Predicting Breast Cancer Mortality in the Presence of Competing Risks Using Smartphone Application Development Software
DOI:
https://doi.org/10.6000/1929-6029.2015.04.04.2Keywords:
Smartphone applications, App Inventor, competing risks, breast cancer, screeningAbstract
The widespread use of smartphone applications (apps) provides a promising new platform for medical research and healthcare decision making. Given the need to help guide clinical discussions about the appropriateness of breast cancer screening in the presence of competing risks among older women, we proposed to incorporate the Fine-Gray prediction model, which offers more intuitive clinical interpretation of risk in the presence of competing risks, into a smartphone-based decision aid application. Clinicians can input the woman’s characteristics and medical history, and the app will output prediction estimates of both types of events (i.e. death from breast cancer and competing risk events) given the presence or absence of breast cancer screening. This prototype was built using drag-and-drop visual programming tools provided by the free, cloud-based software “MIT App Inventor for Android.” It will be intended for clinicians to use in the context of patients’ values to decide whether screening is appropriate for an individual. Our analysis indicated that screening was beneficial to survival, and that older women benefited less from screening due to the increasing incidence of non-breast-cancer competing risk deaths as age increased. The algorithm we implemented for the app provides instant probability estimates that help quantify screening benefits as a function of age, and comorbidity burden.
References
Breast Cancer Facts & Figures 2005-2006. American Cancer Society, Inc., Atlanta, GA 2007.
Clark R. Principles of Cancer Screening. Cancer Control: Journal of the Moffitt Cancer Center 1995; 2: 485-492. DOI: https://doi.org/10.1177/107327489500200601
Tabar L, Fagerberg CJ, Gad A, Baldetorp L, Holmberg LH, Grontoft O, et al. Reduction in mortality from breast cancer after mass screening with mammography. Randomised trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare. Lancet 1985; 1: 829-832. http://dx.doi.org/10.1016/S0140-6736(85)92204-4 DOI: https://doi.org/10.1016/S0140-6736(85)92204-4
Andersson I, Aspegren K, Janzon L, Landberg T, Lindholm K, Linell F, et al. Mammographic screening and mortality from breast cancer: the Malmo mammographic screening trial. BMJ (Clinical research ed.) 1988; 297: 943-948. http://dx.doi.org/10.1136/bmj.297.6654.943 DOI: https://doi.org/10.1136/bmj.297.6654.943
Baker LH. Breast Cancer Detection Demonstration Project: five-year summary report. CA: A Cancer Journal for Clinicians 1982; 32: 194-225. http://dx.doi.org/10.3322/canjclin.32.4.194 DOI: https://doi.org/10.3322/canjclin.32.4.194
Shapiro S, Venet W, Strax P, Venet L, Roeser R. Ten- to fourteen-year effect of screening on breast cancer mortality. Journal of the National Cancer Institute 1982; 69: 349-355.
Tabar L, Fagerberg G, Chen HH, Duffy SW, Smart CR, Gad A, Smith RA. Efficacy of breast cancer screening by age. New results from the Swedish Two-County Trial. Cancer 1995; 75: 2507-2517. http://dx.doi.org/10.1002/1097-0142(19950515)75:10<2507::AID-CNCR2820751017>3.0.CO;2-H DOI: https://doi.org/10.1002/1097-0142(19950515)75:10<2507::AID-CNCR2820751017>3.0.CO;2-H
Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 1958; 53: 457-481. http://dx.doi.org/10.1080/01621459.1958.10501452 DOI: https://doi.org/10.1080/01621459.1958.10501452
Cox DR. Regression Models and Life-Tables. Breakthroughs in Statistics, Springer 1992; pp. 527-541. http://dx.doi.org/10.1007/978-1-4612-4380-9_37 DOI: https://doi.org/10.1007/978-1-4612-4380-9_37
Pintilie M. Competing risks: a practical perspective. John Wiley & Sons 2006, DOI: https://doi.org/10.1002/9780470870709
Gooley TA, Leisenring W, Crowley J, Storer BE. Estimation of failure probabilities in the presence of competing risks: new representations of old estimators. Statistics in Medicine 1999; 18: 695-706. http://dx.doi.org/10.1002/(SICI)1097-0258(19990330)18:6<695::AID-SIM60>3.0.CO;2-O DOI: https://doi.org/10.1002/(SICI)1097-0258(19990330)18:6<695::AID-SIM60>3.0.CO;2-O
Tai BC, Machin D, White I, Gebski V. Competing risks analysis of patients with osteosarcoma: a comparison of four different approaches. Statistics in Medicine 2001; 20: 661-684. http://dx.doi.org/10.1002/sim.711 DOI: https://doi.org/10.1002/sim.711
Prentice RL, Kalbfleisch JD, Peterson Jr. AV, Flournoy N, Farewell V, Breslow N. The analysis of failure times in the presence of competing risks. Biometrics 1978; 541-554. http://dx.doi.org/10.2307/2530374 DOI: https://doi.org/10.2307/2530374
Kalbfleisch JD, Prentice RL. The statistical analysis of failure time data. John Wiley & Sons 2011.
Welch HG, Albertsen PC, Nease RF, Bubolz TA, Wasson JH. Estimating treatment benefits for the elderly: the effect of competing risks. Annals of Internal Medicine 1996; 124: 577-584. http://dx.doi.org/10.7326/0003-4819-124-6-199603150-00007 DOI: https://doi.org/10.7326/0003-4819-124-6-199603150-00007
Lunn M, McNeil D. Applying Cox regression to competing risks. Biometrics 1995; 524-532. http://dx.doi.org/10.2307/2532940 DOI: https://doi.org/10.2307/2532940
Moeschberger M, Klein JP. Statistical methods for dependent competing risks. Lifetime Data Analysis 1995; 1: 195-204. http://dx.doi.org/10.1007/BF00985770 DOI: https://doi.org/10.1007/BF00985770
Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association 1999; 94: 496-509. http://dx.doi.org/10.1080/01621459.1999.10474144 DOI: https://doi.org/10.1080/01621459.1999.10474144
Pew Research Internet Project. http://www.pewinternet.org/ fact-sheets/mobile-technology-fact-sheet/
Edwards J. The iPhone 6 Had Better Be Amazing And Cheap, Because Apple Is Losing The War To Android 2014. http://www.businessinsider.com/iphone-v-android-market-share-2014-5
Warren C. Google Play Hits 1 Million Apps 2013. http://mashable.com/2013/07/24/google-play-1-million/
Schonberg MA, Davis RB, McCarthy EP, Marcantonio ER. Index to predict 5-year mortality of community-dwelling adults aged 65 and older using data from the National Health Interview Survey. Journal of General Internal Medicine 2009; 24: 1115-1122. http://dx.doi.org/10.1007/s11606-009-1073-y DOI: https://doi.org/10.1007/s11606-009-1073-y
Schonberg MA, Davis RB, McCarthy EP, Marcantonio ER. External validation of an index to predict up to 9-year mortality of community-dwelling adults aged 65 and older. Journal of the American Geriatrics Society 2011; 59: 1444-1451. http://dx.doi.org/10.1111/j.1532-5415.2011.03523.x DOI: https://doi.org/10.1111/j.1532-5415.2011.03523.x
Lee SJ, Lindquist K, Segal MR, Covinsky KE. Development and validation of a prognostic index for 4-year mortality in older adults. JAMA 2006; 295: 801-808. http://dx.doi.org/10.1001/jama.295.7.801 DOI: https://doi.org/10.1001/jama.295.7.801
Cruz M, Covinsky K, Widera EW, Stijacic-Cenzer I, Lee SJ. Predicting 10-year mortality for older adults. JAMA 2013; 309: 874-876. http://dx.doi.org/10.1001/jama.2013.1184 DOI: https://doi.org/10.1001/jama.2013.1184
Weaver ER, Horyniak DR, Jenkinson R, Dietze P, Lim MS. "Let's get Wasted!" and Other Apps: Characteristics, Acceptability, and Use of Alcohol-Related Smartphone Applications. JMIR mHealth and uHealth 2013; 1: e9. DOI: https://doi.org/10.2196/mhealth.2709
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of Chronic Diseases 1987; 40: 373-383. http://dx.doi.org/10.1016/0021-9681(87)90171-8 DOI: https://doi.org/10.1016/0021-9681(87)90171-8
Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Medical Care 1998; 36: 8-27. http://dx.doi.org/10.1097/00005650-199801000-00004 DOI: https://doi.org/10.1097/00005650-199801000-00004
Klabunde CN, Potosky AL, Legler JM, Warren JL. Development of a comorbidity index using physician claims data. Journal of Clinical Epidemiology 2000; 53: 1258-1267. http://dx.doi.org/10.1016/S0895-4356(00)00256-0 DOI: https://doi.org/10.1016/S0895-4356(00)00256-0
McCarthy EP, Burns RB, Coughlin SS, Freund KM, Rice J, Marwill SL, et al. Mammography use helps to explain differences in breast cancer stage at diagnosis between older black and white women. Annals of Internal Medicine 1998; 128: 729-736. http://dx.doi.org/10.7326/0003-4819-128-9-199805010-00005 DOI: https://doi.org/10.7326/0003-4819-128-9-199805010-00005
Zhang JX, Iwashyna TJ, Christakis NA. The performance of different lookback periods and sources of information for Charlson comorbidity adjustment in Medicare claims. Medical Care 1999; 37: 1128-1139. http://dx.doi.org/10.1097/00005650-199911000-00005 DOI: https://doi.org/10.1097/00005650-199911000-00005
Akaike H. Information theory and an extension of the maximum likelihood principle. Selected Papers of Hirotugu Akaike, Springer 1998; pp. 199-213. http://dx.doi.org/10.1007/978-1-4612-1694-0_15 DOI: https://doi.org/10.1007/978-1-4612-1694-0_15
Bakoyannis G, Touloumi G. Practical methods for competing risks data: A review. Statistical Methods in Medical Research 2012; 21: 257-272. http://dx.doi.org/10.1177/0962280210394479 DOI: https://doi.org/10.1177/0962280210394479
Kohl M, Heinze G. PSHREG: A SAS macro for proportional and nonproportional substribution hazards regression with competing risk data. Technical report 08/2012, Center for Medical Statistics, Informatics and Intelligent Systems 2013.
Pokress SC, Veiga JJD. MIT App Inventor: Enabling Personal Mobile Computing 2013; arXiv preprint arXiv:1310.2830.
Wolber D, Abelson H, Spertus E, Looney L. App Inventor. O'Reilly Media, Inc 2011.
Kobie N. Google ditches Android App Inventor 2011. PC Pro. http://www.pcpro.co.uk/news/education/369283/google-ditches-android-app-inventor
Kobie N. MIT takes over App Inventor for Android 2011. PC Pro. http://www.pcpro.co.uk/news/education/369370/mit-takes-over-app-inventor-for-android
Sharing and Packaging Apps. MIT App Inventor. http://appinventor.mit.edu/explore/ai2/share.html
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Copyright (c) 2015 Yuanyuan Liu, Ellen P. McCarthy, Long H. Ngo
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