Delayed Denosumab Injections and Fractures Risk Among Subjects with Osteoporosis

Study type

• Study topic:
  Human medicinal product

  Disease /health condition
• Study type:
  Non-interventional study
• Scope of the study:
  Assessment of risk minimisation measure implementation or effectiveness

  Drug utilisation
• Data collection methods:
  Secondary use of data

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

• Anatomical Therapeutic Chemical (ATC) code:
  (M05BX04) denosumab

  denosumab
• Medical condition to be studied:
  Osteoporosis

Population studied

• Short description of the study population:
  Our study population will include individuals aged ≥45 years who used denosumab for the management of osteoporosis between 2010 and 2018.
• Age groups:
  Adults (46 to < 65 years)

  Adults (65 to < 75 years)

  Adults (75 to < 85 years)

  Adults (85 years and over)
• Estimated number of subjects:
  3000

Study design details

• Main study objective:
  The proposed analyses aim to examine the fracture risk of delayed denosumab injections among patients who used this medication for long-term osteoporosis management.
• Outcomes:
  The primary outcome of interest is composite fracture including all types of fracture. Secondary outcomes include major fracture (hip fracture, vertebral fracture, wrist fracture, humerus fracture, pelvis fracture and rib fracture), vertebral fracture, and hip fracture.
• Data analysis plan:
  We will emulate sequential randomized controlled trials (RCT) comparing the three different strategies (no delay, short delay, and long delay) using observational data. To avoid fatal bias, we will use the "clone and censor" method and results from sequential emulated studies will be combined. We will fit a pooled logistic regression model for each fracture outcome. Because the outcome of the models is rare at all times, the odds ratio from this model approximates the hazard ratio (HR). We will compare the HR across groups, with a specific interest in the trend. Inverse probability weighting will be used to ameliorate the selection bias issue introduced by censoring. Non-linear relationships between denosumab injection delay and fracture risk will be examined exploratorily.