Author/Authors :
Hajizadeh Ebrahim نويسنده , Baghestani Ahmad Reza نويسنده , Haghighat Shahpar نويسنده Breast Cancer Research Center (BCRC), ACECR, Tehran, IR Iran , Abdollahi Mahbubeh نويسنده Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, IR Iran
Abstract :
[Background]In oncology studies, categorizing a quantitative prognostic variable or determining cut point is aimed at categorizing individuals into homogenous groups. Such categorizations are useful for treatment recommendations and clinical trial design.[Objectives]This article aims at determining the cut point for breast cancer diagnosis age by factors affecting the patients’ survival, using cure model.[Methods]In this longitudinal study, a total 559 patients with breast cancer referring to breast cancer research center, Tehran, Iran, from 1986 to 2006 entered the study. Patients were followed until 2013. The last status of patients was recorded, using telephone conversation. Then, the cut point of breast cancer diagnosis age was determined, using change point cure model with survival-related covariates and R v.2.15.0 software.[Results]In the present study, the mean age of diagnosis was reported 46.31 ± 11.17. Median time of follow-up was 68.36 months with the range of 0.89 to 324. The results showed that age cut point was 49.45 (± 0.64). In young group, one unit increase in tumor size led to 57% reduction in the chance of cure. In old group, the chance of recovery declined by 51%. In old group, the chance of cure among those with lymph node declined by %61 compared to those without lymph node. In the young group, this variable was not significant. Level of education, type of surgery, and estrogen receptor had no significant relationship with cure in none of the age groups at 5% error level.[Conclusions]The results showed that the effect of age in breast cancer prognosis is adjusted at 50-year cut point, leading to two relatively homogeneous groups. This cut point is effective in assessing predictive and prognostic factors in breast cancer. The difference between effect of tumor size and effect of lymph node involvement in different age groups can be helpful in determining more appropriate therapeutic strategies.