Statistical Analysis
REGRESSION, SENSITIVITY
An a priori sample size calculation was performed using a Poisson regression model in G*Power, version 3.1.9.4 (University of Kiel). To detect a significant (2-sided α = .05) difference between groups of 50% from a base fall rate of 0.85 with 80% power and an expected 20% dropout, a total of 140 participants, with 70 in each group, was needed. The expected effect size of 50% was based on previous PBT reports.The statistical analyses were conducted in collaboration with an external statistician according to a preregistered statistical analysis plan using Stata, version 17.0 (StataCorp LLC). The analyses were based on the intention-to-treat principle. Two-sided Between-group differences in count outcomes were analyzed using a negative binomial regression with person-years of follow-up as an offset variable. We applied the negative binomial regression as the data fit better with this model than with the preregistered Poisson regression with bootstrapping. A sensitivity analysis using the preregistered approach was conducted. Binary daily-life fall outcomes were evaluated using Poisson regression with robust error variance.Preregistered sensitivity analyses were conducted for each daily-life fall outcome, adjusting for age, sex, and previous falls and following the per-protocol principle, among participants who completed at least 75% of the training sessions. Sensitivity analyses that were decided after data collection included fall rates after stratifying participants based on their fall history and laboratory fall rates after multiple imputations.