Park HS, Lloyd S, Decker RH, Wilson LD, Yu JB. Overview of the Surveillance, Epidemiology, and End Results database: evolution, data variables, and quality assurance. Curr Probl Cancer. 2012;36(4):183–90.
Google Scholar
Malmgren JA, Calip GS, Atwood MK, Mayer M, Kaplan HG. Metastatic breast cancer survival improvement restricted by regional disparity: Surveillance, Epidemiology, and End Results and institutional analysis: 1990 to 2011. Cancer. 2020;126(2):390–9.
Google Scholar
Sasaki K, Jabbour E, Short NJ, Jain N, Ravandi F, Pui CH, et al. Acute lymphoblastic leukemia: a population-based study of outcome in the United States based on the Surveillance, Epidemiology, and End Results (SEER) database, 1980–2017. Am J Hematol. 2021;96(6):650–8.
Google Scholar
Miller KD, Nogueira L, Devasia T, Mariotto AB, Yabroff KR, Jemal A, et al. Cancer treatment and survivorship statistics, 2022. CA Cancer J Clin. 2022;72(5):409–36.
Google Scholar
Mehta RS, Lenzner D, Argiris A. Race and health disparities in patient refusal of surgery for early-stage non-small cell lung cancer: a SEER cohort study. Ann Surg Oncol. 2012;19(3):722–7.
Google Scholar
Zavala VA, Bracci PM, Carethers JM, Carvajal-Carmona L, Coggins NB, Cruz-Correa MR, et al. Cancer health disparities in racial/ethnic minorities in the United States. Br J Cancer. 2021;124(2):315–32.
Google Scholar
Daly MC, Paquette IM. Surveillance, Epidemiology, and End Results (SEER) and SEER-medicare databases: use in clinical research for improving colorectal cancer outcomes. Clin Colon Rectal Surg. 2019;32(1):61–8.
Google Scholar
Brar G, Greten TF, Graubard BI, Mcneel TS, Petrick JL, Mcglynn KA, et al. Hepatocellular carcinoma survival by etiology: a SEER-Medicare database analysis. Hepatol Commun. 2020;4(10):1541–51.
Google Scholar
Barzi A, Zhou K, Wang S, Dodge JL, El-Khoueiry A, Setiawan VW. Etiology and outcomes of hepatocellular carcinoma in an ethnically diverse population: the multiethnic cohort. Cancers (Basel). 2021;13(14):3476.
Google Scholar
Tonidandel S, King EB, Cortina JM. Big data methods: leveraging modern data analytic techniques to build organizational science. Organ Res Methods. 2018;21(3):525–47.
Google Scholar
Hasan MM, Popp J, Oláh J. Current landscape and influence of big data on finance. J Big Data. 2020;7(1):1–17.
Google Scholar
Zhang L, Wang H, Li Q, Zhao MH, Zhan QM. Big data and medical research in China. BMJ. 2018;360:j5910.
Google Scholar
Alharthi A, Krotov V, Bowman M. Addressing barriers to big data. Bus Horizons. 2017;60(3):285–92.
Google Scholar
Yang J, Li Y, Liu Q, Li L, Feng A, Wang T, et al. Brief introduction of medical database and data mining technology in big data era. J Evid Based Med. 2020;13(1):57–69.
Google Scholar
Wu WT, Li YJ, Feng AZ, Li L, Huang T, Xu AD, et al. Data mining in clinical big data: the frequently used databases, steps, and methodological models. Mil Med Res. 2021;8(1):44.
Google Scholar
Boateng EY, Abaye DA. A review of the logistic regression model with emphasis on medical research. JDAIP. 2019;7(4):190–207.
Google Scholar
Sur P, Candès EJ. A modern maximum-likelihood theory for high-dimensional logistic regression. Proc Natl Acad Sci U S A. 2019;116(29):14516–25.
Google Scholar
Norton EC, Dowd BE, Maciejewski ML. Odds ratios-current best practice and use. JAMA. 2018;320(1):84–5.
Google Scholar
Shipe ME, Deppen SA, Farjah F, Grogan EL. Developing prediction models for clinical use using logistic regression: an overview. J Thorac Dis. 2019;11(Suppl 4):S574–84.
Google Scholar
Che W, Wang Y, Wang X, Lyu J. Association between age and the presence and mortality of breast cancer synchronous brain metastases in the United States: a neglected SEER analysis. Front Public Health. 2022;10:1000415.
Google Scholar
Lorimer PD, Motz BM, Watson M, Trufan SJ, Prabhu RS, Hill JS, et al. Enteral feeding access has an impact on outcomes for patients with esophageal cancer undergoing esophagectomy: an analysis of SEER-Medicare. Ann Surg Oncol. 2019;26:1311–9.
Google Scholar
Bartek J Jr, Dhawan S, Thurin E, Alattar A, Gulati S, Rydenhag B, et al. Short-term outcome following surgery for rare brain tumor entities in adults: a Swedish nation-wide registry-based study and comparison with SEER database. J Neurooncol. 2020;148(2):281–90.
Google Scholar
Chang W, Wei Y, Ren L, Jian M, Chen Y, Chen J, et al. Short-term and long-term outcomes of robotic rectal surgery-from the real word data of 1145 consecutive cases in China. Surg Endosc. 2020;34(9):4079–88.
Google Scholar
Hankinson TC, Dudley RWR, Torok MR, Patibandla MR, Dorris K, Poonia S, et al. Short-term mortality following surgical procedures for the diagnosis of pediatric brain tumors: outcome analysis in 5533 children from SEER, 2004–2011. J Neurosurg Pediatr. 2016;17(3):289–97.
Google Scholar
Wu C, Zhao Y, Zhang Y, Yang Y, Su W, Yang Y, et al. Gut microbiota specifically mediates the anti-hypercholesterolemic effect of berberine (BBR) and facilitates to predict BBR’s cholesterol-decreasing efficacy in patients. J Adv Res. 2022;37:197–208.
Google Scholar
Hyder O, Dodson RM, Sachs T, Weiss M, Mayo SC, Choti MA, et al. Impact of adjuvant external beam radiotherapy on survival in surgically resected gallbladder adenocarcinoma: a propensity score-matched Surveillance, Epidemiology, and End Results analysis. Surgery. 2014;155(1):85–93.
Google Scholar
Che W, Wang Y, Wang X, Lyu J. Midlife brain metastases in the United States: Is male at risk? Cancer Med. 2022;11(4):1202–16.
Google Scholar
Coffman DL, Zhou J, Cai X. Comparison of methods for handling covariate missingness in propensity score estimation with a binary exposure. BMC Med Res Methodol. 2020;20(1):168.
Google Scholar
Cox DR. Regression models and life-tables. J R Stat Soc B. 1972;34(2):187–202.
Moolgavkar SH, Chang ET, Watson HN, Lau EC. An assessment of the Cox proportional hazards regression model for epidemiologic studies. Risk Anal. 2018;38(4):777–94.
Google Scholar
He QL, Gao SW, Qin Y, Huang RC, Chen CY, Zhou F, et al. Gastrointestinal dysfunction is associated with mortality in severe burn patients: a 10-year retrospective observational study from South China. Mil Med Res. 2022;9(1):49.
Google Scholar
Kalbfleisch JD, Schaubel DE. Fifty years of the cox model. Annu Rev Stat Appl. 2023;10:1–23.
Google Scholar
Martin AM, Cagney DN, Catalano PJ, Warren LE, Bellon JR, Punglia RS, et al. Brain metastases in newly diagnosed breast cancer: a population-based study. JAMA Oncol. 2017;3(8):1069–77.
Google Scholar
Pausch TM, Liu X, Cui J, Wei J, Miao Y, Heger U, et al. Survival benefit of resection surgery for pancreatic ductal adenocarcinoma with liver metastases: a propensity score-matched SEER database analysis. Cancers (Basel). 2022;14(1):57.
Google Scholar
Bhanvadia RR, Rodriguez J 3rd, Bagrodia A, Eggener SE. Lymph node count impacts survival following post-chemotherapy retroperitoneal lymphadenectomy for non-seminomatous testicular cancer: a population-based analysis. BJU Int. 2019;124(5):792–800.
Google Scholar
Che W, Ma W, Lyu J, Wang X. Socioeconomic status and adult gliomas mortality risk: an observational study based on SEER data. World Neurosurg. 2021;155:e131–41.
Google Scholar
Saraswathula A, Megwalu UC. Insurance status and survival of patients with salivary gland cancer. Otolaryngol Head Neck Surg. 2018;159(6):998–1005.
Google Scholar
Zhang SL, Wang WR, Liu ZJ, Wang ZM. Marital status and survival in patients with soft tissue sarcoma: a population-based, propensity-matched study. Cancer Med. 2019;8(2):465–79.
Google Scholar
Zabor EC, Radivoyevitch T, Singh AD, Kilic E, de Klein JEMM, Kalirai H, et al. Conditional survival in uveal melanoma. Ophthalmol Retina. 2021;5(6):536–42.
Google Scholar
Kitajima K, Igeta M, Kuyama J, Kawahara T, Suga T, Otani T, et al. Novel nomogram developed for determining suitability of metastatic castration-resistant prostate cancer patients to receive maximum benefit from radium-223 dichloride treatment-Japanese Ra-223 Therapy in Prostate Cancer using Bone Scan Index (J-RAP-BSI) Trial. Eur J Nucl Med Mol Imaging. 2023;50(5):1487–98.
Google Scholar
Wolbers M, Koller MT, Witteman JCM, Steyerberg EW. Prognostic models with competing risks: methods and application to coronary risk prediction. Epidemiology. 2009;20(4):555–61.
Google Scholar
Latouche A, Allignol A, Beyersmann J, Labopin M, Fine JP. A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions. J Clin Epidemiol. 2013;66(6):648–53.
Google Scholar
Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94(446):496–509.
Google Scholar
Li Y, Sun L, Burstein DS, Getz KD. Considerations of competing risks analysis in cardio-oncology studies: JACC: cardiooncology state-of-the-art review. JACC CardioOncol. 2022;4(3):287–301.
Google Scholar
Li X, Liu Z, Ye Z, Gou S, Wang C. Impact of age on survival of patients with pancreatic cancer after surgery: analysis of SEER data. Pancreatology. 2018;18(1):133–8.
Google Scholar
Gooley TA, Leisenring W, Crowley J, Storer BE. Estimation of failure probabilities in the presence of competing risks: new representations of old estimators. Stat Med. 1999;18(6):695–706.
Google Scholar
Yang J, Pan Z, He Y, Zhao F, Feng X, Liu Q, et al. Competing-risks model for predicting the prognosis of penile cancer based on the SEER database. Cancer Med. 2019;8(18):7881–9.
Google Scholar
Wold S. Spline functions in data analysis. Technometrics. 1974;16(1):1–11.
Google Scholar
Stone CJ, Koo CY. Additive splines in statistics. In: Proceedings of the American Statistical Association. Washington DC; 1985. p. 45–8.
Herndon JE 2nd, Harrell FE Jr. The restricted cubic spline hazard model. Commun Stat-Theor M. 1990;19(2):639–63.
Google Scholar
Frome EL, Kutner MH, Beauchamp JJ. Regression analysis of poisson-distributed data. J Am Stat Assoc. 1973;68(344):935–40.
Google Scholar
Frome EL, Checkoway H. Use of poisson regression models in estimating incidence rates and ratios. Am J Epidemiol. 1985;121(2):309–23.
Google Scholar
Tsikitis VL, Wertheim BC, Guerrero MA. Trends of incidence and survival of gastrointestinal neuroendocrine tumors in the United States: a seer analysis. J Cancer. 2012;3:292.
Google Scholar
Muskens IS, Feng Q, Francis SS, Walsh KM, Mckean-Cowdin R, Gauderman WJ, et al. Pediatric glioma and medulloblastoma risk and population demographics: a Poisson regression analysis. Neurooncol Adv. 2020;2(1):vdaa089.
Google Scholar
Walker JP, Johnson JS, Eguchi MM, Saltzman AF, Cockburn M, Cost NG. Factors affecting lymph node sampling patterns and the impact on survival of lymph node density in patients with Wilms tumor: a Surveillance, Epidemiology, and End Result (SEER) database review. J Pediatr Urol. 2020;16(1):81–8.
Google Scholar
Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26(8):1364–70.
Google Scholar
Bandini M, Marchioni M, Pompe RS, Tian Z, Gandaglia G, Fossati N, et al. First North American validation and head-to-head comparison of four preoperative nomograms for prediction of lymph node invasion before radical prostatectomy. BJU Int. 2018;121(4):592–9.
Google Scholar
Pan X, Yang W, Chen Y, Tong L, Li C, Li H. Nomogram for predicting the overall survival of patients with inflammatory breast cancer: a SEER-based study. Breast. 2019;47:56–61.
Google Scholar
Wu SL, Gai JD, Yu XM, Mao X, Jin F. A novel nomogram and risk classification system for predicting lymph node metastasis of breast mucinous carcinoma: a SEER-based study. Cancer Med. 2022;11(24):4767–83.
Google Scholar
Kutikov A, Egleston BL, Wong YN, Uzzo RG. Evaluating overall survival and competing risks of death in patients with localized renal cell carcinoma using a comprehensive nomogram. J Clin Oncol. 2010;28(2):311–7.
Google Scholar
Yan B, Su BB, Bai DS, Qian JJ, Zhang C, Jin SJ, et al. A practical nomogram and risk stratification system predicting the cancer-specific survival for patients with early hepatocellular carcinoma. Cancer Med. 2021;10(2):496–506.
Google Scholar
Wang Y, Zheng Q, Jia B, An T, Zhao J, Wu M, et al. Effects of surgery on survival of early-stage patients with SCLC: propensity score analysis and nomogram construction in SEER database. Front Oncol. 2020;10:626.
Google Scholar
Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc B. 1996;58(1):267–88.
Zhao P, Yu B. On model selection consistency of Lasso. J Mach Learn Res. 2006;7:2541–63.
Yang Z, Shi G, Zhang P. Development and validation of nomograms to predict overall survival and cancer-specific survival in patients with pancreatic adenosquamous carcinoma. Front Oncol. 2022;12: 831649.
Google Scholar
Peng HT, Siddiqui MM, Rhind SG, Zhang J, da Luz LT, Beckett A. Artificial intelligence and machine learning for hemorrhagic trauma care. Mil Med Res. 2023;10(1):6.
Google Scholar
Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med. 2023;388(13):1201–8.
Google Scholar
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.
Google Scholar
Yu H, Huang T, Feng B, Lyu J. Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a Surveillance, Epidemiology, and End Results analysis. BMC Cancer. 2022;22(1):210.
Google Scholar
Senders JT, Staples P, Mehrtash A, Cote DJ, Taphoorn MJB, Reardon DA, et al. An online calculator for the prediction of survival in glioblastoma patients using classical statistics and machine learning. Neurosurgery. 2020;86(2):E184–92.
Google Scholar
Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19(3):335–51.
Google Scholar
Lim H, Devesa SS, Sosa JA, Check D, Kitahara CM. Trends in thyroid cancer incidence and mortality in the United States, 1974–2013. JAMA. 2017;317(13):1338–48.
Google Scholar
Guo F, Kuo YF, Shih YCT, Giordano SH, Berenson AB. Trends in breast cancer mortality by stage at diagnosis among young women in the United States. Cancer. 2018;124(17):3500–9.
Google Scholar
Lin D, Wang M, Chen Y, Gong J, Chen L, Shi X, et al. Trends in Intracranial Glioma Incidence and Mortality in the United States, 1975–2018. Front Oncol. 2021;11:748061.
Google Scholar
Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41–55.
Google Scholar
Staffa SJ, Zurakowski D. Five steps to successfully implement and evaluate propensity score matching in clinical research studies. Anesth Analg. 2018;127(4):1066–73.
Google Scholar
Simoneau G, Pellegrini F, Debray TP, Rouette J, Muñoz J, Platt RW, et al. Recommendations for the use of propensity score methods in multiple sclerosis research. Mult Scler. 2022;28(9):1467–80.
Google Scholar
Thomas L, Li F, Pencina M. Using propensity score methods to create target populations in observational clinical research. JAMA. 2020;323(5):466–7.
Google Scholar
Qi L, Wan L, Ren X, Zhang W, Tu C, Li Z. The role of chemotherapy in extraskeletal osteosarcoma: a propensity score analysis of the surveillance epidemiology and end results (SEER) database. Med Sci Monit. 2020;26:e925107.
Google Scholar
Lim YJ, Song C, Kim JS. Improved survival with postoperative radiotherapy in thymic carcinoma: a propensity-matched analysis of Surveillance, Epidemiology, and End Results (SEER) database. Lung Cancer. 2017;108:161–7.
Google Scholar
Liu Z, Zeng W, Huang L, Wang Z, Wang M, Zhou L, et al. Prognosis of FTC compared to PTC and FVPTC: findings based on SEER database using propensity score matching analysis. Am J Cancer Res. 2018;8(8):1440–8.
Google Scholar
Lin SW, Anisa KN. Effects of socioeconomic status on cancer patient survival: counterfactual event-based mediation analysis. Cancer Causes Control. 2021;32(1):83–93.
Google Scholar
Leapman MS, Dinan M, Pasha S, Long J, Washington SL, Ma X, et al. Mediators of racial disparity in the use of prostate magnetic resonance imaging among patients with prostate cancer. JAMA Oncol. 2022;8(5):687–96.
Google Scholar
Jiang X, Yan M. Surgical treatment for improved 1-year survival in patients with primary cardiac sarcoma. Anatol J Cardiol. 2021;25(11):796–802.
Google Scholar
Liu X, Wang C, Feng Y, Shen C, He T, Wang Z, et al. The prognostic role of surgery and a nomogram to predict the survival of stage IV breast cancer patients. Gland Surg. 2022;11(7):1224–39.
Google Scholar
Knoble NB, Alderfer MA, Hossain MJ. Socioeconomic status (SES) and childhood acute myeloid leukemia (AML) mortality risk: analysis of SEER data. Cancer Epidemiol. 2016;44:101–8.
Google Scholar
Kaplan NM, Sproul LE, Mulcahy WS. Large prospective study of ramipril in patients with hypertension. CARE Investig Clin Ther. 1993;15(5):810–8.
Google Scholar
Sherman RE, Anderson SA, Dal Pan GJ, Gray GW, Gross T, Hunter NL, et al. Real-world evidence-what is it and what can it tell us. N Engl J Med. 2016;375(23):2293–7.
Google Scholar
Corrigan-Curay J, Sacks L, Woodcock J. Real-world evidence and real-world data for evaluating drug safety and effectiveness. JAMA. 2018;320(9):867–8.
Google Scholar
Fang Y, He W, Wang H, Wu M. Key considerations in the design of real-world studies. Contemp Clin Trials. 2020;96:106091.
Google Scholar
Yuan QM, Lin TH, Jin K, Qiu S, Zhou XH, Jin D, et al. The comparison of survival between active surveillance or watchful waiting and focal therapy for low-risk prostate cancer: a real-world study from the SEER database. Asian J Androl. 2022;24(3):305–10.
Google Scholar
Morgenstern H. Ecologic studies in epidemiology: concepts, principles, and methods. Annu Rev Public Health. 1995;16:61–81.
Google Scholar
Mackenbach JP. Public health epidemiology. J Epidemiol Community Health. 1995;49(4):333–4.
Google Scholar
Lai Y, Shi H, Wang Z, Feng Y, Bao Y, Li Y, et al. Incidence trends and disparities in Helicobacter pylori related malignancy among US adults, 2000–2019. Front Public Health. 2022;10:1056157.
Google Scholar
Che W, Liu J, Fu T, Wang X, Lyu J. Recent trends in synchronous brain metastasis incidence and mortality in the United States: ten-year multicenter experience. Curr Oncol. 2022;29(11):8374–89.
Google Scholar
Horn SR, Stoltzfus KC, Mackley HB, Lehrer EJ, Zhou S, Dandekar SC, et al. Long-term causes of death among pediatric patients with cancer. Cancer. 2020;126(13):3102–13.
Google Scholar
Monson RR. Analysis of relative survival and proportional mortality. Comput Biomed Res. 1974;7(4):325–32.
Google Scholar
Lu Z, Teng Y, Ning X, Wang H, Feng W, Ou C. Long-term risk of cardiovascular disease mortality among classic Hodgkin lymphoma survivors. Cancer. 2022;128(18):3330–9.
Google Scholar
Zaorsky NG, Churilla TM, Egleston BL, Fisher SG, Ridge JA, Horwitz EM, et al. Causes of death among cancer patients. Ann Oncol. 2017;28(2):400–7.
Google Scholar
Comstock GW. Cohort analysis: W.H. Frost’s contributions to the epidemiology of tuberculosis and chronic disease. Soz Praventivmed. 2001;46(1):7–12.
Google Scholar
Levin KA. Study design III: cross-sectional studies. Evid Based Dent. 2006;7(1):24–5.
Google Scholar
Wang X, Cheng Z. Cross-sectional studies: strengths, weaknesses, and recommendations. Chest. 2020;158(1S):S65–71.
Google Scholar
Dey T, Mukherjee A, Chakraborty S. A practical overview of case-control studies in clinical practice. Chest. 2020;158(1S):S57–64.
Google Scholar
Macki M, Air EL. Commentary: what is a case control study? Neurosurgery. 2019;85(2):E390–1.
Google Scholar
Kooistra B, Dijkman B, Einhorn TA, Bhandari M. How to design a good case series. J Bone Joint Surg Am. 2009;91(Suppl 3):21–6.
Google Scholar
Dekkers OM, Egger M, Altman DG, Vandenbroucke JP. Distinguishing case series from cohort studies. Ann Intern Med. 2012;156(1 Pt 1):37–40.
Google Scholar
Vuong HG, Nguyen TPX, Ngo HTT, Hassell L, Kakudo K. Malignant thyroid teratoma: an integrated analysis of case series/case reports. Endocr Relat Cancer. 2021;28(7):495–503.
Google Scholar
Thiese MS. Observational and interventional study design types; an overview. Biochem Med (Zagreb). 2014;24(2):199–210.
Google Scholar
Duggan MA, Anderson WF, Altekruse S, Penberthy L, Sherman ME. The surveillance, epidemiology and end results (SEER) program and pathology: towards strengthening the critical relationship. Am J Surg Pathol. 2016;40(12):e94–102.
Google Scholar