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A time-to-first-event composite endpoint analysis has well-known shortcomings in evaluating a treatment effect in cardiovascular clinical trials. It does not fully describe the clinical benefit of therapy because the severity of the events, events repeated over time, and clinically relevant nonsurvival outcomes cannot be considered. The generalized pairwise comparisons (GPC) method adds flexibility in defining the primary endpoint by including any number and type of outcomes that best capture the clinical benefit of a therapy as compared with standard of care. Clinically important outcomes, including bleeding severity, number of interventions, and quality of life, can easily be integrated in a single analysis. The treatment effect in GPC can be expressed by the net treatment benefit, the success odds, or the win ratio. This review provides guidance on the use of GPC and the choice of treatment effect measures for the analysis and reporting of cardiovascular trials. [Display omitted] • GPC is a statistical method that overcomes shortcomings of time-to-first-event analyses. • GPC can assess treatment effects as NTB, SO, and WR. • This review provides guidance on the use of GPC and choice of treatment effect measures. [ABSTRACT FROM AUTHOR]

Abstract Aims Absolute treatment effects—i.e. numbers needed to treat (NNTs)—of novel antidiabetic drugs for cardiovascular outcomes have not been comprehensively evaluated. We aimed to perform a meta‐analysis of digitalized individual patient outcomes to display and compare absolute treatment effects. Methods and results Individual patient time‐to‐event information from Kaplan–Meier plots of cardiovascular mortality (CM) and/or hospitalization for heart failure (HHF) endpoints from cardiovascular outcome trials (CVOTs) evaluating dipeptidyl peptidase‐4 (DPP‐4) inhibitors, glucagon‐like peptide‐1 (GLP‐1) receptor agonists, and sodium glucose transporter 2 (SGLT2) inhibitors vs. placebo were digitalized using WebPlotDigitizer 4.2 and the R code of Guyot et al.; Weibull regression models were generated, validated, and used to estimate NNT for individual trials; random‐effects meta‐analysis generated Meta‐NNT with 95% confidence intervals. Sixteen CVOTs reported time‐to‐event information (14 in primary diabetes and 2 in primary heart failure populations). Thirteen studies including 96 860 patients were meta‐analysed for CM: At the median follow‐up of 30 months, Meta‐NNTs were 178 (64 to ∞ to −223) for DPP‐4 inhibitors, 261 (158 to 745) for GLP‐1 receptor agonists, and 118 (68 to 435) for SGLT2 inhibitors. Ten studies including 96 128 patients were meta‐analysed for HHF: At the median follow‐up of 29 months, estimated Meta‐NNTs were −644 (229 to ∞ to −134) for DPP‐4 inhibitors, 441 (184 to ∞ to −1100) for GLP‐1 receptor agonists, and 126 (91 to 208) for SGLT2 inhibitors. SGLT2 inhibitors were especially effective for HHF in primary heart failure populations [Meta‐NNT 25 (19 to 39)] vs. primary diabetes populations [Meta‐NNT 233 (167 to 385)] at 16 months of follow‐up. Conclusions We found only modest treatment benefits of GLP‐1 receptor agonists and SGLT2 inhibitors for CM and HHF in primary type 2 diabetes mellitus populations. In primary heart failure populations, SGLT2 inhibitor benefits were substantial and comparable in efficacy to established heart failure medication.

Aims: Absolute treatment effects—i.e. numbers needed to treat (NNTs)—of novel antidiabetic drugs for cardiovascular outcomes have not been comprehensively evaluated. We aimed to perform a meta‐analysis of digitalized individual patient outcomes to display and compare absolute treatment effects. Methods and results: Individual patient time‐to‐event information from Kaplan–Meier plots of cardiovascular mortality (CM) and/or hospitalization for heart failure (HHF) endpoints from cardiovascular outcome trials (CVOTs) evaluating dipeptidyl peptidase‐4 (DPP‐4) inhibitors, glucagon‐like peptide‐1 (GLP‐1) receptor agonists, and sodium glucose transporter 2 (SGLT2) inhibitors vs. placebo were digitalized using WebPlotDigitizer 4.2 and the R code of Guyot et al.; Weibull regression models were generated, validated, and used to estimate NNT for individual trials; random‐effects meta‐analysis generated Meta‐NNT with 95% confidence intervals. Sixteen CVOTs reported time‐to‐event information (14 in primary diabetes and 2 in primary heart failure populations). Thirteen studies including 96 860 patients were meta‐analysed for CM: At the median follow‐up of 30 months, Meta‐NNTs were 178 (64 to ∞ to −223) for DPP‐4 inhibitors, 261 (158 to 745) for GLP‐1 receptor agonists, and 118 (68 to 435) for SGLT2 inhibitors. Ten studies including 96 128 patients were meta‐analysed for HHF: At the median follow‐up of 29 months, estimated Meta‐NNTs were −644 (229 to ∞ to −134) for DPP‐4 inhibitors, 441 (184 to ∞ to −1100) for GLP‐1 receptor agonists, and 126 (91 to 208) for SGLT2 inhibitors. SGLT2 inhibitors were especially effective for HHF in primary heart failure populations [Meta‐NNT 25 (19 to 39)] vs. primary diabetes populations [Meta‐NNT 233 (167 to 385)] at 16 months of follow‐up. Conclusions: We found only modest treatment benefits of GLP‐1 receptor agonists and SGLT2 inhibitors for CM and HHF in primary type 2 diabetes mellitus populations. In primary heart failure populations, SGLT2 inhibitor benefits were substantial and comparable in efficacy to established heart failure medication. [ABSTRACT FROM AUTHOR]

Objective: To estimate the absolute treatment effect of statin therapy on major adverse cardiovascular events (MACE; myocardial infarction, stroke and vascular death) for the individual patient aged ≥70 years. Methods: Prediction models for MACE were derived in patients aged ≥70 years with ( n = 2550) and without ( n = 3253) vascular disease from the 'PROspective Study of Pravastatin in Elderly at Risk' (PROSPER) trial and validated in the 'Secondary Manifestations of ARTerial disease' (SMART) cohort study ( n = 1442) and the 'Anglo-Scandinavian Cardiac Outcomes Trial-Lipid Lowering Arm' (ASCOT-LLA) trial ( n = 1893), respectively, using competing risk analysis. Prespecified predictors were various clinical characteristics including statin treatment. Individual absolute risk reductions (ARRs) for MACE in 5 and 10 years were estimated by subtracting on-treatment from off-treatment risk. Results: Individual ARRs were higher in elderly patients with vascular disease [5-year ARRs: median 5.1 %, interquartile range (IQR) 4.0-6.2 %, 10-year ARRs: median 7.8 %, IQR 6.8-8.6 %] than in patients without vascular disease (5-year ARRs: median 1.7 %, IQR 1.3-2.1 %, 10-year ARRs: 2.9 %, IQR 2.3-3.6 %). Ninety-eight percent of patients with vascular disease had a 5-year ARR ≥2.0 %, compared to 31 % of patients without vascular disease. Conclusions: With a multivariable prediction model the absolute treatment effect of a statin on MACE for individual elderly patients with and without vascular disease can be quantified. Because of high ARRs, treating all patients is more beneficial than prediction-based treatment for secondary prevention of MACE. For primary prevention of MACE, the prediction model can be used to identify those patients who benefit meaningfully from statin therapy. [ABSTRACT FROM AUTHOR]

Our society is currently facing an epidemic of diabetes and heart failure. Historically, certain cardiology treatments, mainly beta-blockers, have been considered ‘dangerous’ in diabetic patients, but the time has come for personalized medicine to be applied in the field of cardiology, especially in heart failure (HF). To determine whether HF treatment should be individualized according to diabetes status, this review of the available randomized evidence was carried out, with special emphasis on treatment-effect modification in relation to diabetes. Based on a large body of evidence in the literature, our review concludes that HF treatment should be the same for diabetic and non-diabetic patients. In concurrence, international guidelines now strongly advocate the use of HF drugs, including beta-blockers, in diabetic HF patients. The benefit of HF treatment is at least as favourable in such patients as in non-diabetic patients on a relative basis. Given the higher risk of events in diabetics, this could translate to an even greater absolute impact of HF treatment in these patients, which should further encourage caregivers to more aggressively manage HF in diabetic patients. To this end, non-cardiologists, including general practitioners and endocrinologists/diabetologists who treat diabetic HF patients, should be considered part of the HF drug optimalization process, including the referral of patients to specialized centres for possible implantable cardiac defibrillators and/or cardiac resynchronization indication assessment. [ABSTRACT FROM AUTHOR]

Objectives: To describe the use of risk-difference curves for communicating time-dependent absolute treatment effects. Study Design and Setting: Three examples based on individual patient data meta-analyses for adjuvant treatments for early-stage breast cancer are presented. Unit record datasets were re-created from the published Kaplan-Meier curves and numbers at risk or person-years at risk. Risk-difference curves, with corresponding 95% confidence bands, are presented and discussed. Results: Risk-difference curves are useful for communicating the results from trials of adjuvant treatments for early-stage cancer when standard measures of the absolute treatment effect for survival data (ie, difference-in-mean and difference-in-median survival) can be difficult to estimate. They also avoid the problem of ''evolving selection bias'', which can affect interval-specific hazard ratio (HR)s in trials with long follow-up and where the participants are heterogeneous with respect to prognosis. Conclusion: Clinical epidemiologists should consider reporting risk-difference curves in addition to Kaplan-Meier curves and the HR. [ABSTRACT FROM AUTHOR]