1. National Comprehensive Cancer Network . Clinical practice guidelines in oncology: colon cancer. Version 2.2015. Available from:http://www.nccn.org/professionals/physician_gls/pdf/colon.pdf. Accessed September 28, 2016.
Google Scholar2. National Comprehensive Cancer Network . Clinical practice guidelines in oncology: rectal cancer. Version 2.2015. Available from:http://www.nccn.org/professionals/physician_gls/pdf/rectal.pdf. Accessed September 28, 2016.
Google Scholar3. Meyerhardt, JA, Mangu, PB, Flynn, PJ, et al. Follow-up care, surveillance protocol, and secondary prevention measures for survivors of colorectal cancer: American Society of Clinical Oncology clinical practice guideline endorsement. J Clin Oncol. 2013;31(35):4465–70. doi:10.1200/JCO.2013.50.7442
Google Scholar | Crossref4. El-Shami, K, Oeffinger, KC, Erb, NL, et al. American Cancer Society Colorectal Cancer survivorship care guidelines. CA Cancer J Clin. 2015;65:427–55. doi:10.3322/caac.21286
Google Scholar | Crossref | Medline5. National Comprehensive Cancer Network . Clinical practice guidelines in oncology: prostate cancer. Version 1.2015. Available from: http://www.nccn.org./professionals/physician_gls/pdf/prostate.pdf. Accessed September 28, 2016.
Google Scholar6. Skolarus, TA, Wolf, A, Erb, NL, et al. American Cancer Society prostate cancer survivorship care guidelines. CA Cancer J Clin. 2014;64:225–49. doi:10.3322/caac.21234
Google Scholar | Crossref | Medline7. Harmandayan, GZ, Gao, F, Mutch, DG, Virgo, KS, Gibb, RK, Johnson, FE, et al. Ovarian cancer patient surveillance after curative-intent initial treatment. Gynecol Oncol. 2011;120(2):205–8. doi:10.1016/j.ygyno.2010.10.028
Google Scholar | Crossref | Medline8. Colt, HG, Murgu, SD, Korst, RJ, Slatore, CG, Unger, M, Silvia Quadrelli, S. Follow-up and surveillance of the patient with lung cancer after curative-intent therapy: diagnosis and management of lung cancer. 3rd ed. American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5 suppl):e437S–54S. doi:10.1378/chest.12-2365
Google Scholar | Crossref | Medline9. National Comprehensive Cancer Network . NCCN guidelines. Chronic myelogenous leukemia. Version 1.2015. Available from: http://www.nccn.org/professionals/physician_gls/f_guidelines.asl. Accessed November 23, 2014.
Google Scholar10. Garber, AJ, Handelsman, Y, Grunberger, G, et al. Consensus statement by the American Association of Clinical Endocrinologists and American College of Endocrinology on the comprehensive type 2 diabetes management algorithm—2020 executive summary. Endocr Pract. 2020;26(1):107–39.
Google Scholar | Crossref11. Surbone, A, Tralongo, P, et al. Categorization of cancer survivors: why we need it. J Clin Oncol. 2016;34(28):3372–4 .
Google Scholar | Crossref | Medline12. Filleron, T, Dalenc, F, Kramar, A, et al. Personalised cancer follow-up: risk stratification, needs assessment or both? Br J Cancer. 2012;106:1579–80. doi:10.1038/bjc.2012.108
Google Scholar | Crossref | Medline13. Raiffa, H, Schaifer, R. Applied Statistical Decision Theory. New York: Wiley; 1961/2000.
Google Scholar14. Stigler, GJ , et al. The economics of information. J Polit Econ. 1961;69(3):213–25. doi:10.1086/258464
Google Scholar | Crossref15. Kunst, NR, Alarid-Escudero, F, Paltiel, AD, Wang, S, et al. A value of information analysis of research on the 21-gene assay for breast cancer management. Value Health. 2019;22:1102–10.
Google Scholar | Crossref | Medline16. Parmigiani, G , et al. Modeling in Medical Decision Making. New York: John Wiley & Sons: 2002.
Google Scholar17. Parmigiani, G , et al. Decision model in screening for breast cancer. In: Bernardo, JM, Berger, JO, Dawid, AP, Smith, AFM, eds. Bayesian Statistics. Vol. 6. Oxford: Oxford University Press; 1999.
Google Scholar18. Rizopoulos, D, Taylor, JMG, Rosmalen, JV, Steyerberg, EW, Takkenberg, JM, et al. Personalized screening intervals for biomarkers using joint models for longitudinal and survival data. Biostatistics. 2016;17:149–64. doi:10.1093/biostatistics/kxv031
Google Scholar | Crossref | Medline19. Li, H, Gatsonis, C, et al. Dynamic optimal strategy for monitoring disease recurrence. Sci China Math. 2012;55(8):1565–82. doi:10.1007/s11425-012-4475-y
Google Scholar | Crossref20. Chakraborty, B, Moodie, EEM. Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine. New York: Springer; 2013.
Google Scholar | Crossref21. Robins, JM , et al. Optimal structural nested models for optimal sequential decisions. In: Lin, DY, Heagerty, P, eds. Proceedings of the Second Seattle Symposium on Biostatistics. New York: Springer; 2004. p 189–326.
Google Scholar | Crossref22. Alagoz, O, Hsu, H, Schaefer, AJ, Roberts, MS, et al. Markov decision processes: a tool for sequential decision making under uncertainty. Med Decis Making. 2010;30(4):474–83.
Google Scholar | SAGE Journals23. Chhatwal, J, Alagoz, O, Burnside, ES, et al. Optimal breast biopsy decision-making based on mammographic features and demographic factors. Oper Res. 2010;58(6):1577–91.
Google Scholar | Crossref | Medline24. Alagoz, O, Chhatwal, J, Burnside, ES, et al. Optimal policies for reducing unnecessary followup mammography exams in breast cancer diagnosis. Decis Anal. 2013;10(3):200–24.
Google Scholar | Crossref | Medline25. Ayer, T, Alagoz, O, Stout, NK, et al. A POMDP approach to personalize mammography screening decisions. Oper Res. 2012;60(5):1019–34.
Google Scholar | Crossref | Medline26. Steimle, LN, Denton, BT, et al. Markov decision processes for screening and treatment of chronic diseases. In: Boucherie, R, van Dijk, N, eds. Markov Decision Processes in Practice. International Series in Operations Research & Management Science. Vol 248. Cham (UK): Springer; 2017.
Google Scholar | Crossref27. Zhang, J, Denton, BT, Balasubramanian, H, et al. Optimization of prostate biopsy referral decisions. Man Serv Oper Manag. 2012;14:529–47.
Google Scholar | Medline28. Raiffa, H, Schlaifer, R, et al. Applied Statistical Decision Theory. Boston: Harvard University Press; 1961.
Google Scholar29. Parmigiani, G, Inoue, LYT. Decision Theory: Principles and Approaches. New York: John Wiley & Sons; 2009.
Google Scholar | Crossref30. Briggs, A, Sculpher, M, Claxton, K, et al. Decision Modelling for Health Economic Evaluation. Oxford (UK): Oxford University Press; 2006.
Google Scholar31. Karrison, T , et al. Restricted mean life with adjustment for covariates. J Am Stat Assoc. 1987;82(400):1169–76.
Google Scholar | Crossref | Medline32. Zucker, D , et al. Restricted mean life with covariates: modification and extension of a useful survival analysis method. J Am Stat Assoc. 1998;93:702–9.
Google Scholar | Crossref33. Calkins, KL, Canan, CE, Moore, RD, et al. An application of restricted mean survival time in a competing risks setting: comparing time to ART initiation by injection drug use. BMC Med Res Methodol. 2018;18:27.
Google Scholar | Crossref | Medline34. Sutton, RS, Barto, AG, et al. Reinforcement Learning: An Introduction. Cambridge (MA): MIT Press; 2018.
Google Scholar35. Carlin, B, Kadane, J, Gelfand, A, et al. Approaches for optimal sequential decision analysis in clinical trials. Biometrics. 1998;54:964–75.
Google Scholar | Crossref | Medline36. Cheng, Y, Shen, Y, et al. Bayesian adaptive designs for clinical trials. Biometrika. 2005;92(3):633–46.
Google Scholar | Crossref37. Tsiatis, A, DeGruttola, V, Wulfsohn, MS, et al. Modeling the relationship of survival to longitudinal data measured with error: applications to survival and CD4 counts in patients with AIDS. J Am Stat Assoc. 1995;90:27–37.
Google Scholar | Crossref | ISI38. Faucett, CL, Thomas, DC, et al. Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach. Stat Med. 1996;15:1663–85.
Google Scholar | Crossref | Medline39. Maziarz, M, Heagerty, PJ, Cai, T, Zheng, Y, et al. On longitudinal prediction with time-to-event outcome: comparison of modeling options. Biometrics. 2017;73:83–93.
Google Scholar | Crossref | Medline40. Meltzer, DO, Hoomans, T, Chung, JW, Basu, A. Minimal Modeling Approaches to Value of Information Analysis for Health Research. Methods Future Research Needs Reports, No. 6. Rockville (MD): Agency for Healthcare Research and Quality; 2011. Available from: https://www.ncbi.nlm.nih.gov/books/NBK62146/
Google Scholar41. Knapp, EA, Fink, AK, Goss, CH, et al. The Cystic Fibrosis Foundation patient registry: design and methods of a national observational disease registry. Ann Am Thorac Soc. 2016 ;3(7):1173–9.
Google Scholar | Crossref42. Diggle, PJ, Heagerty, PJ, Liang, KY, Zeger, SL, et al. Analysis of Longitudinal Data. Oxford (UK): Oxford University Press; 2002.
Google Scholar43. Ramos, KJ, Somayaji, R, Lease, ED, Goss, CH, Aitken, ML, et al. Cystic fibrosis physicians’ perspectives on the timing of referral for lung transplant evaluation: a survey of physicians in the United States. BMC Pulm Med. 2017;17(1):21.
Google Scholar | Crossref | Medline44. Ramos, KJ, Quon, BS, Psoter, KJ, et al. Predictors of non-referral of patients with cystic fibrosis for lung transplant evaluation in the United States. J Cyst Fibros. 2016;15(2):196–203.
Google Scholar | Crossref | Medline45. Liu, Y, Vela, M, Rudakevych, T, Wigfield, C, Garrity, E, Saunders, MR, et al. Patient factors associated with lung transplant referral and waitlist for patients with cystic fibrosis and pulmonary fibrosis. J Heart Lung Transplant. 2017;36(3):264–71.
Google Scholar | Crossref46. Ramos, KJ, Quon, BS, Psoter, KJ, et al. Predictors of non-referral of patients with cystic fibrosis for lung transplant evaluation in the United States. J Cyst Fibros. 2016;15(2):196–203.
Google Scholar | Crossref | Medline47. Liou, TG, Adler, FR, Cahill, BC, et al. Survival effect of lung transplantation among patients with cystic fibrosis. JAMA. 2001;286(21):2683–9.
Google Scholar | Crossref | Medline48. Thabut, G, Christie, JD, Mal, H, et al. Survival benefit of lung transplant for cystic fibrosis since lung allocation score implementation. Am J Respir Crit Care Med. 2013;187(12):1335–40.
Google Scholar | Crossref | Medline49. Vock, DM, Durheim, MT, Tsuang, WM, et al. Survival benefit of lung transplantation in the modern era of lung allocation. Ann Am Thorac Soc. 2017;14(2):172–81.
Google Scholar | Medline50. Dupont, WD , et al. Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data. Cambridge (UK): Cambridge University Press: 2009.
Google Scholar | Crossref51. Institute of Medicine and National Research Council . Implementing Cancer Survivorship Care Planning. Washington (DC): National Academies Press; 2007.
Google Scholar