Bayesian Statistics for Medical Devices: Progress Since 2010.
Bayesian adaptive designs
Benefit-risk decision analysis
Diagnostic test accuracy
Hierarchical Bayesian modeling
Prior Information
Real-world evidence
Journal
Therapeutic innovation & regulatory science
ISSN: 2168-4804
Titre abrégé: Ther Innov Regul Sci
Pays: Switzerland
ID NLM: 101597411
Informations de publication
Date de publication:
05 2023
05 2023
Historique:
received:
08
09
2022
accepted:
24
12
2022
medline:
28
4
2023
pubmed:
4
3
2023
entrez:
3
3
2023
Statut:
ppublish
Résumé
The use of Bayesian statistics to support regulatory evaluation of medical devices began in the late 1990s. We review the literature, focusing on recent developments of Bayesian methods, including hierarchical modeling of studies and subgroups, borrowing strength from prior data, effective sample size, Bayesian adaptive designs, pediatric extrapolation, benefit-risk decision analysis, use of real-world evidence, and diagnostic device evaluation. We illustrate how these developments were utilized in recent medical device evaluations. In Supplementary Material, we provide a list of medical devices for which Bayesian statistics were used to support approval by the US Food and Drug Administration (FDA), including those since 2010, the year the FDA published their guidance on Bayesian statistics for medical devices. We conclude with a discussion of current and future challenges and opportunities for Bayesian statistics, including artificial intelligence/machine learning (AI/ML) Bayesian modeling, uncertainty quantification, Bayesian approaches using propensity scores, and computational challenges for high dimensional data and models.
Identifiants
pubmed: 36869194
doi: 10.1007/s43441-022-00495-w
pii: 10.1007/s43441-022-00495-w
pmc: PMC9984131
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
453-463Informations de copyright
© 2023. The Author(s), under exclusive licence to The Drug Information Association, Inc.
Références
U.S. Food and Drug Administration. The Use of Bayesian Statistics in Medical Device Clinical Trials: Guidance for Industry and Food and Drug Administration Staff, 2010. http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm071072.htm . Accessed 2 Aug 2022.
Campbell G. Bayesian statistics in medical devices: Innovation sparked by FDA. J Biopharm Stat. 2011;21:871–87.
pubmed: 21830920
doi: 10.1080/10543406.2011.589638
Campbell G. The experience in the center for devices and radiological health with Bayesian strategies. Clin Trials J. 2005;2:359–63.
doi: 10.1191/1740774505cn093oa
Irony T, Simon R. Application of Bayesian methods to medical device trials. In: Becker KM, Whyte JJ, editors. Clinical evaluation of medical devices, principles and case studies. 2nd ed. New York: Humana Press; 2006. p. 99–116.
doi: 10.1007/978-1-59745-004-1_5
Pennello GA, Thompson L. Experience with reviewing Bayesian medical device trials. J Biopharm Stat. 2008;18(1):81–115.
pubmed: 18161543
doi: 10.1080/10543400701668274
Bonangelino P, Irony T, Liang S, Li X, Mukhi V, Ruan S, Xu Y, Yang X, Wang C. Bayesian approaches in medical device clinical trials: a discussion with examples in the regulatory setting. J Biopharm Stat. 2011;21(5):938–53.
pubmed: 21830924
doi: 10.1080/10543406.2011.589650
O’Malley AJ, Normand S-LT. Statistics: keeping pace with the medical technology revolution. Chance. 2003;16(4):41–4. https://doi.org/10.1080/09332480.2003.10554874 .
doi: 10.1080/09332480.2003.10554874
National Research Council. Combining information: statistical issues and opportunities for research. Washington, DC: The National Academies Press; 1992. https://doi.org/10.17226/20865 .
doi: 10.17226/20865
U.S. Food and Drug Administration, Center for Devices and Radiological Health, "PMA P170030 FDA Summary of Safety and Effectiveness Data," Available at https://www.accessdata.fda.gov/cdrh_docs/pdf17/P170030B.pdf . Accessed 2 Aug 2022.
Ibrahim JG, Chen M-H. Power prior distributions for regression models. Stat Sci. 2000;15(1):46–60.
Ye K, Han Z, Duan Y, Bai T. Normalized power prior Bayesian analysis. 2022; arXiv:2204.05615 [stat.ME]. Accessed at arXiv:2204.05615 .
U.S. Food and Drug Administration, Center for Devices and Radiological Health, "PMA P160052 FDA Summary of Safety and Effectiveness Data," Available at https://www.accessdata.fda.gov/cdrh_docs/pdf16/P160052B.pdf . Accessed 2 Aug 2022.
Hobbs BP, Carlin BP, Mandrekar SJ, et al. Hierarchical commensurate and power prior models for adaptive incorporation of historical information in clinical trials. Biometrics. 2011;67(3):1047–56.
pubmed: 21361892
doi: 10.1111/j.1541-0420.2011.01564.x
Hobbs BP, Sargent DJ, Carlin BP. Commensurate priors for incorporating historical information in clinical trials using general and generalized linear model. Bayesian Anal. 2012;7(3):639–74.
pubmed: 24795786
doi: 10.1214/12-BA722
Mitchell TJ, Beauchamp JJ. Bayesian variable selection in linear regression. J Amer Statist Assoc. 1988;83(404):1023–32.
doi: 10.1080/01621459.1988.10478694
Malec D. A closer look at combining data among a small number of binomial experiments. Stat Med. 2001;20:1811–24.
pubmed: 11406843
doi: 10.1002/sim.782
O’Malley AJ, Normand SL, Kuntz RE. Sample size calculation for a historically controlled clinical trial with adjustment for covariates. J Biopharm Stat. 2002;12(2):227–47.
pubmed: 12413242
doi: 10.1081/BIP-120015745
O’Malley AJ, Normand SL, Kuntz RE. Application of models for multivariate mixed outcomes to medical device trials: coronary artery stenting. Stat Med. 2003;22(2):313–36.
pubmed: 12520564
doi: 10.1002/sim.1337
Kadhhodayan Y, Somogyi CT, Cross DT, et al. Technical, angiographic and clinical outcomes of neuroform 1, 2, 2 treo and 3 devices in stent-assisted coiling of intracranial aneurysms. J Neurointerv Surg. 2012;4:368–74.
doi: 10.1136/neurintsurg-2011-010076
Morita S, Thall PF, Müller P. Determining the effective sample size of a parametric prior. Biometrics. 2008;64:595–602.
pubmed: 17764481
doi: 10.1111/j.1541-0420.2007.00888.x
Neuenschwander B, Weber S, Schmidli H, O’Hagan A. Predictively consistent prior effective sample sizes. Biometrics. 2020;76(2):578–87.
pubmed: 32142163
doi: 10.1111/biom.13252
Viele K, Berry S, Neuenschwander B, et al. Use of historical control data for assessing treatment effects in clinical trials. Pharm Stat. 2014;13(1):41–54.
pubmed: 23913901
doi: 10.1002/pst.1589
Thompson L, Chu J, Xu J, et al. Dynamic borrowing from a single prior data source using the conditional power prior. J Biopharm Stat. 2021;31(4):403–24.
pubmed: 34520325
doi: 10.1080/10543406.2021.1895190
Jiang L, Nie L, Yuan Y. Elastic priors to dynamically borrow information from historical data in clinical trials. Biometrics. 2021. https://doi.org/10.1111/biom.13551 .
doi: 10.1111/biom.13551
pubmed: 34854477
Psioda M, Ibrahim J. Bayesian clinical trial design using historical data that inform the treatment effect. Biostatistics. 2019;20(3):400–15.
pubmed: 29547966
doi: 10.1093/biostatistics/kxy009
Hobbs BP, Carlin BP, Sargent DJ. Adaptive adjustment of the randomization ratio using historical control data. Clin Trials J. 2013;10:430–40.
doi: 10.1177/1740774513483934
Kotalik A, Vock D, Donny E, et al. Dynamic borrowing in the presence of treatment effect heterogeneity. Biostatistics. 2021;22(4):789–804.
pubmed: 31977040
doi: 10.1093/biostatistics/kxz066
U.S. Food and Drug Administration. Leveraging Existing Clinical Data for Extrapolation to Pediatric Uses of Medical Devices: Guidance for Industry and Food and Drug Administration Staff, 2016. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/leveraging-existing-clinical-data-extrapolation-pediatric-uses-medical-devices . Accessed 23 Aug 2022.
Kovalchik SA, Varadhan R, Weiss CO. Assessing heterogeneity of treatment effect in a clinical trial with the proportional interactions model. Stat Med. 2013;32(28):4906–23.
pubmed: 23788362
doi: 10.1002/sim.5881
U.S. Food and Drug Administration, Center for Devices and Radiological Health, "PMA P970003/S207 FDA Summary of Safety and Effectiveness Data," Available at https://www.accessdata.fda.gov/cdrh_docs/pdf/p970003s207b.pdf . Accessed 3 Aug 2022.
Gelman A, Hill J, Yajima M. Why we (usually) don’t have to worry about multiple comparisons. J Res Educ Eff. 2012;5(2):189–211.
Alosh M, Fritsch K, Huque M, et al. Statistical considerations on subgroup analysis in clinical trials. Stat Biopharm Res. 2015;7(4):286–303.
doi: 10.1080/19466315.2015.1077726
Henderson NC, Louis TA, Wang C, Varadhan R. Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research. Health Serv Outcomes Res Methodol. 2016;16(4):213–33.
pubmed: 27881932
doi: 10.1007/s10742-016-0159-3
Lewis C. Thayer DT A loss function related to the FDR for random effects multiple comparisons. J Stat Plan Inference. 2004;125:49–58.
doi: 10.1016/j.jspi.2003.07.020
Pennello G, Rothmann M. Bayesian subgroup analysis with hierarchical models. In: Menon S, Peace KE, chen D-G, editors. Biopharmaceutical applied statistics symposium. Springer: Biostatistical Analysis of Clinical Trials; 2019.
US. Food and Drug Administration, Center for Devices and Radiological Health, "PMA P170027 FDA Summary of Safety and Effectiveness Data," Available at https://www.accessdata.fda.gov/cdrh_docs/pdf17/P170027B.pdf . Accessed 3 Aug 2022.
Chow SC, Chang M. Adaptive design methods in clinical trials–a review. Orphanet J Rare Dis. 2008. https://doi.org/10.1186/1750-1172-3-11 .
doi: 10.1186/1750-1172-3-11
pubmed: 18454853
Hobbs BP, Carlin BP. Practical Bayesian design and analysis for drug and device clinical trials. J Biopharm Stat. 2008;18(1):54–80.
pubmed: 18161542
doi: 10.1080/10543400701668266
Broglio KR, Connor JT, Berry SM. Not too big, not too small: a goldilocks approach to sample size selection. J Biopharm Stat. 2014;24(3):685–705.
pubmed: 24697532
doi: 10.1080/10543406.2014.888569
Berry SM, Carlin BP, Lee JJ, et al. Bayesian adaptive methods for clinical trials. Boca Raton, FL: CRC Press; 2011.
Campbell G. Similarities and differences of Bayesian designs and adaptive designs for medical devices: a regulatory view. Stat Biopharm Res. 2013;5:356–68.
doi: 10.1080/19466315.2013.846873
U.S. Food and Drug Administration. 2016. Adaptive Designs for Medical Device Clinical Studies: Guidance for Industry and Food and Drug Administration Staff. Available at https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm446729.pdf . Accessed 2 Aug 2022.
U.S. Food and Drug Administration, Center for Devices and Radiological Health, "PMA P100045 FDA Summary of Safety and Effectiveness Data," Available at https://www.accessdata.fda.gov/cdrh_docs/pdf10/P100046B.pdf . Accessed 2 Aug 2022.
U.S. Food and Drug Administration, Center for Devices and Radiological Health, "PMA P180050 FDA Summary of Safety and Effectiveness Data," Available at https://www.accessdata.fda.gov/cdrh_docs/pdf18/P180050b.pdf . Accessed 2 Aug 2022.
Zile MR, Abraham WT, Lindenfeld J, Weaver FA, Zannad F, Graves T, Rogers T, Galle EG. First granted example of novel FDA trial design under expedited access pathway for premarket approval: BeAT-HF. Am Heart J. 2018;204:139–50.
pubmed: 30118942
doi: 10.1016/j.ahj.2018.07.011
Campbell G, Pennello G, Yue L. Missing data in the regulation of medical devices. J Biopharm Stat. 2011;21(2):180–95.
pubmed: 21390995
doi: 10.1080/10543406.2011.550094
Tanner M. Tools for statistical inference: methods for the exploration of posterior distributions and likelihood functions. 3rd ed. Springer; 1996.
doi: 10.1007/978-1-4612-4024-2
Little R, Rubin D. Statistical analysis with missing data. 3rd ed. Wiley; 2019.
Pennello GA. Bayesian analysis of diagnostic test accuracy when disease state is unverified for some subjects. J Biopharm Stat. 2011;21:954–70.
pubmed: 21830925
doi: 10.1080/10543406.2011.590921
U.S. Food and Drug Administration. Factors to Consider When Making Benefit-Risk Determinations in Medical Device Premarket Approval and De Novo Classifications: Guidance for Industry and Food and Drug Administration Staff. 2019. Available at https://www.fda.gov/media/99769/download . Accessed 2 Aug 2022.
Fu B, Li X, Scott J, He W. A new framework to address challenges in quantitative benefit-risk assessment for medical products. Contemp Clin Trials. 2020;95:106073. https://doi.org/10.1016/j.cct.2020.106073 .
doi: 10.1016/j.cct.2020.106073
pubmed: 32622973
Lewis RJ, Berry DA. Group-sequential clinical trials: a classical evaluation of Bayesian decision-theoretic designs. J Amer Statist Assoc. 1994;89:1528–34.
doi: 10.1080/01621459.1994.10476893
Rosner GL. Bayesian methods in regulatory science. Stat Biopharm Res. 2020;12(2):130–6. https://doi.org/10.1080/19466315.2019.1668843 .
doi: 10.1080/19466315.2019.1668843
pubmed: 32489520
U.S. Food and Drug Administration. Patient Preference Information–Voluntary Submission, Review in Premarket Approval Applications, Humanitarian Device Exemption Applications, and De Novo Requests, and Inclusion in Decision Summaries and Device Labeling: Guidance for Industry, Food and Drug Administration Staff, and Other Stakeholders, 2016. Available at https://www.fda.gov/media/92593/download . Accessed 2 Aug 2022.
Hauber AB, González JM, Groothuis-Oudshoorn CG, Prior T, Marshall DA, Cunningham C, IJzerman MJ, Bridges JF. Statistical methods for the analysis of discrete choice experiments: a report of the ISPOR conjoint analysis good research practices task force. Value Health. 2016;19(4):300–15. https://doi.org/10.1016/j.jval.2016.04.004 .
doi: 10.1016/j.jval.2016.04.004
pubmed: 27325321
Hatfield LA, Baugh CM, Azzone V, et al. Regulator loss functions and hierarchical modeling for safety decision making. Med Decis Mak. 2017;37(5):512–22.
doi: 10.1177/0272989X16686767
Pepe MS, Janes H, Li CI, Bossuyt PM, Feng Z, Hilden J. Early-phase studies of biomarkers: what target sensitivity and specificity values might confer clinical utility? Clin Chem. 2016;62(5):737–42.
pubmed: 27001493
doi: 10.1373/clinchem.2015.252163
Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak. 2006;26(6):565–74.
doi: 10.1177/0272989X06295361
Kerr KF, Brown MD, Zhu K, Janes H. Assessing the clinical impact of risk prediction models with decision curves: guidance for correct interpretation and appropriate use. J Clin Oncol. 2016;34(21):2534–40.
pubmed: 27247223
doi: 10.1200/JCO.2015.65.5654
Kerr KF, Marsh TL, Janes H. The importance of uncertainty and opt-in v. opt-out: best practices for decision curve analysis. Med Decis Mak. 2019;39(5):491–2.
doi: 10.1177/0272989X19849436
Baker SG. Putting risk prediction in perspective: relative utility curves. J Natl Cancer Inst. 2009;101(22):1538–1542. Erratum in: J Natl Cancer Inst. 2014;106(11):dju337.
Marsh TL, Janes H, Pepe MS. Statistical inference for net benefit measures in biomarker validation studies. Biometrics. 2020;76(3):843–52.
pubmed: 31732971
doi: 10.1111/biom.13190
Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39(4):561–577. Erratum in: Clin Chem. 1993;39(8):1589.
Yang X, Thompson L, Chu J, et al. Adaptive design practice at the Center for Devices and Radiological Health (CDRH), January 2007 to May 2013. Therap Innov Reg Sci. 2016;50(6):710–7.
doi: 10.1177/2168479016656027
U.S. Food and Drug Administration. Breakthrough Devices Program: Guidance for Industry and Food and Drug Administration Staff, December 2018. Available at https://www.fda.gov/regulatory-information/search-fda-guidance-documents/breakthrough-devices-program . Accessed 24 Aug 2022.
U.S. Food and Drug Administration, Center for Devices and Radiological Health, "PMA P180007 FDA Summary of Safety and Effectiveness Data," Available at https://www.accessdata.fda.gov/cdrh_docs/pdf18/P180007b.pdf . Accessed 2 Aug 2022.
U.S. Food and Drug Administration, Center for Devices and Radiological Health, "PMA P180036 FDA Summary of Safety and Effectiveness Data," Available at https://www.accessdata.fda.gov/cdrh_docs/pdf18/P180036b.pdf . Accessed 2 Aug 2022.
U.S. Food and Drug Administration, Center for Devices and Radiological Health, "PMA P190016 FDA Summary of Safety and Effectiveness Data," Available at https://www.accessdata.fda.gov/cdrh_docs/pdf19/P190016b.pdf . Accessed 2 Aug 2022.
U.S. Food and Drug Administration, Center for Devices and Radiological Health, "PMA P210034 FDA Summary of Safety and Effectiveness Data," Available at https://www.accessdata.fda.gov/cdrh_docs/pdf21/P210034b.pdf . Accessed 2 Aug 2022.
U.S. Food and Drug Administration, Center for Devices and Radiological Health, "PMA P170019 FDA Summary of Safety and Effectiveness Data," Available at https://www.accessdata.fda.gov/cdrh_docs/pdf17/P170019b.pdf . Accessed 2 Aug 2022.
U.S. Food and Drug Administration (2017). The Use of Real World Evidence to Support Regulatory Decision-Making: Guidance for Industry and Food and Drug Administration Staff. Available at https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-real-world-evidence-support-regulatory-decision-making-medical-devices . Accessed 2 Aug 2022.
U.S. Food and Drug Administration (2021). Center for Devices and Radiological Health. Examples of Real-World Evidence (RWE) Used in Medical Device Regulatory Decisions. Available at https://www.fda.gov/media/146258/download . Accessed 2 Aug 2022.
U.S. Food and Drug Administration, Center for Devices and Radiological Health, "PMA P070015/S128 and P110019/S075 FDA Summary of Safety and Effectiveness Data," Available at https://www.accessdata.fda.gov/cdrh_docs/pdf11/P110019S075B.pdf . Accessed 2 Aug 2022.
Campbell G. Regulatory acceptance of Bayesian statistics. In: Lesaffre E, Baio G, Boulanger B, editors. Bayesian methods in pharmaceutical research. Boca Raton: CRC Press; 2020. p. 41–51.
doi: 10.1201/9781315180212-2
Kurzenhäuser S, Hoffrage U. Teaching Bayesian reasoning: an evaluation of a classroom tutorial for medical students. Med Teach. 2002;24(5):516–21.
pubmed: 12450472
doi: 10.1080/0142159021000012540
Sedlmeier P, Gigerenzer G. Teaching Bayesian reasoning in less than two hours. J Exp Psych. 2001;130:380–400.
doi: 10.1037/0096-3445.130.3.380
Meurer WJ, Lewis RJ, Tagle D, et al. An overview of the adaptive designs accelerating promising trials into treatments (ADAPT-IT) project. Ann Emerg Med. 2012;60(4):451–7.
pubmed: 22424650
doi: 10.1016/j.annemergmed.2012.01.020
Yue L. Regulatory considerations in the design of comparative observational studies using propensity scores. J Biopharm Stat. 2012;22:1272–9.
pubmed: 23075022
doi: 10.1080/10543406.2012.715111
Wang C, Li H, Chen WC, et al. Propensity score-integrated power prior approach for incorporating real-world evidence in single-arm clinical studies. J Biopharm Stat. 2019;29(5):731–48.
pubmed: 31530111
doi: 10.1080/10543406.2019.1657133
Li H, Chen WC, Wang C, Lu N, Song C, Tiwari R, Xu Y, Yue LQ. Augmenting both arms of a randomized controlled trial using external data: an application of the propensity score-integrated approaches. Stat Biosci. 2022;14(1):79–89.
pubmed: 34178164
doi: 10.1007/s12561-021-09315-5
Haddad T, Himes A, Thompson L, et al. Incorporation of stochastic engineering models as prior information in Bayesian medical device trials. J Biopharm Stat. 2017;27(6):1089–103.
pubmed: 28281931
doi: 10.1080/10543406.2017.1300907
Badano A. In silico imaging clinical trials: cheaper, faster, better, safer, and more scalable. Trials. 2021. https://doi.org/10.1186/s13063-020-05002-w .
doi: 10.1186/s13063-020-05002-w
pubmed: 33468186
Jang KJ, Pant YV, Zhang B, et al. Robustness evaluation of computer-aided clinical trials for medical devices. In: Proceedings of 10th ACM/IEEE International Conference on CyberPhysical Systems, April 16–18, 2019, Montreal, QC, Canada. ACM, New York, NY 2019;163–173. Accessed at dl.acm.org/doi/ https://doi.org/10.1145/3302509.3311058
Badano A, Graff CG, Badal A, et al. Evaluation of digital breast tomosynthesis as replacement of full-field digital mammography using an in silico imaging trial. JAMA Netw Open. 2018;1(7):e185474.
pubmed: 30646401
doi: 10.1001/jamanetworkopen.2018.5474
Medical Device Innovation Consortium. Incorporation of stochastic engineering models as prior information in Bayesian medical device trials. 2018 Dec. Available at https://mdic.org/news/incorporation-of-stochastic-engineering-models-as-prior-information-in-bayesian-medical-device-trials/ . Accessed 23 Aug 2022.
U.S. Food and Drug Administration. Humanitarian Device Exemption. Available at https://www.fda.gov/medical-devices/premarket-submissions-selecting-and-preparing-correct-submission/humanitarian-device-exemption . Accessed 23 Aug 2022.
Polack F, Thomas S, et al. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. N Engl J Med. 2020;383:2603–15.
pubmed: 33301246
doi: 10.1056/NEJMoa2034577
U.S. Food and Drug Administration. Complex Innovative Design Pilot Program Trial Design Case Studies. Available at https://www.fda.gov/drugs/development-resources/complex-innovative-trial-design-meeting-program . Accessed 2 Aug 2022.
Fayers PM, Ashby D, Parmar MK. Tutorial in biostatistics Bayesian data monitoring in clinical trials. Stat Med. 1997;16(12):1413–30. https://doi.org/10.1002/(sici)1097-0258(19970630)16 .
doi: 10.1002/(sici)1097-0258(19970630)16
pubmed: 9232762
Ferraioli G, Tinelli C, Zicchetti M, et al. Reproducibility of real-time shear wave elastography in the evaluation of liver elasticity. Eur J Radiol. 2012;81(11):3102–6.
pubmed: 22749107
doi: 10.1016/j.ejrad.2012.05.030
Albert JH, Chib S. Bayesian analysis of binary and polychotomous response data. J Amer Statist Assoc. 1993;88:669–79.
doi: 10.1080/01621459.1993.10476321
Johnson VE, Albert JH. Ordinal data modeling. Springer; 1999.
doi: 10.1007/b98832
Rice K, Ye L. Expressing regret: a unified view of credible intervals. Am Stat. 2022. https://doi.org/10.1080/00031305.2022.2039764 .
doi: 10.1080/00031305.2022.2039764
pubmed: 36035272
Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. Npj Digit Med. 2020;3(1):18. https://doi.org/10.1038/s41746-020-00324-0 .
doi: 10.1038/s41746-020-00324-0
Dunson DB. Statistics in the big data era: Failures of the machine. Stat Probab Lett. 2018;136:4–9.
doi: 10.1016/j.spl.2018.02.028
Loghmanpour NA, Kanwar MK, Druzdzel MJ, et al. A new Bayesian network-based risk stratification model for prediction of short-term and long-term LVAD mortality. ASAIO J. 2015;61(3):313–23.
pubmed: 25710772
doi: 10.1097/MAT.0000000000000209
Forsberg JA, Potter BK, Wagner MB, et al. Lessons of war: turning data into decisions. EBioMedicine. 2015;2(9):1235–42. Accessed at https://walterreed.tricare.mil/Health-Services/Specialty-Care/Murtha-Cancer-Center/Orthopaedic-Oncology/Jonathan-Agner-Forsberg-MD . Accessed 23 Aug 2022.
Stojadinovic A, Eberhardt J, Brown TS, et al. Development of a Bayesian model to estimate health care outcomes in the severely wounded. J Multidiscip Healthc. 2010;3:125–35.
pubmed: 21197361
doi: 10.2147/JMDH.S11537
Wang C, Louis T, Weiss C, et al. Beanz: an R package for Bayesian analysis of heterogeneous treatment effect with graphical user interface. J Stat Softw. 2018;85(7):1–31.
doi: 10.18637/jss.v085.i07
He X, Madigan C, Wellner J, et al. (2019). Statistics at a crossroads: Who is for the challenge? NSF Workshop report. National Science Foundation. https://www.nsf.gov/mps/dms/documents/Statistics_at_a_Crossroads_Workshop_Report_2019.pdf . Accessed 23 Aug 2022.