How do SGLT2 inhibitors protect the kidney? A mediation analysis of the EMPA-REG OUTCOME trial.

Introduction: Mechanisms underlying kidney benefits with sodium-glucose cotransporter-2 (SGLT2) inhibition in heart failure and/or type 2 diabetes (T2D) with established cardiovascular disease are currently unclear.

Methods: We evaluated post hoc the factors mediating the effect of empagliflozin on a composite kidney outcome (first sustained estimated glomerular filtration rate ≥40% reduction from baseline, initiation of renal replacement therapy or death due to kidney disease) in EMPA-REG OUTCOME (Empagliflozin Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients). Variables, calculated as change from baseline or updated mean, were evaluated as time-dependent covariates and using a landmark approach (at Week 12) in Cox regression analyses.

Sampling designs for rare time-dependent exposures: a comparison of the nested exposure case-control design and exposure density sampling.

In extensive cohort studies, the ascertainment of covariate information on all individuals can be challenging. In hospital epidemiology, an additional issue is often the time-dependency of the exposure of interest. We revisit and compare two sampling designs constructed for rare time-dependent exposures and possibly common outcomes - the nested exposure case-control design and exposure density sampling.

Which patients to sample in clinical cohort studies when the number of events is high and measurement of additional markers is constrained by limited resources.

Purpose: We consider an existing clinical cohort with events but limited resources for the investigation of a further potentially expensive marker. Biological material of the patients is stored in a biobank, but only a limited number of samples can be analyzed with respect to the marker. The question arises as to which patients to sample, if the number of events preclude standard sampling designs.

Exposure density sampling: Dynamic matching with respect to a time-dependent exposure.

Estimating the potential risk associated with an exposure occurring over time requires complex statistical techniques, since ignoring the time from study entry until the exposure leads to potentially seriously biased effect estimates. A prominent example is estimating the effect of hospital-acquired infections on adverse outcomes in patients admitted to the intensive care unit. Exposure density sampling has been proposed as an approach to dynamic matching with respect to a time-dependent exposure.

Risk prediction for Staphylococcus aureus surgical site infection following cardiothoracic surgery; A secondary analysis of the V710-P003 trial.

Background: Identifying patients undergoing cardiothoracic surgery at high risk of Staphylococcus aureus surgical site infection (SSI) is a prerequisite for implementing effective preventive interventions. The objective of this study was to develop a risk prediction model for S. aureus SSI or bacteremia after cardiothoracic surgery based on pre-operative variables.

How Does Empagliflozin Reduce Cardiovascular Mortality? Insights From a Mediation Analysis of the EMPA-REG OUTCOME Trial.

Objective: In the BI 10773 (Empagliflozin) Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME) trial involving 7,020 patients with type 2 diabetes and established cardiovascular (CV) disease, empagliflozin given in addition to standard of care reduced the risk of CV death by 38% versus placebo (hazard ratio [HR] 0.62 [95% CI 0.49, 0.

Modelling two cause-specific hazards of competing risks in one cumulative proportional odds model?

Competing risks extend standard survival analysis to considering time-to-first-event and type-of-first-event, where the event types are called competing risks. The competing risks process is completely described by all cause-specific hazards, ie, the hazard marked by the event type. Separate Cox models for each cause-specific hazard are the standard approach to regression modelling, but they come with the interpretational challenge that there are as many regression coefficients as there are competing risks.

Analysis of Clinical Cohort Data Using Nested Case-control and Case-cohort Sampling Designs. A Powerful and Economical Tool.

Background: Sampling from a large cohort in order to derive a subsample that would be sufficient for statistical analysis is a frequently used method for handling large data sets in epidemiological studies with limited resources for exposure measurement. For clinical studies however, when interest is in the influence of a potential risk factor, cohort studies are often the first choice with all individuals entering the analysis.

Objectives: Our aim is to close the gap between epidemiological and clinical studies with respect to design and power considerations.