A comparison of the Polytomous logistic regression and joint Cox proportional hazards models for evaluating multiple disease subtypes in prospective cohort studies

Cancer Epidemiology Biomarkers and Prevention

Volumn 22, Issue 2, 2013, Pages 275-285

  Xue, Xiaonan ^a   Kim, Mimi Y ^a   Gaudet, Mia M ^b   Park, Yikyung ^c   Heo, Moonseong ^a   Hollenbeck, Albert R ^d   Strickler, Howard D ^a   Gunter, Marc J ^e  

^a ALBERT EINSTEIN COLLEGE OF MEDICINE   (United States)

^b AMERICAN CANCER SOCIETY   (United States)

^c NATIONAL CANCER INSTITUTE   (United States)

^d AARP   (United States)

^e IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ESTROGEN; PROGESTERONE RECEPTOR;

ADULT; AGED; ARTICLE; BODY SIZE; BREAST CANCER; DATA ANALYSIS; DISEASE ASSOCIATION; EXPOSURE VARIABLE; HAZARD RATIO; HUMAN; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; POLYTOMOUS LOGISTIC REGRESSION MODEL; PRIORITY JOURNAL; PROPORTIONAL HAZARDS MODEL; SIMULATION; STATISTICAL PARAMETERS; SYSTEMATIC ERROR;

BODY MASS INDEX; BREAST NEOPLASMS; COMPUTER SIMULATION; FEMALE; FOLLOW-UP STUDIES; HUMANS; LOGISTIC MODELS; NEOPLASM STAGING; OBESITY; PROGNOSIS; PROPORTIONAL HAZARDS MODELS; PROSPECTIVE STUDIES; RECEPTORS, ESTROGEN; RECEPTORS, PROGESTERONE; RISK FACTORS;

EID: 84873394888     PISSN: 10559965     EISSN: None     Source Type: Journal    
DOI: 10.1158/1055-9965.EPI-12-1050     Document Type: Article

References (24)

1. Schroeder JC, Conway K, Li Y, Mistry K, Bell DA, Taylor JA. P53 mutations in bladder cancer: evidence for exogenous versus endogenous risk factors. Cancer Res 2003;63:7530-8.
2. Hosmer DW, Lemeshow S. Applied logistic regression. 2nd ed. New York, NY: John Wiley & Sons, Inc; 2000, Chapter 8, p. 273.
3. Rosenblatt K, Daling JR, Chen C, Sherman KJ, Schwartz S. Marijuana use and risk of oral squamous cell carcinoma. Cancer Res 2004;64:4049-54.
4. Kelemen LE, Cerhan JR, Lim U, Davis S, Cozen W, Schenk M, et al. Vegetables, fruit and antioxidant-related nutrients and risk of non-Hodgkin lymphoma: a National Cancer Institute-Surveillance, Epidemiology, and End Results population-based case-control study. Am J Clin Nutr 2006;83:1401-10.
5. Agalliu I, Gern R, Leanza S, Burk RD. Associations of high-grade prostate cancer with BRCA1 and BRCA2 founder mutations. Clin Cancer Res 2009;15:1112-20.
6. Blackmore KM, Lesosky M, Barnett H, Raboud JM, Vieth R, Knight JA. Vitamin D from dietary intake and sunlight exposure and the risk of hormone-receptor-defined breast cancer. Am J Epidemiol 2008;168:915-24.
7. Morris AP, Lindgren CM, Zeggini E, Timpson NJ, Frayling TM, Hattersley AT, et al. A powerful approach to sub-phenotype analysis in population-based genetic association studies. Genet Epidemiol 2010;34:335-43.
8. Potter J, Cerhan J, Sellers TA, McGovern PG, Drinkard C, Kushi LR, et al. Progesterone and estrogen receptors and mammary neoplasia in the Iowa Women's Health Study: How many kinds of breast cancer are there? Cancer Epidemiol Biomarkers Prev 1995;4:319-26.
9. Gapstur SM, Morrow M, Sellers TA. Hormone replacement therapy and risk of breast cancer with a favorable histology: results of the Iowa Women's Health Study. JAMA 1999;281:2091-7.
10. Ziv E, Tice J, Smith-Bindman R, Shepherd J, Cummings S, Kerlikowske K. Mammographic density and estrogen receptor status of breast cancer. Cancer Epidemiol Biomarkers Prev 2004;13:2090-5.
11. MacInnis RJ, English DR, Gertig DM, Hopper JL, Giles GG. Body size and composition and prostate cancer risk. Cancer Epidemiol Biomarkers Prev 2003;12:1417-21.
12. Cui Y, Shikany JM, Liu S, Shagufta Y, Rohan T. Selected antioxidants and risk of hormone receptor-defined invasive breast cancers among postmenopausal women in the Women's Health Initiative Observational Study. Am J Clin Nutr 2008;87:1009-18.
13. Kenfield SA, Wei EK, Stampfer MJ, Rosner RA, Colditz GA. Comparison of aspects of smoking among the four histological types of lung cancer. Tobacco Control 2008;17:198-204.
14. Lunn M, McNeil D. Applying Cox regression to competing risk. Biometrics 1995;51:524-32.
15. Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: Competing risks and multi-state models. Statist Med 2007;26:2389-430.
16. Glynn RJ, Rosner B. Comparison of risk factors for the competing risks of coronary heart disease, stroke and venous thromboembolism. Am J Epidemiol 2005;162:975-82.
17. Chatterjee N, Sinha S, Diver R, Feigelson H. Analysis of cohort studies with multivariate, partially observed, disease classification data. Biometrika 2010;97:683-98.
18. Ahn J, Schatzkin A, Lacey JV, Albanes D, Ballard-Barbash R, Adams KF, et al. Adiposity, adult weight change and postmenopausal breast cancer risk. Arch Intern Med 2007;167:2091-102.
19. Chatterjee N. A two stage regression model for epidemiological studies with multivariate disease classification data. JASA 2004;99:127-38.
20. Wei LJ, Lin DY, Weissfeld L. Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. JASA 1989;84:1065-73.
21. Schoenfeld D. Partial residuals for the proportional hazards regression model. Biometrika 1982;69:239-41.
22. Mebane WR Jr, Sekhon JS. Version 1.8-6. Available from: http://cran. r-project.org/web/packages/multinomRob/index.html. 2012-09-29.
23. Prentice RL, Caan B, Chlebowski RT, Patterson R, Kuller LH, Ockene JK, et al. Low-fat dietary pattern and risk of invasive breast cancer: the Women's Health Initiative Randomized Controlled Dietary Modification Trial. JAMA 2006;295:629-42.
24. Kalbleisch JD, Prentice RL. The statistical analysis of failure time data. 2nd ed. New York, NY: John Wiley & Sons Inc; 2002 Chapter 10, p. 255.