1. Streptomycin in Tuberculosis Trials Committee. Streptomycin treatment of pulmonary tuberculosis. Br. Med. J. 1948, 2, 769–782. [CrossRef]
2. Bothwell, L.E.; Podolsky, S.H. The emergence of the randomized, controlled trial. N. Engl. J. Med. 2016, 375, 501–504. [CrossRef] [PubMed]
3. Milsted, R. Cancer drug approval in the United States, Europe, and Japan. Adv. Cancer Res. 2006, 96, 371–391. [CrossRef]
4. Townsley, C.A.; Selby, R.; Siu, L.L. Systematic review of barriers to the recruitment of older patients with cancer onto clinical trials. J. Clin. Oncol. 2005, 23, 3112–3124. [CrossRef] [PubMed]
5. Liu, R.; Rizzo, S.; Whipple, S.; Pal, N.; Pineda, A.L.; Lu, M.; Arnieri, B.; Lu, Y.; Capra, W.; Copping, R.; et al. Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature 2021, 592, 629–633. [CrossRef] [PubMed]
6. Averitt, A.J.; Weng, C.; Ryan, P.; Perotte, A. Translating evidence into practice: Eligibility criteria fail to eliminate clinically significant differences between real-world and study populations. NPJ Digit. Med. 2020, 3, 67. [CrossRef] [PubMed]
7. Okuyama, A.; Higashi, T. Patterns of cancer treatment in different age groups in Japan: An analysis of hospital-based cancer registry data, 2012–2015. Jpn. J. Clin. Oncol. 2018, 48, 417–425. [CrossRef]
8. Hilgers, R.; König, F.; Molenberghs, G. Design and analysis of clinical trials for small rare disease populations. J. Rare Dis. Res. Treat. 2016, 1, 53–60. [CrossRef]
9. Latimer, N.R. Survival analysis for economic evaluations alongside clinical trials—Extrapolation with patient-level data: Inconsis- tencies, limitations, and a practical guide. Med. Decis. Mak. 2013, 33, 743–754. [CrossRef]
10. Corrigan-Curay, J.; Sacks, L.; Woodcock, J. Real-world evidence and real-world data for evaluating drug safety and effectiveness. J. Am. Med. Assoc. 2018, 320, 867–868. [CrossRef]
11. Katkade, V.B.; Sanders, K.N.; Zou, K.H. Real world data: An opportunity to supplement existing evidence for the use of long-established medicines in health care decision making. J. Multidiscip. Healthc. 2018, 11, 295–304. [CrossRef] [PubMed]
12. Gerstein, H.C.; McMurray, J.; Holman, R.R. Real-world studies no substitute for RCTs in establishing efficacy. Lancet 2019, 393, 210–211. [CrossRef]
13. Wang, S.V.; Schneeweiss, S.; Gagne, J.J.; Evers, T.; Gerlinger, C.; Desai, R.; Najafzadeh, M. Using real-world data to extrapolate evidence from randomized controlled trials. Clin. Pharmacol. Ther. 2019, 105, 1156–1163. [CrossRef] [PubMed]
14. Makady, A.; de Boer, A.; Hillege, H.; Klungel, O.; Goettsch, W. What is real-world data? A review of definitions based on literature and stakeholder interviews. Value Health 2017, 20, 858–865. [CrossRef]
15. Jansana, A.; Del Cura, I.; Prados-Torres, A.; Cuesta, T.S.; Poblador-Plou, B.; Miguel, A.G.; Lanzuela, M.; Ibañez, B.; Tamayo, I.; Moreno-Iribas, C.; et al. Use of real-world data to study health services utilisation and comorbidities in long-term breast cancer survivors (the SURBCAN study): Study protocol for a longitudinal population-based cohort study. BMJ Open 2020, 10, e040253. [CrossRef]
16. Kang, J.; Cairns, J. Protocol for data extraction: How real-world data have been used in the National Institute for Health and Care Excellence appraisals of cancer therapy. BMJ Open 2022, 12, e055985. [CrossRef]
17. Ministry of Health, Labour and Welfare. Medical Facility Survey of Japan 2020, Ministry of Health, Labour and Welfare Web Site. Available online: https://www.mhlw.go.jp/toukei/saikin/hw/iryosd/m20/dl/is2003_01.pdf (accessed on 30 April 2022). (In Japanese)
18. Hayashida, K.; Murakami, G.; Matsuda, S.; Fushimi, K. History and profile of diagnosis procedure combination (DPC): Develop- ment of a real data collection system for acute inpatient care in Japan. J. Epidemiol. 2021, 31, JE20200288. [CrossRef]
19. National Cancer Center, Japan. The Standardised Roles for Hospital Based Cancer Registries. Available online: https://ganjoho. jp/med_pro/cancer_control/can_reg/hospital/regulation.html17 (accessed on 30 April 2022). (In Japanese)
20. Okuyama, A.; Watabe, M.; Makoshi, R.; Takahashi, H.; Tsukada, Y.; Higashi, T. Impact of the COVID-19 pandemic on the diagnosis of cancer in Japan: Analysis of hospital-based cancer registries. Jpn. J. Clin. Oncol. 2022, 52, 1215–1224. [CrossRef]
21. Brierley, J.D.; Gospodarowicz, M.K.; Wittekind, C. TNM Classification of Malignant Tumours Stage, 8th ed.; Wiley-Blackwell: Hoboken, NJ, USA, 2016.
22. Quan, H.; Sundararajan, V.; Halfon, P.; Fong, A.; Burnand, B.; Luthi, J.-C.; Saunders, L.D.; Beck, C.A.; Feasby, T.E.; Ghali, W.A. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med. Care 2005, 43, 1130–1139. [CrossRef]
23. Japan Clinical Oncology Group. Common Terminology Criteria for Adverse Events v. 5.0 Japanese Translation JCOG Version (CTCAE v. 5.0-JCOG). 2017. Available online: http://www.jcog.jp/doctor/tool/CTCAEv5.0J_20180915_miekeshi_v21_1.pdf (accessed on 30 April 2022).
24. Kuk, A.Y.C.; Chen, C.-H. A mixture model combining logistic regression with proportional hazards regression. Biometrika 1992, 79, 531–541. [CrossRef]
25. Miltenberger, R.; Götte, H.; Schüler, A.; Jahn-Eimermacher, A. Progression-free survival in oncological clinical studies: Assessment time bias and methods for its correction. Pharm. Stat. 2021, 20, 864–878. [CrossRef] [PubMed]
26. Aleksakhina, S.N.; Imyanitov, E.N. Cancer therapy guided by mutation tests: Current status and perspectives. Int. J. Mol. Sci. 2021, 22, 10931. [CrossRef] [PubMed]
27. Lewandowska, A.; Rudzki, G.; Lewandowski, T.; Prochnicki, M.; Rudzki, S.; Laskowska, B.; Brudniak, J. Quality of life of cancer patients treated with chemotherapy. Int. J. Environ. Res. Public Health 2020, 17, 6938. [CrossRef] [PubMed]