. 2008 Jul-Aug;11(4):539-44. DOI:10.1111/j.1524-4733.2007.00301.x; Gray AM, Clarke PM, Wolstenholme JL, Wordsworth S. Modelling outcomes using patient-level data. In: Gray AM, Clarke PM, Wolstenholme JL, Wordsworth S, editors. Applied Methods of Cost-effectiveness Analysis in Health Care. New York: Oxford University Press; 2011. p. 61-80.; Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology. 2003 May;14(3):300-6.; Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999 Jan;10(1):37-48.; Gruber S, van der Laan MJ. tmle: An R Package for Targeted Maximum Likelihood Estimation. J Stat Softw. 2012;51(13):1-35. DOI:10.18637/jss.v051.i13; Hayes AJ, Leal J, Gray AM, Holman RR, Clarke PM. UKPDS outcomes model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia. 2013 Sep;56(9):1925-33. DOI:10.1007/s00125-013-2940-y; Hejazi NS, van der Laan MJ, Janes HE, Gilbert PB, Benkeser DC. Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials. Biometrics. 2021 Dec;77(4):1241-53. DOI:10.1111/biom.13375; Hernán MA, Taubman SL. Does obesity shorten life? The importance of well-defined interventions to answer causal questions. Int J Obes (Lond). 2008 Aug;32 Suppl 3:S8-14. DOI:10.1038/ijo.2008.82; Hernán MA, Hernández-Díaz S, Robins JM. Randomized trials analyzed as observational studies. Ann Intern Med. 2013 Oct;159(8):560-2. DOI:10.7326/0003-4819-159-8-201310150-00709; Hernán MA, Sauer BC, Hernández-Díaz S, Platt R, Shrier I. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol. 2016 Nov;79:70-5. DOI:10.1016/j.jclinepi.2016.04.014; Hernán MA, Robins JM. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC; 2020.; Hernán MA, Robins JM. Per-Protocol Analyses of Pragmatic Trials. N Engl J Med. 2017 Oct;377(14):1391-8. DOI:10.1056/NEJMsm1605385; Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004 Sep;15(5):615-25. DOI:10.1097/01.ede.0000135174.63482.43; Hernán MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology. 2000 Sep;11(5):561-70. DOI:10.1097/00001648-200009000-00012; Hernán MA, Hernández-Díaz S. Beyond the intention-to-treat in comparative effectiveness research. Clin Trials. 2012 Feb;9(1):48-55. DOI:10.1177/1740774511420743; Hunink MGM, Glasziou PG, Siegel JE, Weeks JC, Pliskin JS, Elstein AS, Weinstein MC. Valuing outcomes. In: Hunink MGM, Glasziou PG, Siegel JE, Weeks JC, Pliskin JS, Elstein AS, Weinstein MC, editors. Decision making in health and medicine - Integrating evidence and values. New York: Cambridge University Press; 2001. p. 88-127.; Husereau D, Drummond M, Petrou S, Carswell C, Moher D, Greenberg D, Augustovski F, Briggs AH, Mauskopf J, Loder E; CHEERS Task Force. Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. Cost Eff Resour Alloc. 2013 Mar;11(1):6. DOI:10.1186/1478-7547-11-6; IQWiG. Systematic guideline search and appraisal, as well as extraction of new and relevant recommendations for the DMP Breast cancer. 2008.; Jahn B, Pfeiffer KP, Theurl E, Blackhouse G, Bowen J, Hopkins R, et al. Capacities Constrains, Waiting Lists and Economic Evaluations: A Case Study on Stents using Discrete Event Simulation. SMDM Europe 2008; 1-4 June 2008; Engelberg, Switzerland.; Jahn B, Theurl E, Siebert U, Pfeiffer KP. Tutorial in medical decision modeling incorporating waiting lines and queues using discrete event simulation. Value Health. 2010 Jun-Jul;13(4):501-6. DOI:10.1111/j.1524-4733.2010.00707.x; Jahn B, Rochau U, Kurzthaler C, Paulden M, Kluibenschädl M, Arvandi M, Kühne F, Goehler A, Krahn MD, Siebert U. Lessons Learned from a Cross-Model Validation between a Discrete Event Simulation Model and a Cohort State-Transition Model for Personalized Breast Cancer Treatment. Med Decis Making. 2016 Apr;36(3):375-90. DOI:10.1177/0272989X15604158; Johnson ML, Crown W, Martin BC, Dormuth CR, Siebert U. Good research practices for comparative effectiveness research: analytic methods to improve causal inference from nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report - Part III. Value Health. 2009 Nov-Dec;12(8):1062-73. DOI:10.1111/j.1524-4733.2009.00602.x; Karnon J, Stahl J, Brennan A, Caro JJ, Mar J, Möller J. Modeling using discrete event simulation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-4. Med Decis Making. 2012 Sep-Oct;32(5):701-11. DOI:10.1177/0272989X12455462; Karnon J, Stahl J, Brennan A, Caro JJ, Mar J, Möller J; ISPOR-SMDM Modeling Good Research Practices Task Force. Modeling using discrete event simulation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-4. Value Health. 2012 Sep-Oct;15(6):821-7. DOI:10.1016/j.jval.2012.04.013; Kattan MW, Cowen ME. Encyclopedia of Medical Decision Making. Thousand Oaks: Sage Publications; 2010.; Keeney RL, Raiffa H. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. New York: Wiley; 1976.; Kleinbaum DG, Kupper LL, Muller KE, Nizam A. Applied regression analysis and other multivariable methods. Belmont: Duxbury Press; 1998.; Kuehne F, Siebert U, Faries DE. A target trial approach with dynamic treatment regimes and replicates analyses. In: Faries D, Zhang Z, Kadziola ZA, Siebert U, Kuehne F, Obenchain RL, Haro JM, editors. Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS. Cary, NC: SAS; 2020. p. 321-52.; Kuehne F, Chancellor J, Mollon P, Powderly WG, editors. Microsimulation or cohort modelling? A comparative case study in HIV based on treatment experienced patients. International Health Economic Association (iHEA) 6th World Congress on Health Economics; 2007; Copenhagen.; Kuehne F, Jahn B, Conrads-Frank A, Bundo M, Arvandi M, Endel F, Popper N, Endel G, Urach C, Gyimesi M, Murray EJ, Danaei G, Gaziano TA, Pandya A, Siebert U. Guidance for a causal comparative effectiveness analysis emulating a target trial based on big real world evidence: when to start statin treatment. J Comp Eff Res. 2019 Sep;8(12):1013-25. DOI:10.2217/cer-2018-0103; Kühne FC, Chancellor J, Mollon P, Myers DE, Louie M, Powderly WG. A microsimulation of the cost-effectiveness of maraviroc for antiretroviral treatment-experienced HIV-infected individuals. HIV Clin Trials. 2010 Mar-Apr;11(2):80-99. DOI:10.1310/hct1102-80; Latimer NR, White IR, Tilling K, Siebert U. Improved two-stage estimation to adjust for treatment switching in randomised trials: g-estimation to address time-dependent confounding. Stat Methods Med Res. 2020 Oct;29(10):2900-18. DOI:10.1177/0962280220912524; Latimer NR. Survival analysis for economic evaluations alongside clinical trials - extrapolation with patient-level data: inconsistencies, limitations, and a practical guide. Med Decis Making. 2013 Aug;33(6):743-54. DOI:10.1177/0272989X12472398; Latimer NR, White IR, Abrams KR, Siebert U. Causal inference for long-term survival in randomised trials with treatment switching: Should re-censoring be applied when estimating counterfactual survival times? Stat Methods Med Res. 2019 Aug;28(8):2475-93. DOI:10.1177/0962280218780856; Latimer NR, Abrams KR. NICE DSU Technical Support Document 16: Adjusting Survival Time Estimates in the Presence of Treatment Switching. London: National Institute for Health and Care Excellence (NICE); 2014.; Latimer NR, Henshall C, Siebert U, Bell H. Treatment switching: statistical and decision-making challenges and approaches. Int J Technol Assess Health Care. 2016 Jan;32(3):160-6. DOI:10.1017/S026646231600026X; Latimer NR, Abrams KR, Siebert U. Two-stage estimation to adjust for treatment switching in randomised trials: a simulation study investigating the use of inverse probability weighting instead of re-censoring. BMC Med Res Methodol. 2019 Mar;19(1):69. DOI:10.1186/s12874-019-0709-9; Legendre AM. Nouvelles méthodes pour la détermination des orbites des comètes. Paris: F. Didot; 1805.; Lendle SD, Petersen ML, Schwab J, van der Laan MJ. ltmle: An R Package Implementing Targeted Minimum Loss-Based Estimation for Longitudinal Data. J Stat Softw. 2017;81(1):1-21. DOI:10.18637/jss.v081.i01; Luque-Fernandez MA, Zoega H, Valdimarsdottir U, Williams MA. Deconstructing the smoking-preeclampsia paradox through a counterfactual framework. Eur J Epidemiol. 2016 Jun;31(6):613-23. DOI:10.1007/s10654-016-0139-5; Luque-Fernandez MA, Schomaker M, Rachet B, Schnitzer ME. Targeted maximum likelihood estimation for a binary treatment: A tutorial. Stat Med. 2018 Jul;37(16):2530-46. DOI:10.1002/sim.7628; Luque-Fernandez MA, Redondo-Sanchez D, Schomaker M. Effect Modification and Collapsibility in Evaluations of Public Health Interventions. Am J Public Health. 2019 Mar;109(3):e12-e3. DOI:10.2105/AJPH.2018.304916; Luque-Fernandez MA. ELTMLE: Stata module to provide Ensemble Learning Targeted Maximum Likelihood Estimation. Boston: Boston College Department of Economics; 2017. (Statistical Software Components; S458337). Available from: https://ideas.repec.org/c/boc/bocode/s458337.htmlTest; Luque-Fernandez MA, Schomaker M, Redondo-Sanchez D, Jose Sanchez Perez M, Vaidya A, Schnitzer ME. Educational Note: Paradoxical collider effect in the analysis of non-communicable disease epidemiological data: a reproducible illustration and web application. Int J Epidemiol. 2019 Apr;48(2):640-53. DOI:10.1093/ije/dyy275; Marshall DA, Burgos-Liz L, IJzerman MJ, Osgood ND, Padula WV, Higashi MK, Wong PK, Pasupathy KS, Crown W. Applying dynamic simulation modeling methods in health care delivery research-the SIMULATE checklist: report of the ISPOR simulation modeling emerging good practices task force. Value Health. 2015 Jan;18(1):5-16. DOI:10.1016/j.jval.2014.12.001; Marshall DA, Burgos-Liz L, IJzerman MJ, Crown W, Padula WV, Wong PK, Pasupathy KS, Higashi MK, Osgood ND; ISPOR Emerging Good Practices Task Force. Selecting a dynamic simulation modeling method for health care delivery research - part 2: report of the ISPOR Dynamic Simulation Modeling Emerging Good Practices Task Force. Value Health. 2015 Mar;18(2):147-60. DOI:10.1016/j.jval.2015.01.006; McGrath S, Lin V, Zhang Z, Petito LC, Logan RW, Hernán MA, Young JG. gfoRmula: An R Package for Estimating the Effects of Sustained Treatment Strategies via the Parametric g-formula. Patterns (NY). 2020 Jun;1(3):100008. DOI:10.1016/j.patter.2020.100008; Miksch F, Jahn B, Espinosa KJ, Chhatwal J, Siebert U, Popper N. Why should we apply ABM for decision analysis for infectious diseases? An example for dengue interventions. PLoS One. 2019;14(8):e0221564. DOI:10.1371/journal.pone.0221564; Miller DK, Homan SM. Determining transition probabilities: confusion and suggestions. Med Decis Making. 1994 Jan-Mar;14(1):52-8. DOI:10.1177/0272989X9401400107; Morden JP, Lambert PC, Latimer N, Abrams KR, Wailoo AJ. Assessing methods for dealing with treatment switching in randomised controlled trials: a simulation study. BMC Med Res Methodol. 2011 Jan;11:4. DOI:10.1186/1471-2288-11-4; Murray EJ, Robins JM, Seage GR, Freedberg KA, Hernán MA. A Comparison of Agent-Based Models and the Parametric G-Formula for Causal Inference. Am J Epidemiol. 2017 Jul;186(2):131-42. DOI:10.1093/aje/kwx091; Murray EJ, Robins JM, Seage GR 3rd, Lodi S, Hyle EP, Reddy KP, Freedberg KA, Hernán MA. Using Observational Data to Calibrate Simulation Models. Med Decis Making. 2018 Feb;38(2):212-24. DOI:10.1177/0272989X17738753; Murray EJ, Robins JM, Seage GR 3rd, Freedberg KA, Hernán MA. The Challenges of Parameterizing Direct Effects in Individual-Level Simulation Models. Med Decis Making. 2020 Jan;40(1):106-11. DOI:10.1177/0272989X19894940; National Institute for Health and Care Excellence (NICE). Guide to the Methods of Technology Appraisal 2013. London: NICE; 2013. (Process and Methods Guides; 9).; National Institute for Health and Care Excellence (NICE). Guide to the Processes of Technology Appraisal. London: NICE; 2014. (Process and Methods Guides; 19).; National Institute for Health and Care Excellence (NICE). Bevacizumab (first-line), sorafenib (first-and second-line), sunitinib (second-line) and temsirolimus (first-line) for the treatment of advanced and/or metastatic renal cell carcinoma. London: NICE; 2009. (Technology Appraisal Guidance; 178).; National Institute for Health and Care Excellence (NICE). Vemurafenib for treating locally advanced or metastatic BRAF V600 mutation-positive malignant melanoma. London: NICE; 2012. (Technology Appraisal Guidance; 269).; National Institute for Health and Care Excellence (NICE). Everolimus for the second-line treatment of advanced renal cell carcinoma. London: NICE; 2011. (Technology Appraisal Guidance; 2019).; National Institute for Health and Care Excellence (NICE). Sunitinib for the treatment of gastrointestinal stromal tumours. London: NICE; 2009. (Technology Appraisal Guidance; 179).; National Institute for Health and Clinical Excellence (NICE). Assessing cost impact; Methods guide. London: NICE; 2011.; Pearl J. Causality: models, reasoning, and inference. Cambridge: Cambridge University Press; 2009.; Pearl J. On the consistency rule in causal inference: axiom, definition, assumption, or theorem? Epidemiology. 2010 Nov;21(6):872-5. DOI:10.1097/EDE.0b013e3181f5d3fd; Petersen ML, Porter KE, Gruber S, Wang Y, van der Laan MJ. Diagnosing and responding to violations in the positivity assumption. Stat Methods Med Res. 2012 Feb;21(1):31-54. DOI:10.1177/0962280210386207; Philips Z, Bojke L, Sculpher M, Claxton K, Golder S. Good practice guidelines for decision-analytic modelling in health technology assessment: a review and consolidation of quality assessment. Pharmacoeconomics. 2006;24(4):355-71. DOI:10.2165/00019053-200624040-00006; Pitman R, Fisman D, Zaric GS, Postma M, Kretzschmar M, Edmunds J, Brisson M; ISPOR-SMDM Modeling Good Research Practices Task Force. Dynamic transmission modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--5. Value Health. 2012 Sep-Oct;15(6):828-34. DOI:10.1016/j.jval.2012.06.011; Pitman R, Fisman D, Zaric GS, Postma M, Kretzschmar M, Edmunds J, Brisson M; ISPOR-SMDM Modeling Good Research Practices Task Force. Dynamic transmission modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-5. Med Decis Making. 2012 Sep-Oct;32(5):712-21. DOI:10.1177/0272989X12454578; Rehkopf DH, Glymour MM, Osypuk TL. The Consistency Assumption for Causal Inference in Social Epidemiology: When a Rose is Not a Rose. Curr Epidemiol Rep. 2016 Mar;3(1):63-71. DOI:10.1007/s40471-016-0069-5; Roberts M, Russell LB, Paltiel AD, Chambers M, McEwan P, Krahn M; ISPOR-SMDM Modeling Good Research Practices Task Force. Conceptualizing a model: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-2. Value Health. 2012 Sep-Oct;15(6):804-11. DOI:10.1016/j.jval.2012.06.016; Roberts M, Russell LB, Paltiel AD, Chambers M, McEwan P, Krahn M; ISPOR-SMDM Modeling Good Research Practices Task Force. Conceptualizing a model: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-2. Med Decis Making. 2012 Sep-Oct;32(5):678-89. DOI:10.1177/0272989X12454941; Robins J. A new approach to causal inference in mortality studies with sustained exposure periods - Application to control of the healthy worker survivor effect. Mathematical Modelling. 1986;7(9-12):1393-512.; Robins JM, Hernán MA, Siebert U. Estimations of the effects of multiple interventions. In: Ezzati M, Lopez AD, Rodgers A, Murray CJL, editors. Comparative quantification of health risks: global and regional burden of disease attributable to selected major risk factors. Geneva: World Health Organization; 2004. p. 2191-230.; Robins JM, Blevins D, Ritter G, Wulfsohn M. G-estimation of the effect of prophylaxis therapy for Pneumocystis carinii pneumonia on the survival of AIDS patients. Epidemiology. 1992 Jul;3(4):319-36. DOI:10.1097/00001648-199207000-00007; Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2012.; Rothman KJ. Modern Epidemiology. Boston/Toronto: Little, Brown and Company; 1994.; Sackett DL, Rosenberg WM, Gray JA, Haynes RB, Richardson WS. Evidence based medicine: what it is and what it isn't. BMJ. 1996 Jan;312(7023):71-2. DOI:10.1136/bmj.312.7023.71; Sanders GD, Neumann PJ, Basu A, Brock DW, Feeny D, Krahn M, Kuntz KM, Meltzer DO, Owens DK, Prosser LA, Salomon JA, Sculpher MJ, Trikalinos TA, Russell LB, Siegel JE, Ganiats TG. Recommendations for Conduct, Methodological Practices, and Reporting of Cost-effectiveness Analyses: Second Panel on Cost-Effectiveness in Health and Medicine. JAMA. 2016 Sep;316(10):1093-103. DOI:10.1001/jama.2016.12195; Schomaker M, Luque-Fernandez MA, Leroy V, Davies MA. Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions. Stat Med. 2019 Oct;38(24):4888-911. DOI:10.1002/sim.8340; Schuler MS, Rose S. Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies. Am J Epidemiol. 2017 Jan;185(1):65-73. DOI:10.1093/aje/kww165; Schwarzer R, Siebert U. Methods, procedures, and contextual characteristics of health technology assessment and health policy decision making: comparison of health technology assessment agencies in Germany, United Kingdom, France, and Sweden. Int J Technol Assess Health Care. 2009 Jul;25(3):305-14. DOI:10.1017/S0266462309990092; Sculpher M, Fenwick E, Claxton K. Assessing quality in decision analytic cost-effectiveness models. A suggested framework and example of application. Pharmacoeconomics. 2000 May;17(5):461-77. DOI:10.2165/00019053-200017050-00005; Siebert U, Alagoz O, Bayoumi AM, Jahn B, Owens DK, Cohen DJ, Kuntz KM. State-transition modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3. Med Decis Making. 2012 Sep-Oct;32(5):690-700. DOI:10.1177/0272989X12455463; Siebert U. The role of decision-analytic models in the prevention, diagnosis and treatment of coronary heart disease. Z Kardiol. 2002;91 Suppl 3:144-51. DOI:10.1007/s00392-002-1326-9; Siebert U, Rochau U, Claxton K. When is enough evidence enough? Using systematic decision analysis and value-of-information analysis to determine the need for further evidence. Z Evid Fortbild Qual Gesundhwes. 2013;107(9-10):575-84.; Siebert U, Hernán MA, Robins JM. Monte Carlo simulation of the direct and indirect impact of risk factor interventions on coronary heart disease. An application of the g-formula. In: Proceedings of the 8th Biennial Conference of the European Society for Medical Decision Making; 2002 Jun 2-5; Taormina, Sicily, Italy. p. 51.; Siebert U. When should decision-analytic modeling be used in the economic evaluation of health care? Eur J Health Econom. 2003;4:143-50.; Siebert U, Jahn B, Mühlberger N, Fricke FU, Schöffski O. Entscheidungsanalyse und Modellierungen. In: Schöffski O, Graf von der Schulenburg JM, editors. Gesundheitsökonomische Evaluation. 4th ed. Berlin, Heidelberg, New York: Springer; 2012. p. 275-324.; Siebert U, Sroczynski G; German Hepatitis C Model (GEHMO) Group; HTA Expert Panel on Hepatitis C. Antiviral therapy for patients with chronic hepatitis C in Germany - Evaluation of effectiveness and cost-effectiveness of initial combination therapy with Interferon/Peginterferon plus Ribavirin. Köln: DIMDI; 2003.; Siebert U, Kurth T. Lebensqualität als Parameter von medizinischen Entscheidungsanalysen. In: Ravens-Sieberer U, Cieza A, von Steinbüchel N, Bullinger M, editors. Lebensqualität und Gesundheitsökonomie in der Medizin. Landsberg: Ecomed; 2000. p. 365-92.; Siebert U. Transparente Entscheidungen in Public Health mittels systematischer Entscheidungsanalyse. In: Schwartz FW, Walter U, Siegrist J, Kolip P, Leidl R, Dierks ML, Busse R, Schneider N, editors. Das Public Health Buch. 3rd ed. Munich: Urban & Fischer; 2012. p. 517-35.; Siebert U. Using decision-analytic modelling to transfer international evidence from health technology assessment to the context of the German health care system. GMS Health Technol Assess. 2005 Nov;1:Doc03.; Siebert U. Causal Inference and Heterogeneity Bias in Decision-Analytic Modeling of Cardiovascular Disease Interventions. Boston, MA: Harvard School of Public Health; 2005.; Siebert U, Alagoz O, Bayoumi AM, Jahn B, Owens DK, Cohen DJ, Kuntz KM; ISPOR-SMDM Modeling Good Research Practices Task Force. State-transition modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3. Value Health. 2012 Sep-Oct;15(6):812-20. DOI:10.1016/j.jval.2012.06.014; Siebert U, Kuehne F, Faries DE. Marginal structural models with inverse probability weighting. In: Faries D, Zhang Z, Kadziola ZA, Siebert U, Kuehne F, Obenchain RL, Haro JM, editors. Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS. Cary, NC: SAS; 2020. p. 303-20.; Sonnenberg FA, Beck JR. Markov models in medical decision making: a practical guide. Med Decis Making. 1993 Oct-Dec;13(4):322-38. DOI:10.1177/0272989X9301300409; Spirtes P, Glymour C, Scheines R. Causation, Prediction, and Search. 2nd ed. Cambridge: MIT Press; 2001.; Sroczynski G, Schnell-Inderst P, Mühlberger N, Lang K, Aidelsburger P, Wasem J, Mittendorf T, Engel J, Hillemanns P, Petry KU, Krämer A, Siebert U. Decision-analytic modeling to evaluate the long-term effectiveness and cost-effectiveness of HPV-DNA testing in primary cervical cancer screening in Germany. GMS Health Technol Assess. 2010 Apr;6:Doc05. DOI:10.3205/hta000083; Stahl JE. Modelling methods for pharmacoeconomics and health technology assessment: an overview and guide. Pharmacoeconomics. 2008;26(2):131-48. DOI:10.2165/00019053-200826020-00004; Stollenwerk B, Lhachimi SK, Briggs A, Fenwick E, Caro JJ, Siebert U, Danner M, Gerber-Grote A. Communicating the parameter uncertainty in the IQWiG efficiency frontier to decision-makers. Health Econ. 2015 Apr;24(4):481-90. DOI:10.1002/hec.3041; Taubman SL, Robins JM, Mittleman MA, Hernán MA. Intervening on risk factors for coronary heart disease: an application of the parametric g-formula. Int J Epidemiol. 2009 Dec;38(6):1599-611. DOI:10.1093/ije/dyp192; Tennant PW, Harrison WJ, Murray EJ, Arnold KF, Berrie L, Fox MP, Gadd SC, Keeble C, Ranker LR, Textor J, Tomova GD, Gilthorpe MS, Ellison GTH. Use of directed acyclic graphs (DAGs) in applied health research: review and recommendations. medRxiv. 2019:2019.12.20.19015511. DOI:10.1101/2019.12.20.19015511; Textor J, Hardt J, Knüppel S. DAGitty: a graphical tool for analyzing causal diagrams. Epidemiology. 2011 Sep;22(5):745. DOI:10.1097/EDE.0b013e318225c2be; Tran L, Petersen M, Schwab J, Van der Laan M. Robust variance estimation and inference for causal effect estimation. Arxiv. 2018:1810.03030. DOI:10.48550/arXiv:1810.03030; Trikalinos TA, Siebert U, Lau J. Decision-analytic modeling to evaluate benefits and harms of medical tests: uses and limitations. Med Decis Making. 2009 Sep-Oct;29(5):E22-9. DOI:10.1177/0272989X09345022; Ultsch B, Damm O, Beutels P, Bilcke J, Brüggenjürgen B, Gerber-Grote A, Greiner W, Hanquet G, Hutubessy R, Jit M, Knol M, von Kries R, Kuhlmann A, Levy-Bruhl D, Perleth M, Postma M, Salo H, Siebert U, Wasem J, Wichmann O. Methods for Health Economic Evaluation of Vaccines and Immunization Decision Frameworks: A Consensus Framework from a European Vaccine Economics Community. Pharmacoeconomics. 2016 Mar;34(3):227-44. DOI:10.1007/s40273-015-0335-2; Van der Laan M, Rose S. Targeted Learning in Data Science - Causal Inference for Complex Longitudinal Studies. Basel: Springer; 2018. DOI:10.1007/978-3-319-65304-4; Weinstein MC, Siegel JE, Gold MR, Kamlet MS, Russell LB. Recommendations of the Panel on Cost-effectiveness in Health and Medicine. JAMA. 1996 Oct;276(15):1253-8.; Weinstein MC, Fineberg HV. Utility Analysis: Clinical Decisions Involving Many Possible Outcomes. In: Weinstein MC, Fineberg HV, editors. Clinical Decision Analysis. Philadelphia: Saunders; 1980. p. 184-211.; Weinstein MC, O'Brien B, Hornberger J, Jackson J, Johannesson M, McCabe C, Luce BR; ISPOR Task Force on Good Research Practices - Modeling Studies. Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices - Modeling Studies. Value Health. 2003 Jan-Feb;6(1):9-17. DOI:10.1046/j.1524-4733.2003.00234.x; Weinstein MC, Stason WB. Foundations of cost-effectiveness analysis for health and medical practices. N Engl J Med. 1977 Mar;296(13):716-21. DOI:10.1056/NEJM197703312961304; Westreich D, Cole SR, Young JG, Palella F, Tien PC, Kingsley L, Gange SJ, Hernán MA. The parametric g-formula to estimate the effect of highly active antiretroviral therapy on incident AIDS or death. Stat Med. 2012 Aug;31(18):2000-9. DOI:10.1002/sim.5316; Whitcomb BW, Schisterman EF, Perkins NJ, Platt RW. Quantification of collider-stratification bias and the birthweight paradox. Paediatr Perinat Epidemiol. 2009 Sep;23(5):394-402. DOI:10.1111/j.1365-3016.2009.01053.x; Winslow CE. The untilled fields of public health. Science. 1920 Jan;51(1306):23-33. DOI:10.1126/science.51.1306.23; http://dx.doi.org/10.3205/000314Test; http://nbn-resolving.de/urn:nbn:de:0183-0003147Test; http://www.egms.de/en/journals/gms/2022-20/000314.shtmlTest

  9. 9
    دورية أكاديمية

    المصدر: GMS German Medical Science; VOL: 20; DOC11 /20221221/

    العلاقة: Council Directive 93/42/EEC of 14 June 1993 concerning medical devices.; Council Directive 90/385/EEC of 20 June 1990 on the approximation of the laws of the Member States relating to active implantable medical devices.; Regulation (EU) 2017/746 of the European Parliament and of the Council of 5 April 2017 on in vitro diagnostic medical devices and repealing Directive 98/79/EC and Commission Decision 2010/227/EU.; Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices, amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and repealing Council Directives 90/385/EEC and 93/42/EEC.; Directive 98/79/EC of the European Parliament and of the Council of 27 October 1998 on in vitro diagnostic medical devices.; Bartelmes M, Neumann U, Lühmann D, Schönermark MP, Hagen A. Methods for assessment of innovative medical technologies during early stages of development. GMS Health Technol Assess. 2009 Nov 5;5:Doc15. DOI:10.3205/hta000077; Bojke L, Claxton K, Bravo-Vergel Y, Sculpher M, Palmer S, Abrams K. Eliciting distributions to populate decision analytic models. Value Health. 2010 Aug;13(5):557-64. DOI:10.1111/j.1524-4733.2010.00709.x; Briggs AH, Weinstein MC, Fenwick EA, Karnon J, Sculpher MJ, Paltiel AD; ISPOR-SMDM Modeling Good Research Practices Task Force. Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-6. Med Decis Making. 2012 Sep-Oct;32(5):722-32. DOI:10.1177/0272989X12458348; Campos NG, Tsu V, Jeronimo J, Mvundura M, Kim JJ. Estimating the value of point-of-care HPV testing in three low- and middle-income countries: a modeling study. BMC Cancer. 2017 Nov;17(1):791. DOI:10.1186/s12885-017-3786-3; Caro JJ, Briggs AH, Siebert U, Kuntz KM; ISPOR-SMDM Modeling Good Research Practices Task Force. Modeling good research practices - overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-1. Med Decis Making. 2012 Sep-Oct;32(5):667-77. DOI:10.1177/0272989X12454577; Ciani O, Wilcher B, van Giessen A, Taylor RS. Linking the Regulatory and Reimbursement Processes for Medical Devices: The Need for Integrated Assessments. Health Econ. 2017 Feb;26 Suppl 1:13-29. DOI:10.1002/hec.3479; Critselis E, Vlahou A, Stel VS, Morton RL. Cost-effectiveness of screening type 2 diabetes patients for chronic kidney disease progression with the CKD273 urinary peptide classifier as compared to urinary albumin excretion. Nephrol Dial Transplant. 2018 Mar;33(3):441-9. DOI:10.1093/ndt/gfx068; Di Paolo A, Sarkozy F, Ryll B, Siebert U. Personalized medicine in Europe: not yet personal enough? BMC Health Serv Res. 2017 Apr;17(1):289. DOI:10.1186/s12913-017-2205-4; Doble B, John T, Thomas D, Fellowes A, Fox S, Lorgelly P. Cost-effectiveness of precision medicine in the fourth-line treatment of metastatic lung adenocarcinoma: An early decision analytic model of multiplex targeted sequencing. Lung Cancer. 2017 May;107:22-35. DOI:10.1016/j.lungcan.2016.05.024; Drummond M, Griffin A, Tarricone R. Economic evaluation for devices and drugs--same or different? Value Health. 2009 Jun;12(4):402-4. DOI:10.1111/j.1524-4733.2008.00476_1.x; Drummond MF, Schwartz JS, Jönsson B, Luce BR, Neumann PJ, Siebert U, Sullivan SD. Key principles for the improved conduct of health technology assessments for resource allocation decisions. Int J Technol Assess Health Care. 2008;24(3):244-58; discussion 362-8. DOI:10.1017/S0266462308080343; Faulkner E, Annemans L, Garrison L, Helfand M, Holtorf AP, Hornberger J, Hughes D, Li T, Malone D, Payne K, Siebert U, Towse A, Veenstra D, Watkins J; Personalized Medicine Development and Reimbursement Working Group. Challenges in the development and reimbursement of personalized medicine-payer and manufacturer perspectives and implications for health economics and outcomes research: a report of the ISPOR personalized medicine special interest group. Value Health. 2012 Dec;15(8):1162-71. DOI:10.1016/j.jval.2012.05.006; Faulkner E, Holtorf AP, Walton S, Liu CY, Lin H, Biltaj E, Brixner D, Barr C, Oberg J, Shandhu G, Siebert U, Snyder SR, Tiwana S, Watkins J, IJzerman MJ, Payne K. Being Precise About Precision Medicine: What Should Value Frameworks Incorporate to Address Precision Medicine? A Report of the Personalized Precision Medicine Special Interest Group. Value Health. 2020 May;23(5):529-39. DOI:10.1016/j.jval.2019.11.010; Garrison LP Jr, Neumann PJ, Erickson P, Marshall D, Mullins CD. Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task Force report. Value Health. 2007 Sep-Oct;10(5):326-35. DOI:10.1111/j.1524-4733.2007.00186.x; Haakma W, Steuten LM, Bojke L, IJzerman MJ. Belief elicitation to populate health economic models of medical diagnostic devices in development. Appl Health Econ Health Policy. 2014 Jun;12(3):327-34. DOI:10.1007/s40258-014-0092-y; Hartz S, John J. Contribution of economic evaluation to decision making in early phases of product development: a methodological and empirical review. Int J Technol Assess Health Care. 2008;24(4):465-72. DOI:10.1017/S0266462308080616; Hernán MA, Sauer BC, Hernández-Díaz S, Platt R, Shrier I. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol. 2016 Nov;79:70-5. DOI:10.1016/j.jclinepi.2016.04.014; Hernán MA, Robins JM. Estimating causal effects from epidemiological data. J Epidemiol Community Health. 2006 Jul;60(7):578-86. DOI:10.1136/jech.2004.029496; Hunink MGM, Weinstein MC, Wittenberg E, Drummond MF, Pliskin JS, Wong JB, Glasziou PP. Decision making in health and medicine. 2nd ed. Cambridge: Cambridge University Press; 2014.; Iglesias CP, Thompson A, Rogowski WH, Payne K. Reporting Guidelines for the Use of Expert Judgement in Model-Based Economic Evaluations. Pharmacoeconomics. 2016 Nov;34(11):1161-72. DOI:10.1007/s40273-016-0425-9; IJzerman MJ, Steuten LM. Early assessment of medical technologies to inform product development and market access: a review of methods and applications. Appl Health Econ Health Policy. 2011 Sep;9(5):331-47. DOI:10.2165/11593380-000000000-00000; Khoudigian-Sinani S, Blackhouse G, Levine M, Thabane L, O'Reilly D. The premarket assessment of the cost-effectiveness of a predictive technology "Straticyte(TM)" for the early detection of oral cancer: a decision analytic model. Health Econ Rev. 2017 Oct;7(1):35. DOI:10.1186/s13561-017-0170-6; Kluytmans A, Deinum J, Jenniskens K, van Herwaarden AE, Gloerich J, van Gool AJ, van der Wilt GJ, Grutters JPC. Clinical biomarker innovation: when is it worthwhile? Clin Chem Lab Med. 2019 Oct;57(11):1712-20. DOI:10.1515/cclm-2019-0098; Kristensen FB, Husereau D, Huic M, Drummond M, Berger ML, Bond K, Augustovski F, Booth A, Bridges JFP, Grimshaw J, IJzerman MJ, Jonsson E, Ollendorf DA, Rüther A, Siebert U, Sharma J, Wailoo A. Identifying the Need for Good Practices in Health Technology Assessment: Summary of the ISPOR HTA Council Working Group Report on Good Practices in HTA. Value Health. 2019 Jan;22(1):13-20. DOI:10.1016/j.jval.2018.08.010; Kuehne F, Jahn B, Conrads-Frank A, Bundo M, Arvandi M, Endel F, Popper N, Endel G, Urach C, Gyimesi M, Murray EJ, Danaei G, Gaziano TA, Pandya A, Siebert U. Guidance for a causal comparative effectiveness analysis emulating a target trial based on big real world evidence: when to start statin treatment. J Comp Eff Res. 2019 Sep;8(12):1013-25. DOI:10.2217/cer-2018-0103; Lansdorp-Vogelaar I, Goede SL, Bosch LJW, Melotte V, Carvalho B, van Engeland M, Meijer GA, de Koning HJ, van Ballegooijen M. Cost-effectiveness of High-performance Biomarker Tests vs Fecal Immunochemical Test for Noninvasive Colorectal Cancer Screening. Clin Gastroenterol Hepatol. 2018 Apr;16(4):504-12.e11. DOI:10.1016/j.cgh.2017.07.011; Lotan Y, Woldu SL, Sanli O, Black P, Milowsky MI. Modelling cost-effectiveness of a biomarker-based approach to neoadjuvant chemotherapy for muscle-invasive bladder cancer. BJU Int. 2018 Sep;122(3):434-40. DOI:10.1111/bju.14220; Mitchell D, Guertin JR, Dubois A, Dubé MP, Tardif JC, Iliza AC, Fanton-Aita F, Matteau A, LeLorier J. A Discrete Event Simulation Model to Assess the Economic Value of a Hypothetical Pharmacogenomics Test for Statin-Induced Myopathy in Patients Initiating a Statin in Secondary Cardiovascular Prevention. Mol Diagn Ther. 2018 Apr;22(2):241-54. DOI:10.1007/s40291-018-0323-2; Namin AT, Jalali MS, Vahdat V, Bedair HS, O'Connor MI, Kamarthi S, Isaacs JA. Adoption of New Medical Technologies: The Case of Customized Individually Made Knee Implants. Value Health. 2019 Apr;22(4):423-30. DOI:10.1016/j.jval.2019.01.008; Pietzsch JB, Paté-Cornell ME. Early technology assessment of new medical devices. Int J Technol Assess Health Care. 2008 Winter;24(1):36-44. DOI:10.1017/S0266462307080051; Pitman R, Fisman D, Zaric GS, Postma M, Kretzschmar M, Edmunds J, Brisson M; ISPOR-SMDM Modeling Good Research Practices Task Force. Dynamic transmission modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-5. Med Decis Making. 2012 Sep-Oct;32(5):712-21. DOI:10.1177/0272989X12454578; Retèl VP, Bueno-de-Mesquita JM, Hummel MJ, van de Vijver MJ, Douma KF, Karsenberg K, van Dam FS, van Krimpen C, Bellot FE, Roumen RM, Linn SC, van Harten WH. Constructive Technology Assessment (CTA) as a tool in coverage with evidence development: the case of the 70-gene prognosis signature for breast cancer diagnostics. Int J Technol Assess Health Care. 2009 Jan;25(1):73-83. DOI:10.1017/S0266462309090102; Roberts M, Russell LB, Paltiel AD, Chambers M, McEwan P, Krahn M; ISPOR-SMDM Modeling Good Research Practices Task Force. Conceptualizing a model: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-2. Med Decis Making. 2012 Sep-Oct;32(5):678-89. DOI:10.1177/0272989X12454941; Robins JM, Hernán MA, Siebert U. Estimations of the Effects of Multiple Interventions. In: Ezzati M, Lopez AD, Rodgers A, Murray CJL, editors. Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors. Geneva: World Health Organization; 2004. p. 2191-230.; Rothery C, Claxton K, Palmer S, Epstein D, Tarricone R, Sculpher M. Characterising Uncertainty in the Assessment of Medical Devices and Determining Future Research Needs. Health Econ. 2017 Feb;26 Suppl 1:109-23. DOI:10.1002/hec.3467; Schnell-Inderst P, Hunger T, Conrads-Frank A, Arvandi M, Siebert U. Recommendations for primary studies evaluating therapeutic medical devices were identified and systematically reported through reviewing existing guidance. J Clin Epidemiol. 2018 Feb;94:46-58. DOI:10.1016/j.jclinepi.2017.10.007; Siebert U. When should decision-analytic modeling be used in the economic evaluation of health care? Eur J Health Econom. 2003;4:143-50. DOI:10.1007/s10198-003-0205-2; Tarricone R, Boscolo PR, Armeni P. What type of clinical evidence is needed to assess medical devices? Eur Respir Rev. 2016 Sep;25(141):259-65. DOI:10.1183/16000617.0016-2016; Trikalinos TA, Siebert U, Lau J. Decision-Analytic Modeling to Evaluate Benefits and Harms of Medical Tests - Uses and Limitations. In: Agency for Healthcare Research and Quality, editor. Medical Tests - White Paper Series. Rockville: AHRQ; 2009.; Weaver DT, Raphel TJ, Melamed A, Rauh-Hain JA, Schorge JO, Knudsen AB, Pandharipande PV. Modeling treatment outcomes for patients with advanced ovarian cancer: Projected benefits of a test to optimize treatment selection. Gynecol Oncol. 2018 May;149(2):256-62. DOI:10.1016/j.ygyno.2018.02.007; Weinstein MC, Fineberg HV, Elstein AS, Frazier HS, Neuhauser D, Neutra RR. Cinical Decision Analysis. 1st ed. Philadelphia: W.B. Saunders; 1980.; Wenker S, van Lieshout C, Frederix G, van der Heijden J, Loh P, Chamuleau SAJ, van Slochteren F. MRI-guided pulmonary vein isolation for atrial fibrillation: what is good enough? An early health technology assessment. Open Heart. 2019;6(2):e001014. DOI:10.1136/openhrt-2019-001014; Yu TM, Morrison C, Gold EJ, Tradonsky A, Arnold RJG. Budget Impact of Next-Generation Sequencing for Molecular Assessment of Advanced Non-Small Cell Lung Cancer. Value Health. 2018 Nov;21(11):1278-85. DOI:10.1016/j.jval.2018.04.1372; http://dx.doi.org/10.3205/000313Test; http://nbn-resolving.de/urn:nbn:de:0183-0003138Test; http://www.egms.de/en/journals/gms/2022-20/000313.shtmlTest

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