Effect on non-vascular outcomes of lowering LDL cholesterol in patients with chronic kidney disease: results from the Study of Heart and Renal Protection.

BACKGROUND: Reducing LDL cholesterol (LDL-C) with statin-based therapy reduces the risk of major atherosclerotic events among patients with chronic kidney disease (CKD), with no evidence of an excess risk of cancer or death from any non-vascular cause. However, non-randomized data have suggested that statin therapy may have effects (both adverse and beneficial) on particular non-vascular conditions that do not cause death. METHODS: The Study of Heart and Renal Protection (SHARP) randomized patients with CKD to simvastatin 20 mg plus ezetimibe 10 mg (simvastatin/ezetimibe) daily versus matching placebo. Participants were followed up at least 6 monthly and all post-randomization serious adverse events (SAEs) were recorded. This supplementary analysis reports the effects of treatment on non-vascular SAEs, overall, by system of disease, by baseline characteristics, and by duration of follow-up. RESULTS: During a median of 4.9 years follow-up, similar numbers of participants in the two groups experienced at least one non-vascular SAE (3551 [76.4%] simvastatin/ezetimibe vs 3537 [76.6%] placebo; risk ratio [RR] 0.99, 95% confidence interval [CI] 0.95-1.04). There was no good evidence of any significant effect of simvastatin/ezetimibe on SAEs attributed to any particular nonvascular disease system (of 43 comparisons, only 3 yielded an uncorrected p value < 0.05, of which the smallest was p = 0.02). The relative risk of any nonvascular SAE did not vary significantly among particular prognostic subgroups or by duration of follow-up. CONCLUSIONS: In the SHARP trial, allocation to simvastatin/ezetimibe combination therapy was not associated with any significant non-vascular hazard. TRIALS REGISTRATION: SHARP was retrospectively registered after the first participant was enrolled in 2003 at ISRCTN (ISRCTN54137607 on 31 January 2005: http://www.isrctn.com/ISRCTN54137607) and ClinicalTrials.gov (NCT00125593 on 29 July 2005: https://clinicaltrials.gov/ct2/show/NCT00125593).

Dependent censoring in piecewise exponential survival models.

There are often reasons to suppose that there is dependence between the time to event and time to censoring, or dependent censoring, for survival data, particularly when considering medical data. This is because the decision to treat or not is often made according to prognosis, usually with the most ill patients being prioritised. Due to identifiability issues, sensitivity analyses are often used to assess whether independent censoring can lead to misleading results. In this paper, a sensitivity analysis method for piecewise exponential survival models is presented. This method assesses the sensitivity of the results of standard survival models to small amounts of dependence between the time to failure and time to censoring variables. It uses the same assumption about the dependence between the time to failure and time to censoring as previous sensitivity analyses for both standard parametric survival models and the Cox model. However, the method presented in this paper allows the use of more flexible models for the marginal distributions whilst remaining computationally simple. A simulation study is used to assess the accuracy of the sensitivity analysis method and identify the situations in which it is suitable to use this method. The study found that the sensitivity analysis performs well in many situations, but not when the data have a high proportion of censoring.