Treatment patterns and clinical effectiveness outcomes of palbociclib in combination with aromatase inhibitor (AI) or fulvestrant in hormone receptor positive (HR+)/human epidermal growth factor receptor 2 negative (HER2-) advanced or metastatic breast cancer patients: an observational study using Flatiron Electronic Health Record (EHR) database

Study type

• Study type:
  Non-interventional study
• Scope of the study:
  Drug utilisation

  Effectiveness study (incl. comparative)

Study drug and medical condition

• Name of medicine:
  IBRANCE
• Medical condition to be studied:
  Breast cancer

Population studied

• Age groups:
  • Adult and elderly population (≥18 years)

    • Adults (18 to < 65 years)

      • Adults (18 to < 46 years)
      • Adults (46 to < 65 years)
    • Elderly (≥ 65 years)

      • Adults (65 to < 75 years)
      • Adults (75 to < 85 years)
      • Adults (85 years and over)
• Estimated number of subjects:
  500

Study design details

• Main study objective:
  The main objective is to describe patient demographics, clinical characteristics, treatment patterns and clinical effectiveness outcomes in a cohort of HR+/HER2- breast cancer patients who initiated palbociclib (Ibrance®) in combination with an AI or fulvestrant for treatment of advanced or metastatic disease, using flatiron HER database.
• Outcomes:
  Patient demographics, clinical characteristics, treatment patterns, effectivness outcomes
• Data analysis plan:
  All analyses will be descriptive in nature and no statistical tests of hypotheses will be performed. Patient demographics, clinical characteristics and/or treatment patterns will be presented for all patients and will be explored by line of therapy (provided data are sufficient). Descriptive statistics will be reported for continuous variables (eg, age, time from initial breast cancer diagnosis to metastatic diagnosis) using the mean, median, 25th and 75th quantiles, minimum, maximum, and standard deviation. Categorical variables (eg, region, stage at initial diagnosis) will be reported using frequencies and proportions. The calculation of percentages will always include the missing category in the case of missing values. Counts of missing observations will thus be included in the denominator and presented as a separate category. Kaplan-Meier figures will be generated for “time to” variables and related statistics will be reported.