Bayesian synthesis using prior information on fracture risk from randomized trials to analyze post-market data

Objective — To conduct a Bayesian evidence synthesis using commonly available statistical procedures in order to estimate fracture risk for postmenopausal women undergoing hormonal therapy for breast cancer.

Study Design and Setting — Using linked administrative data, we conducted a retrospective cohort study of women age 66 or older diagnosed with stage I to III breast cancer in Ontario, Canada, between April 1, 2003 and February 28, 2010. We used data augmentation to perform Bayesian Cox regression of the hazard of a hip, spine or wrist/forearm fracture, adjusting for age, history of fragility fracture, corticosteroid use, osteoporosis, rheumatoid arthritis, dementia or diabetes diagnoses.

Results — Of 10,259 included in the sample, 3,733 initiated on Tamoxifen and 6,526 on an aromatase inhibitor (AI). Posterior probabilities that the HR exceeded 1 for AI compared to Tamoxifen were 46% (HR=0.99, 95%CrI 0.71, 1.25), 35% (HR=0.94, 95%CrI 0.78, 1.26) and 76% (HR=1.08, 95%CrI 0.88, 1.32) with an uninformative prior, and 63% (HR=1.04, 95%CrI 0.83, 1.3), 84% (HR=1.12, 95%CrI 0.89, 1.4) and 89% (HR=1.13, 95%CrI 0.93, 1.36) with an informative prior, for hip, spine and wrist/forearm fracture, respectively.

Conclusions — Prior information resulted in higher posterior probabilities. The strength of evidence for increased risk varied by fracture site.