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Time-to-progression in breast cancer: a stratification model for clinical trials.

Abstract
The development of new anti-tumour drugs without clear cytoreductive activity has necessitated changes in the design of clinical trials. Defining the "time" parameter has become the essential objective of the majority of these trials. However, in breast cancer, this parameter is highly variable and, as such, difficult to quantify. We developed a useful tool that takes into account the inter-relatedness of all the variables known to have the capacity to predict the time-to-progression (TTP) in advanced breast cancer. From the Alamo database (GEICAM), we selected 1798 patients diagnosed as having metastatic breast cancer. Univariate analysis was performed using the method of Kaplan-Meier. Multivariate analysis was with the Cox regression method. The variables that were shown to have independent predictive value for the TTP were: non-visceral metastatic disease, single metastases, hormonal receptor positive N/T ratio<2 and disease-free interval (DFI) > or = 24 months. Taking into account the variables that had reached an independent predictive value, we constructed a model of scoring in which the patients were grouped according to the TTP. Using our new scoring model, it is possible to group patients with metastatic breast cancer according to the predicted TTP. This can be a useful tool at the time of selecting and stratifying patients on entry into new randomised clinical trials.
AuthorsJuan de la Haba-Rodríguez, Enrique Aranda, Antonio Llombart, Ana Lluch, Emilio Alba, Blanca Munárriz, Encarna Adrover, Ignacio Tusquets, Ana Balil, Agustí Barnadas, Lourdes Calvo, Miguel Martín, Grupo Español de Investigación en Cáncer de Mama (GEICAM)
JournalBreast (Edinburgh, Scotland) (Breast) Vol. 17 Issue 3 Pg. 239-44 (Jun 2008) ISSN: 0960-9776 [Print] Netherlands
PMID18042386 (Publication Type: Journal Article)
Topics
  • Aged
  • Breast Neoplasms (epidemiology, pathology, secondary)
  • Clinical Trials as Topic
  • Disease Progression
  • Female
  • Humans
  • Middle Aged
  • Models, Statistical
  • Multivariate Analysis
  • Risk Assessment
  • Time Factors

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