Statistical Analysis
Frailty, CKD
For the hypothetical trial, we would try to randomize individuals such that all factors that could possibly affect the outcome would be balanced between the treatment groups. In the emulation setting, there is the potential for confounding by indication, thus we used propensity score weighting methods to ensure the treatment groups, conditional on these confounders, would be exchangeable at baseline. Variables used in the propensity score calculation included age, sex, race, BMI, smoking status (current vs former or never), blood pressure medications (yes vs no), nonstatin prescriptions (yes vs no), polypharmacy (yes vs no), comorbidities as listed previously (yes vs no for each), specific CKD stage (3 vs 4), Frailty Index,We used overlap weighting to minimize the influence of extreme propensity scores on the model output.As a final sensitivity analysis, we considered a new user design, assuming eligibility assessment at CKD diagnosis and selecting the first eligible baseline for each treatment and individual, ie, 1 row for statin initiators at time of statin initiation and 1 row for each eligible individual at index or date of initial CKD diagnosis.All analyses were conducted in SAS Enterprise Guide version 8.3 (SAS Institute). Two-sided 