Tumor characteristics.
Time frame · At baseline.
Percentage of days covered of compliance
Time frame · At 18 months
Description of breast cancer treatment.
Time frame · At baseline.
Including triptorelin formulation (1-month or 3-month formulation), planned neoadjuvant/adjuvant chemotherapy (yes/no) and oral adjuvant endocrine treatment prescribed ((Aromatase Inhibitor (AI) or Tamoxifen (TAM))
Identification of potential demographic factors predictive of any treatment switch.
Time frame · At 18 months.
A multivariate logistic regression will be used to identify demographic factors predictive of any treatment switches.
Identification of demographic and tumor characteristics correlated to the entire prescribed breast cancer treatment.
Time frame · At 18 months.
A multivariate logistic regression will be used to identify characteristics correlated to the prescription of the 1-month or 3-month triptorelin formulations with TAM or an AI.
Proportion of participants who switch between triptorelin formulations, from AI to TAM or vice-versa, or undergo both triptorelin and adjuvant therapy switches
Time frame · At 18 months.
The switches between triptorelin formulation or from the AI to TAM will be described together with the reasons for switches.
Proportion of disease-free participants.
Time frame · At 18 months.
Disease-free survival is defined as the time from triptorelin initiation to disease recurrence as determined by the Investigator.
Proportion of participants alive.
Time frame · At 18 months.
The OS rate will be estimated using the Kaplan-Meier method. Survival time will be defined as the time from triptorelin initiation to participant's death for any reason.
Proportion of compliant patients (100% of the planned injections) to triptorelin
Time frame · At 18 months.
Identification of potential demographic, tumor-related or breast cancer treatment-related factors predictive of suboptimal OFS.
Time frame · At 18 months.
as per local definition, or empirically defined as E2 levels ranging between 30 and 70 pg/ml according to the average range collected in Italian centers. A multivariate logistic regression will be used to identify predictive factors of suboptimal OFS as per local definition