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Relative survival tables and an application

Year 2023, Volume: 16 Issue: 1, 1 - 25, 29.06.2023

Abstract

In survival analysis studies with long follow-up periods, cause of death information may not be available or there may be some lack of information. It may not be possible to directly predict the probability of survival due to the disease when the cause of death is unclear or the available information is unreliable. Therefore, the probability of survival due to the disease is sometimes evaluated by a measure of the relative probability of survival between a group diagnosed with the disease and the larger population. Creating life tables and obtaining survival probabilities have an important place in relative survival analysis. For the estimation of these probabilities Ederer I, Ederer II, Hakulinen and Pohar Perme methods are used. In this study, these methods were applied on the prostate cancer dataset in the literature which includes competing risks. Life tables containing relative survival probabilities for different age groups were obtained. Ederer I, Ederer II, Hakulinen methods were used for the relative survival and Pohar Perme method was used for the net survival and no significant difference was observed between the results obtained. In all methods, the five year relative survival for male patients with prostate cancer was 44% for patients were aged 44-59, 49% for patients were aged 60-74, and 36% for patients were aged 75 or over. Similarly, life tables were created using age standardization, no significant difference was observed in survival probabilities as a result of age standardization. In addition, crude death probabilities due to both prostate cancer and competing risks were obtained and interpreted.

References

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  • [2] Erhardt, C.L., 1958, What is the Cause of Death, The Journal of the American Medical Association, 13-168:2, 161-168.
  • [3] Spiegelman, M., Bellows, M.T., Erhardt, C.L., Keehn, R.J., Moriyama, I.M., Parkhurst, E. and Sellers, A.H., 1958, Problems in the Medical Certification of Causes of Death, American Journal of Public Health and the Nations Health, 48, 71-80.
  • [4] Ederer, F., Axtell, L.M. and Cutler S.J., 1961, The Relative Survival Rate: A Statistical Methodology, National Cancer Institute Monograph, 6, 101-102.
  • [5] Nelson, C.P., Lambert, P.C, Squire, I.B. and Jones, D.R., 2008, Relative Survival: What can Cardiovascular Disease Learn from Cancer?, European Heart Journal, 29:7, 941-947.
  • [6] Ederer, F. and Heise, H., 1959, Instructions to IBM 650 Programmers in Processing Survival Computations, Methodological Note 10. End Results Evaluation Section, National Cancer Institute.
  • [7] Hakulinen, T., 1977, On Long-term Relative Survival Rates, Journal of Chronic Diseases, 30:7, 431-443.
  • [8] Hakulinen, T., 1982, Cancer Survival Corrected for Heterogeneity in Patient Withdrawal, Biometrics, 38:4, 933-42.
  • [9] Hakulinen, T., Seppä, K. and Lambert, P.C., 2011, Choosing the Relative Survival Method for Cancer Survival Estimation, European Journal of Cancer, 47:14, 2202-2210.
  • [10] Hakulinen, T. and Tenkanen, L., 1987, Regression Analysis of Relative Survival Rates, Journal of the Royal Statistical Society Series C (Applied Statistics), 36:3, 309–317.
  • [11] Esteve, J., Benhamou, E., Croasdale, M. and Raymond L.,1990, Relative Survival and the Estimation of Net Survival: Elements for Further Discussion, Statistics in Medicine, 9, 529-538.
  • [12] Sasieni, P., 1996, Proportional Excess Hazards, Biometrika, 83:1, 127– 141.
  • [13] Dickman, P.W., Sloggett, A., Hills, M. and Hakulinen, T., 2004, Regression Models for Relative Survival, Statistics in Medicine, 23, 51-64.
  • [14] Nelson, C., Lambert, P.C., Squire, I.B., and Jones, D.R., 2007, Flexible Parametric Models for Relative Survival with Application in Coronary Heart Disease, Statistics in Medicine, 26:30, 5486–5498.
  • [15] Perme, M.P., Henderson, R. and Stare, J., 2009, An Approach to Estimation in Relative Survival Regression, Biostatistics, 10:1, 136–146
  • [16] Zahl, P.H. and Aalen, O.O., 1998, Adjusting and Comparing Survival Curves by Means of an Additive Risk Model, Lifetime Data Analysis, 4:2, 149–168.
  • [17] Aalen, O.O., Borgan, O. and Gjessing, H.K., 2008, Survival and Event History Analysis, Springer, New York.
  • [18] Cortese, G. and Scheike, T.H., 2008, Dynamic Regression Hazards Models for Relative Survival, Statistics in Medicine, 27:18, 3563–3548.
  • [19] Perme, M.P., Stare J. and Esteve J., 2012, On Estimation in Relative Survival, Biometrics, 68, 113-120.
  • [20] Pohar M., and Stare, J., 2006, Relative Survival Analysis in R, Computer Methods and Programs in Biomedicine, 81, 272–278.
  • [21] Giorgi, R., Payan, J. and Gouvernet, J., 2005, RSURV: A Function to Perform Relative Survival Analysis with S-PLUS or R, Computer Methods and Programs in Biomedicine, 78:2, 175-178.
  • [22] Dickman, P.W. and Coviello, E., 2015, Estimating and Modeling Relative Survival, The Stata Journal, 15:1, 186–215.
  • [23] Sasieni, P. and Brentnall, A.R., 2017, On Standardized Relative Survival, Biometrics, 73:2, 473-482.
  • [24] Hakulinen, T. and Dyba, T.A., 2007, Chapter 3 - Recent Developments in Relative Survival Analysis, Outcome Azzam F.G. Taktak, Anthony C. Fisher (Eds), Outcome Prediction in Cancer, Elsevier, 43-64.
  • [25] Percy, C., Stanek, E 3rd. and Gloeckler, L., 1981, Accuracy of Cancer Death Certificates and Its Effect on Cancer Mortality Statistics, American Journal of Public Health, 71:3, 242-250.
  • [26] Berkson, J. and Gage, R.P., 1950, Calculation of Survival Rates for Cancer, Proceedings of the Staff Meetings of the Mayo Clinic, 24-25:11, 270-286.
  • [27] Sandin, F., 2008, Analyzing and Modeling the Relative Survival Rate of Patients Diagnosed with Malignant Melanoma, Master Thesis, Uppsala University, Sweden.
  • [28] Klein, J.P. and Moeschberger, M.L., 2003, Survival Analysis: Techniques for Censored and Truncated Data Second Edition, Springer, New York.
  • [29] Pokhrel, A. and Hakulinen, T., 2008, How to Interpret the Relative Survival Ratios of Cancer Patients. European Journal of Cancer, 44, 2661–2667.
  • [30] Hakama, M. and Hakulinen, T., 1977, Estimating the Expectation of Life in Cancer Survival Studies with Incomplete Follow-up Information, Journal of Chronic Diseases, 30:9, 585-597.
  • [31] Seppä, K., Hakulinen, T. and Pokhrel, A., 2015, Choosing the Net Survival Method for Cancer Survival Estimation, European Journal of Cancer, 51:9, 1123-1129.
  • [32] Pokhrel, A., 2007, Age Standardization of Relative Survival Ratios for Cancer Patients, Doctoral Thesis, Acta Universitatis Tamperensis 1278, Tampere.
  • [33] Dickman, P.W., Hakulinen, T., Luostarinen, T., Pukkala, E., Sankila, R., Söderman, B. and Teppo, L., 1999, Survival of Cancer Patients in Finland 1955-1994, Acta Oncologica, 38:12, 1-103.
  • [34] Bailar, J.C. III, 1964, Survival of Patients with Cancer of the Uterine Cervix and Corpus, Cutler S. (Ed.), National Cancer Institute, Bethesda MD.
  • [35] Black, R.J. and Bashir, S.A., 1998, World Standard Cancer Patient Populations: A Resource for Comparative Analysis of Survival Data, IARC Scientific Publications, 145, 9-11.
  • [36] Corazziari, I., Quinn, M. and Capocaccia. R., 2004, Standard Cancer Patient Population for Age Standardising Survival Ratios, European Journal of Cancer, 40:15, 2307-2316.
  • [37] Brenner, H. and Hakulinen, T., 2003, On Crude and Age-adjusted Relative Survival Rates, Journal of Clinical Epidemiology, 56:12, 1185-1191.
  • [38] Brenner, H., Arndt, V., Gefeller, O. and Hakulinen, T., 2004, An Alternative Approach to Age Adjustment of Cancer Survival Rates, European Journal of Cancer, 40:15, 2317-2322.
  • [39] Dickman, P., Kişisel Web Sayfası, https://www.pauldickman.com/, Son Erişim Tarihi: 26.04.2022.
  • [40] Brenner, H. and Hakulinen, T., 2005, Age Adjustment of Cancer Survival Rates: Methods, Point Estimates and Standard Errors, British Journal of Cancer, 93, 372–375.
  • [41] The Human Mortality Database, https://www.mortality.org/, Son Erişim Tarihi: 12.05.2022.
  • [42] Byar, D.P. and Green, S.B., 1980, The Choice of Treatment for Cancer Patients Based on Covariate Information, Bulletin du Cancer, 67:4, 477-90.
  • [43] Vanderbilt Biostatistics Datasets, https://hbiostat.org/data/, Son Erişim Tarihi: 26.04.2022.
  • [44] Stata/BE 6-month for students, https://www.stata.com/order/, Sürüm Başlangıç Tarihi: 28.12.2021.
  • [45] Cronin, K.A. and Feuer, E.J., 2000, Cumulative Cause-specific Mortality for Cancer Patients in the Presence of Other Causes: A Crude Analogue of Relative Survival, Statistics in Medicine, 19:13, 1729-1740.
  • [46] Orrason, A.W., Garmo, H., Styrke, J., Dickman, P.W. and Stattin, P., 2021, Comparison of Relative Survival and Cause-Specific Survival in Men with Prostate Cancer According to Age and Risk Category: A Nationwide, Population-Based Study, American Journal of Epidemiology, 190:10, 2053–2063.
  • [47] Lambert, P.C., Dickman, P.W. and Rutherford, M.J., 2015, Comparison of Different Approaches to Estimating Age Standardized Net Survival, BMC Medical Research Methodology, 15:64, 1-13.
  • [48] Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre L.A. and Jemal, A., 2018, Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries, Cancer Journal for Clinicians, 68:6, 394–424.
  • [49] Fairley, L., Forman, D., West, R. and Manda, S., 2018, Spatial Variation in Prostate Cancer Survival in the Northern and Yorkshire Region of England Using Bayesian Relative Survival Smoothing, British Journal of Cancer, 99, 1786–1793.
  • [50] Stanford, J.L., Stephenson, R.A., Coyle L.M., Cerhan, J., Correa, R., Eley, J.W., Gilliland, F., Hankey, B., Kolonel, L.N., Kosary, C., Ross, R., Severson, R. and West D., 1999, Prostate Cancer Trends 1973–1995. SEER Program, National Cancer Institute, Bethesda, MD: National Institutes of Health Publ. No. 99–4543.
  • [51] Merrill, R.M. and Bird, J.S., 2002, Effect of Young Age on Prostate Cancer Survival: A Population-based Assessment (United States), Cancer Causes and Control, 13, 435–443.

Göreli yaşam tabloları ve bir uygulama

Year 2023, Volume: 16 Issue: 1, 1 - 25, 29.06.2023

Abstract

Uzun takip süreleri olan gözlemsel yaşam çözümlemesi çalışmalarında ölüm nedeni bilgisi bulunmayabilir veya bazı bilgi eksiklikleri olabilir. Yaşam çözümlemesinde, ölüm nedeni belli olmadığında veya eldeki bilgi güvenilir olmadığında hastalığa bağlı yaşam olasılığını doğrudan tahmin etmek mümkün olmayabilir. Bu nedenle hastalığa bağlı yaşam olasılığı, bazen hastalık teşhisi koyulmuş bir grup ile daha geniş bir kitle arasındaki göreli yaşam olasılığının bir ölçüsü ile değerlendirilir. Böyle durumlarda göreli yaşam çözümlemesi yöntemlerinin kullanılması gerekmektedir. Yalnızca hasta takip zamanlarının ve takip sonundaki yaşamsal durumun kaydedildiği kanser araştırmaları, göreli yaşam çözümlemesinin sıklıkla kullanıldığı bir alandır.

Göreli yaşam çözümlemesinde yaşam tablolarının oluşturulması ve yaşam olasılıklarının elde edilmesi önemli bir yere sahiptir. Bu olasılıkların tahmini için Ederer I, Ederer II, Hakulinen ve Pohar Perme yöntemleri kullanılmaktadır. Bu yöntemler, literatürde yer alan ve yarışan riskleri içeren prostat kanseri veri kümesi üzerinde uygulanmıştır. Farklı yaş grupları için göreli yaşam olasılıklarını içeren yaşam tabloları elde edilmiştir. Göreli yaşam olasılığı için Ederer I, Ederer II, Hakulinen ve net yaşam olasılığı için Pohar Perme yöntemleri kullanılmış, elde edilen sonuçlar arasında belirgin bir farklılık görülmemiştir. Tüm yöntemlerde prostat kanserine yakalanmış erkek hastalara ait beş yıllık göreli yaşam olasılığı 44-59 yaş aralığındaki hastalar için %44, 60-74 yaş aralığındaki hastalar için %49 ve 75 yaş ve üzeri hastalar için %36 olarak elde edilmiştir. Benzer biçimde yaş standartlaştırma kullanılarak da yaşam tabloları oluşturulmuş, yaş standartlaştırması sonucunda yaşam olasılıklarında belirgin bir değişiklik gözlenmemiştir. Ayrıca hem prostat kanserine bağlı hem yarışan risklere bağlı kaba ölüm olasılıkları elde edilmiş ve yorumlanmıştır.

References

  • [1] Akyol Cengiz, S., Göreli Yaşam Çözümlemesi, Yüksek Lisans Tezi, Hacettepe Üniversitesi Fen Bilimleri Enstitüsü, Ankara, 2022.
  • [2] Erhardt, C.L., 1958, What is the Cause of Death, The Journal of the American Medical Association, 13-168:2, 161-168.
  • [3] Spiegelman, M., Bellows, M.T., Erhardt, C.L., Keehn, R.J., Moriyama, I.M., Parkhurst, E. and Sellers, A.H., 1958, Problems in the Medical Certification of Causes of Death, American Journal of Public Health and the Nations Health, 48, 71-80.
  • [4] Ederer, F., Axtell, L.M. and Cutler S.J., 1961, The Relative Survival Rate: A Statistical Methodology, National Cancer Institute Monograph, 6, 101-102.
  • [5] Nelson, C.P., Lambert, P.C, Squire, I.B. and Jones, D.R., 2008, Relative Survival: What can Cardiovascular Disease Learn from Cancer?, European Heart Journal, 29:7, 941-947.
  • [6] Ederer, F. and Heise, H., 1959, Instructions to IBM 650 Programmers in Processing Survival Computations, Methodological Note 10. End Results Evaluation Section, National Cancer Institute.
  • [7] Hakulinen, T., 1977, On Long-term Relative Survival Rates, Journal of Chronic Diseases, 30:7, 431-443.
  • [8] Hakulinen, T., 1982, Cancer Survival Corrected for Heterogeneity in Patient Withdrawal, Biometrics, 38:4, 933-42.
  • [9] Hakulinen, T., Seppä, K. and Lambert, P.C., 2011, Choosing the Relative Survival Method for Cancer Survival Estimation, European Journal of Cancer, 47:14, 2202-2210.
  • [10] Hakulinen, T. and Tenkanen, L., 1987, Regression Analysis of Relative Survival Rates, Journal of the Royal Statistical Society Series C (Applied Statistics), 36:3, 309–317.
  • [11] Esteve, J., Benhamou, E., Croasdale, M. and Raymond L.,1990, Relative Survival and the Estimation of Net Survival: Elements for Further Discussion, Statistics in Medicine, 9, 529-538.
  • [12] Sasieni, P., 1996, Proportional Excess Hazards, Biometrika, 83:1, 127– 141.
  • [13] Dickman, P.W., Sloggett, A., Hills, M. and Hakulinen, T., 2004, Regression Models for Relative Survival, Statistics in Medicine, 23, 51-64.
  • [14] Nelson, C., Lambert, P.C., Squire, I.B., and Jones, D.R., 2007, Flexible Parametric Models for Relative Survival with Application in Coronary Heart Disease, Statistics in Medicine, 26:30, 5486–5498.
  • [15] Perme, M.P., Henderson, R. and Stare, J., 2009, An Approach to Estimation in Relative Survival Regression, Biostatistics, 10:1, 136–146
  • [16] Zahl, P.H. and Aalen, O.O., 1998, Adjusting and Comparing Survival Curves by Means of an Additive Risk Model, Lifetime Data Analysis, 4:2, 149–168.
  • [17] Aalen, O.O., Borgan, O. and Gjessing, H.K., 2008, Survival and Event History Analysis, Springer, New York.
  • [18] Cortese, G. and Scheike, T.H., 2008, Dynamic Regression Hazards Models for Relative Survival, Statistics in Medicine, 27:18, 3563–3548.
  • [19] Perme, M.P., Stare J. and Esteve J., 2012, On Estimation in Relative Survival, Biometrics, 68, 113-120.
  • [20] Pohar M., and Stare, J., 2006, Relative Survival Analysis in R, Computer Methods and Programs in Biomedicine, 81, 272–278.
  • [21] Giorgi, R., Payan, J. and Gouvernet, J., 2005, RSURV: A Function to Perform Relative Survival Analysis with S-PLUS or R, Computer Methods and Programs in Biomedicine, 78:2, 175-178.
  • [22] Dickman, P.W. and Coviello, E., 2015, Estimating and Modeling Relative Survival, The Stata Journal, 15:1, 186–215.
  • [23] Sasieni, P. and Brentnall, A.R., 2017, On Standardized Relative Survival, Biometrics, 73:2, 473-482.
  • [24] Hakulinen, T. and Dyba, T.A., 2007, Chapter 3 - Recent Developments in Relative Survival Analysis, Outcome Azzam F.G. Taktak, Anthony C. Fisher (Eds), Outcome Prediction in Cancer, Elsevier, 43-64.
  • [25] Percy, C., Stanek, E 3rd. and Gloeckler, L., 1981, Accuracy of Cancer Death Certificates and Its Effect on Cancer Mortality Statistics, American Journal of Public Health, 71:3, 242-250.
  • [26] Berkson, J. and Gage, R.P., 1950, Calculation of Survival Rates for Cancer, Proceedings of the Staff Meetings of the Mayo Clinic, 24-25:11, 270-286.
  • [27] Sandin, F., 2008, Analyzing and Modeling the Relative Survival Rate of Patients Diagnosed with Malignant Melanoma, Master Thesis, Uppsala University, Sweden.
  • [28] Klein, J.P. and Moeschberger, M.L., 2003, Survival Analysis: Techniques for Censored and Truncated Data Second Edition, Springer, New York.
  • [29] Pokhrel, A. and Hakulinen, T., 2008, How to Interpret the Relative Survival Ratios of Cancer Patients. European Journal of Cancer, 44, 2661–2667.
  • [30] Hakama, M. and Hakulinen, T., 1977, Estimating the Expectation of Life in Cancer Survival Studies with Incomplete Follow-up Information, Journal of Chronic Diseases, 30:9, 585-597.
  • [31] Seppä, K., Hakulinen, T. and Pokhrel, A., 2015, Choosing the Net Survival Method for Cancer Survival Estimation, European Journal of Cancer, 51:9, 1123-1129.
  • [32] Pokhrel, A., 2007, Age Standardization of Relative Survival Ratios for Cancer Patients, Doctoral Thesis, Acta Universitatis Tamperensis 1278, Tampere.
  • [33] Dickman, P.W., Hakulinen, T., Luostarinen, T., Pukkala, E., Sankila, R., Söderman, B. and Teppo, L., 1999, Survival of Cancer Patients in Finland 1955-1994, Acta Oncologica, 38:12, 1-103.
  • [34] Bailar, J.C. III, 1964, Survival of Patients with Cancer of the Uterine Cervix and Corpus, Cutler S. (Ed.), National Cancer Institute, Bethesda MD.
  • [35] Black, R.J. and Bashir, S.A., 1998, World Standard Cancer Patient Populations: A Resource for Comparative Analysis of Survival Data, IARC Scientific Publications, 145, 9-11.
  • [36] Corazziari, I., Quinn, M. and Capocaccia. R., 2004, Standard Cancer Patient Population for Age Standardising Survival Ratios, European Journal of Cancer, 40:15, 2307-2316.
  • [37] Brenner, H. and Hakulinen, T., 2003, On Crude and Age-adjusted Relative Survival Rates, Journal of Clinical Epidemiology, 56:12, 1185-1191.
  • [38] Brenner, H., Arndt, V., Gefeller, O. and Hakulinen, T., 2004, An Alternative Approach to Age Adjustment of Cancer Survival Rates, European Journal of Cancer, 40:15, 2317-2322.
  • [39] Dickman, P., Kişisel Web Sayfası, https://www.pauldickman.com/, Son Erişim Tarihi: 26.04.2022.
  • [40] Brenner, H. and Hakulinen, T., 2005, Age Adjustment of Cancer Survival Rates: Methods, Point Estimates and Standard Errors, British Journal of Cancer, 93, 372–375.
  • [41] The Human Mortality Database, https://www.mortality.org/, Son Erişim Tarihi: 12.05.2022.
  • [42] Byar, D.P. and Green, S.B., 1980, The Choice of Treatment for Cancer Patients Based on Covariate Information, Bulletin du Cancer, 67:4, 477-90.
  • [43] Vanderbilt Biostatistics Datasets, https://hbiostat.org/data/, Son Erişim Tarihi: 26.04.2022.
  • [44] Stata/BE 6-month for students, https://www.stata.com/order/, Sürüm Başlangıç Tarihi: 28.12.2021.
  • [45] Cronin, K.A. and Feuer, E.J., 2000, Cumulative Cause-specific Mortality for Cancer Patients in the Presence of Other Causes: A Crude Analogue of Relative Survival, Statistics in Medicine, 19:13, 1729-1740.
  • [46] Orrason, A.W., Garmo, H., Styrke, J., Dickman, P.W. and Stattin, P., 2021, Comparison of Relative Survival and Cause-Specific Survival in Men with Prostate Cancer According to Age and Risk Category: A Nationwide, Population-Based Study, American Journal of Epidemiology, 190:10, 2053–2063.
  • [47] Lambert, P.C., Dickman, P.W. and Rutherford, M.J., 2015, Comparison of Different Approaches to Estimating Age Standardized Net Survival, BMC Medical Research Methodology, 15:64, 1-13.
  • [48] Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre L.A. and Jemal, A., 2018, Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries, Cancer Journal for Clinicians, 68:6, 394–424.
  • [49] Fairley, L., Forman, D., West, R. and Manda, S., 2018, Spatial Variation in Prostate Cancer Survival in the Northern and Yorkshire Region of England Using Bayesian Relative Survival Smoothing, British Journal of Cancer, 99, 1786–1793.
  • [50] Stanford, J.L., Stephenson, R.A., Coyle L.M., Cerhan, J., Correa, R., Eley, J.W., Gilliland, F., Hankey, B., Kolonel, L.N., Kosary, C., Ross, R., Severson, R. and West D., 1999, Prostate Cancer Trends 1973–1995. SEER Program, National Cancer Institute, Bethesda, MD: National Institutes of Health Publ. No. 99–4543.
  • [51] Merrill, R.M. and Bird, J.S., 2002, Effect of Young Age on Prostate Cancer Survival: A Population-based Assessment (United States), Cancer Causes and Control, 13, 435–443.
There are 51 citations in total.

Details

Primary Language Turkish
Subjects Statistics, Risk Analysis
Journal Section Articles
Authors

Sema Akyol 0000-0003-3360-5164

Nihal Ata Tutkun 0000-0001-5204-680X

Early Pub Date June 27, 2023
Publication Date June 29, 2023
Published in Issue Year 2023 Volume: 16 Issue: 1

Cite

IEEE S. Akyol and N. Ata Tutkun, “Göreli yaşam tabloları ve bir uygulama”, JSSA, vol. 16, no. 1, pp. 1–25, 2023.