Assessing the impact of early detection biases on breast cancer survival of Catalan women

  1. Roso Llorach, Albert
  2. Forné Izquierdo, Carles
  3. Macià Guilà, Francesc
  4. Galceran Padrós, Jaume
  5. Marcos Gragera, Rafael
  6. Rué Monné, Montserrat
Revista:
Sort: Statistics and Operations Research Transactions

ISSN: 1696-2281

Año de publicación: 2014

Volumen: 38

Número: 2

Páginas: 139-160

Tipo: Artículo

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Otras publicaciones en: Sort: Statistics and Operations Research Transactions

Resumen

Survival estimates for women with screen-detected breast cancer are affected by biases specific to early detection. Lead-time bias occurs due to the advance of diagnosis, and length-sampling bias because tumors detected on screening exams are more likely to have slower growth than tumors symptomatically detected. Methods proposed in the literature and simulation were used to assess the impact of these biases. If lead-time and length-sampling biases were not taken into account, the median survival time of screen-detected breast cancer cases may be overestimated by 5 years and the 5-year cumulative survival probability by between 2.5 to 5 percent units.

Referencias bibliográficas

  • Blanch, J., Sala, M., Ibáñez, J., Domingo, L., Fernandez, B., Otegi, A., Barata, T., Zubizarreta, R., Ferrer, J., Castells, X., Rué, M. and Salas, D. (2014). Impact of risk factors on different interval cancer subtypes in a population-based breast cancer screening programme. PLoS One, 9(10): e110207.
  • Borras, J., Ameijide, A., Vilardell, L., Valls, J., Marcos-Gragera, R. and Izquierdo, A. (2008). Trends in cancer incidence in Catalonia, 1985-2002. Medicina Clı́nica (Barcelona), 131 Suppl 1, 11–18.
  • Burton, A., Altman, D. G., Royston, P. and Holder, L. (2006). The design of simulation studies in medical statistics. Statistics in Medicine, 25, 4279–4292.
  • Bush, D., Smith, B., Younger, J. and Michaelson, J. S. (2010). The non-breast-cancer death rate among breast cancer patients. Breast Cancer Research and Treatment.
  • Domingo, L., Salas, D., Zubizarreta, R., Baré, M., Sarriugarte, G., Barata, T., Ibáñez, J., Blanch, J., PuigVives, M., Fernández, A., Castells, X. and Sala, M. (2014). Tumor phenotype and breast density in distinct categories of interval cancer: results of population-based mammography screening in Spain. Breast Cancer Research, 16: R3.
  • Duffy, S. W., Nagtegaal, I. D., Wallis, M., Cafferty, F. H., Houssami, N., Warwick, J., Allgood, P. C., Kearins, O., Tappenden, N., O’Sullivan, E. and Lawrence, G. (2008). Correcting for lead time and length bias in estimating the effect of screen detection on cancer survival. American Journal of Epidemiology, 168, 98–104.
  • Galceran, J., Puigdefabregas, A., Ribas, G., Izquierdo, A., Pareja, L. andMarcos-Gragera, R. (2008). Cancer survival trends in Catalonia and comparison with Europe. Medicina Clı́nica (Barcelona), 131 Suppl 1, 19–24.
  • Gotzsche, P. C. and Nielsen, M. (2009). Screening for breast cancer with mammography. Cochrane Database of Systematic Reviews, (4), CD001877.
  • Kafadar, K. and Prorok, P. C. (2009). Effect of length biased sampling of unobserved sojourn times on the survival distribution when disease is screen detected. Statistics in Medicine, 28, 2116–2146.
  • Lee, S. and Zelen, M. (2006). A stochastic model for predicting the mortality of breast cancer. Journal of the National Cancer Institute Monographs, 79–86.
  • Lee, S. J. and Zelen, M. (1998). Scheduling periodic examinations for the early detection of disease: applications to breast cancer. Journal of the American Statistical Association, 93, 1271–1281.
  • Lee, S. J. and Zelen, M. (2008). Mortality modeling of early detection programs. Biometrics, 64, 386–395.
  • Lehtimaki, T., Lundin, M., Linder, N., Sihto, H., Holli, K., Turpeenniemi-Hujanen, T., Kataja, V., Isola, J., Joensuu, H. and Lundin, J. (2011). Long-term prognosis of breast cancer detected by mammography screening or other methods. Breast Cancer Research and Treatment, 13, R134.
  • Mahnken, J. D., Chan, W., Jr Freeman, D. H. and Freeman, J. L. (2008). Reducing the effects of lead-time bias, length bias and over-detection in evaluating screening mammography: a censored bivariate data approach. Statistical Methods in Medical Research, 17, 643–663.
  • Martinez-Alonso, M., Vilaprinyo, E., Marcos-Gragera, R. and Rue, M. (2010). Breast cancer incidence and overdiagnosis in Catalonia (Spain). Breast Cancer Research and Treatment, 12, R58.
  • R Core Team (2013). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
  • Shen, Y., Yang, Y., Inoue, L. Y., Munsell, M. F., Miller, A. B. and Berry, D. A. (2005). Role of detection method in predicting breast cancer survival: analysis of randomized screening trials. Journal of the National Cancer Institute, 97, 1195–1203.
  • Trivedi, P. K. and Zimmer, D. M. (2007). Copula modeling: an introduction for practitioners. Foundation and trends in Econometrics, 1, 1–111.
  • Vilaprinyo, E., Gispert, R., Martinez-Alonso, M., Carles, M., Pla, R., Espinas, J. A. and Rue, M. (2008). Competing risks to breast cancer mortality in Catalonia. BMC Cancer, 8, 331.
  • Vilaprinyo, E., Rue, M., Marcos-Gragera, R. andMartinez-Alonso, M. (2009). Estimation of ageand stagespecific Catalan breast cancer survival functions using US and Catalan survival data. BMC Cancer, 9, 98.
  • Walter, S. D. and Day, N. E. (1983). Estimation of the duration of a pre-clinical disease state using screening data. American Journal of Epidemiology, 118, 865–886.
  • Walter, S. D. and Stitt, L. W. (1987). Evaluating the survival of cancer cases detected by screening. Statistics in Medicine, 6, 885–900.
  • Xu, J. L., Fagerstrom, R. M. and Prorok, P. C. (1999). Estimation of post-lead-time survival under dependence between lead-time and post-lead-time survival. Statistics in Medicine, 18, 155–162.
  • Xu, J. L. and Prorok, P. C. (1995). Non-parametric estimation of the post-lead-time survival distribution of screen-detected cancer cases. Statistics in Medicine, 14, 2715–2725.
  • Zelen, M. and Feinleib, M. (1969). On the theory of screening for chronic diseases. Biometrika, 56, 601– 614.
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