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Projecting effectiveness after ending a randomized controlled trial: a two-state Markov microsimulation model

Part of: Special Collection - Methods

Published online by Cambridge University Press:  03 August 2020

Fei Yuan Open the ORCID record for Fei Yuan [Opens in a new window] ,
Shrikant I. Bangdiwala Open the ORCID record for Shrikant I. Bangdiwala [Opens in a new window] ,
Wesley Tong Open the ORCID record for Wesley Tong [Opens in a new window]  and
Andre Lamy Open the ORCID record for Andre Lamy [Opens in a new window]
Fei Yuan*
Affiliation:
Population Health Research Institute, DBCVSRI, 20 Copeland Avenue, Hamilton, ONL8L 2X2, Canada
Shrikant I. Bangdiwala
Affiliation:
Population Health Research Institute, DBCVSRI, 20 Copeland Avenue, Hamilton, ONL8L 2X2, Canada Department of Health Research Methods, Evidence and Impact, McMaster University, 1280 Main St W, Hamilton, ON L8S 4L8, Canada
Wesley Tong
Affiliation:
Population Health Research Institute, DBCVSRI, 20 Copeland Avenue, Hamilton, ONL8L 2X2, Canada
Andre Lamy
Affiliation:
Population Health Research Institute, DBCVSRI, 20 Copeland Avenue, Hamilton, ONL8L 2X2, Canada Department of Health Research Methods, Evidence and Impact, McMaster University, 1280 Main St W, Hamilton, ON L8S 4L8, Canada Hamilton Health Sciences, 237 Barton St. East, Hamilton, ONL8L 2X2, Canada
*
Author for correspondence: Fei Yuan, E-mail: yuanf@phri.ca; yuanfeifei@gmail.com
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Abstract

Objective

To investigate the behavior of restricted mean survival time (RMST) and designs of a two-state Markov microsimulation model through a 2 × 4 × 2 full factorial experiment.

Method

By projecting patient-wise 15-year-post-trial survival, we estimated life-year-gained between an intervention and a control group using data from the Cardiovascular Outcomes for People Using Anticoagulation Strategies Study (COMPASS). Projections considered either in-trial events or post-trial medications. They were compared based on three factors: (i) choice of probability of death, (ii) lengths of cycle, and (iii) usage of half-a-cycle age correction. Three-way analysis of variance and post-hoc Tukey's Honest Significant Difference test compared means among factors.

Results

When both in-trial events and post-trial study medications were considered, monthly, quarterly, or semiannually were not different from one other in projected life-year-gained. However, the annual one was different from the others: mean and 95 percent confidence interval 252.2 (190.5–313.9) days monthly, 251.8 (192.0–311.6) quarterly, 249.1 (189.7–308.5) semiannually, and 240.8 (178.5–303.1) annually. The other two factors also impacted life-year-gained: background probability (269.1 [260.3–277.9] days projected with REACH-based-probabilities, 227.7 [212.6–242.8] with a USA life table); half-a-cycle age correction (245.5 [199.0–292] with correction and 251.4 [209.1–293.7] without correction). When not considering post-trial medications, only the choice of probability of death appeared to impact life-year-gained.

Conclusion

For a large trial or cohort, to optimally project life-year-gained, one should consider using (i) annual projections, (ii) life table probabilities, (iii) in-trial events, and (iv) post-trial medication use.

Keywords

StatisticsHealth economics/economic evaluationClinical epidemiologyModelingEpidemiology
Type
Method
Information
International Journal of Technology Assessment in Health Care , Volume 36 , Issue 4 , August 2020 , pp. 317 - 324
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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Footnotes

Abbreviations: ASA, acetyl-salicylic-acid or aspirin; COMPASS, the Cardiovascular Outcomes for People Using Anticoagulation Strategies Study; CAD, coronary artery diseases; CHD, coronary heart diseases; ICER, incremental cost-effectiveness ratio; a modification of the International Society on Thrombosis and Homeostasis (ISTH) criteria for major bleeding (fatal bleeding excluded); MI, myocardial infarction; PAD, peripheral artery diseases; RMST, restricted mean survival time. This measurement is used to measure survivals of two intervention groups (life expectancy) and the incremental survival (life-year-gained) between intervention groups; REACH registry, Reduction of Atherothrombosis for Continued Health Registry; Riva, rivaroxaban.

References

Wong, ND. Epidemiological studies of CHD and the evolution of preventive cardiology. Nat Rev Cardiol. 2014;11:276–89.CrossRefGoogle ScholarPubMed
Bosch, J, Eikelboom, JW, Connolly, SJ, Bruns, NC, Lanius, V, Yuan, F et al. Rationale, design and baseline characteristics of participants in the Cardiovascular Outcomes for People Using Anticoagulation Strategies (COMPASS) trial. Can J Cardiol. 2017;33:1027–35.CrossRefGoogle ScholarPubMed
Eikelboom, JW, Connolly, SJ, Bosch, J, Dagenais, GR, Hart, RG, Shestakovska, O et al. Rivaroxaban with or without aspirin in stable cardiovascular disease. N Engl J Med. 2017;377:1319–30.CrossRefGoogle ScholarPubMed
Magnuson, EA, Li, H, Wang, K, Vilain, K, Shafiq, A, Bonaca, MP et al. Cost-effectiveness of long-term ticagrelor in patients with prior myocardial infarction – results from the PEGASUS-TIMI 54 trial. J Am Coll Cardiol. 2017;70:527–38.CrossRefGoogle ScholarPubMed
Bress, A, Bellows, B, King, JB, Hess, R, Beddhu, S, Zhang, Z et al. Cost-effectiveness of intensive versus standard blood-pressure control. N Engl J Med. 2017;377:745–55.CrossRefGoogle ScholarPubMed
Kypridemos, C, Collins, B, McHale, P, Bromley, H, Parvulescu, P, Capewell, S et al. Future cost-effectiveness and equity of the NHS Health Check cardiovascular disease prevention programme: Microsimulation modelling using data from Liverpool, UK. PLoS Med. 2018;15:e1002573.CrossRefGoogle ScholarPubMed
Zafari, Z, Bryan, S, Sin, DD, Conte, T, Khakban, R, Sadatsafavi, M. A systematic review of health economics simulation models of chronic obstructive pulmonary disease. Value Health. 2017;20:152–62.CrossRefGoogle ScholarPubMed
Gerdtham, U, Zethraeus, N. Predicting survival in cost-effectiveness analyses based on clinical trials. Int J Technol Assess Health Care. 2003;19:507–12.CrossRefGoogle ScholarPubMed
Simpson, KN, Strassburger, A, Jones, WJ, Dietz, B, Rajagopalan, R. Comparison of Markov model and discrete-event simulation techniques for HIV. PharmacoEconomics. 2009;27:159–65.CrossRefGoogle ScholarPubMed
Reed, SD. Statistical considerations in economic evaluations: A guide for cardiologists. Eur Heart J. 2014;25:1652–6.CrossRefGoogle Scholar
Soares, M, Castro, L. Continuous time simulation and discretized models for cost-effectiveness analysis. PharmacoEconomics. 2012;30:1101–17.CrossRefGoogle ScholarPubMed
Carvalho, TM, Heijnsdijk, EAM, Coffeng, L, Koning, HJ. Evaluating parameter uncertainty in a simulation model of cancer using emulators. Med Decis Making. 2019;39:405–13.CrossRefGoogle Scholar
Chhatwal, J, Jayasuriya, S, Elbasha, EH. Changing cycle lengths in state-transition models: Challenges and solutions. Med Decis Making. 2016;36:952–64.CrossRefGoogle ScholarPubMed
Saidi, O, Flaherty, M, Zoghlami, N, Malouche, D, Capewell, S, Critchley, JA et al. Comparing strategies to prevent stroke and ischemic heart disease in the Tunisian population: Markov modeling approach using a comprehensive sensitivity analysis algorithm. Comput Math Methods Med. 2019;2019:2123079.CrossRefGoogle ScholarPubMed
Rutter, C, Miglioretti, D, Savarino, J. Bayesian calibration of microsimulation models. J Am Stat Assoc. 2009;104:1338–50.CrossRefGoogle ScholarPubMed
Choi, SE, Brandeau, ML, Basu, S. Dynamic treatment selection and modification for personalised blood pressure therapy using a Markov decision process model: A cost-effectiveness analysis. BMJ Open. 2017;7:e018374.CrossRefGoogle ScholarPubMed
Sonnenberg, FA, Beck, JR. Markov models in medical decision making: A practical guide. Med Decis Making. 1993;13:322–38.CrossRefGoogle ScholarPubMed
Barenfiregt, JJ. The half-cycle correction: Banish rather than explain it. Med Decis Making. 2009;29:500–2.CrossRefGoogle Scholar
Royston, P, Parmar, MKB. The use of restricted mean survival time to estimate the treatment effect in randomized clinical trials when the proportional hazards assumption was in doubt. Stat Med. 2011;30:2409–21.CrossRefGoogle ScholarPubMed
Royston, P, Parmar, MKB. 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 Med Res Methodol. 2013;13:152.CrossRefGoogle ScholarPubMed
Uno, H, Tian, L, Horiguchi, M, Cronin, A, Battioui, C, Bell, J. Package ‘survRM2’. 2017. Available from: https://cran.r-project.org/web/packages/survRM2/survRM2.pdfGoogle Scholar
Arias, E, Heron, M, Xu, J. United States life tables, 2013. Natl Vital Stat Rep. 2017;66:1–64.Google ScholarPubMed
Wilson, PW, D'Agostino, R Sr, Bhatt, DL, Eagle, K, Pencina, MJ, Smith, SC et al. An international model to predict recurrent cardiovascular disease. Am J Med. 2012;125:695–703.CrossRefGoogle ScholarPubMed
Ohman, EM, Bhatt, DL, Steg, PG, Goto, S, Hirsch, AT, Liau, CS et al. The REduction of Atherothrombosis for Continued Health (REACH) registry: An international, prospective, observational investigation in subjects at risk for atherothrombotic events-study design. Am Heart J. 2006;151:786.e1–786.e10.CrossRefGoogle ScholarPubMed
Damen, JAAG, Hooft, L, Schuit, E, Debray, TPA, Collins, GS, Tzoulaki, I et al. Prediction models for cardiovascular disease risk in the general population: Systematic review. Br Med J. 2016;353:i2416.CrossRefGoogle ScholarPubMed
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