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Meta-regression of randomized control trials with antithrombotics: weak correlation between net clinical benefit and all cause-mortality.

ABSTRACT: This study aimed to explore the validity of the use of the net clinical benefit (NCB), i.e. the sum of major bleeding and thrombotic events, as a potential surrogate for all-cause mortality in clinical trials assessing antithrombotics. Published randomized controlled trials testing anticoagulants in the prevention or treatment of venous thromboembolism (VTE) and non-valvular atrial fibrillation (NVAF) were systematically reviewed. The validity of NCB as a surrogate endpoint was estimated by calculating the strength of correlation of determination (R²) and its 95% confidence interval (CI) between the relative risks of NCB and all-cause mortality. Amongst the 125 trials retrieved, the highest R²_trial values were estimated for NVAF (R²_trial = 0.41, 95% CI [0.03; 0.48]), and acute VTE (R²_trial = 0.30, 95% CI [0.04; 0.84]). Conversely, the NCB did not correlate with all-cause mortality in prevention studies with medical (R²_trial = 0.12, 95% CI [0.00; 0.36]), surgical (R²_trial = 0.05, 95% CI [0.00; 0.23]), and cancer patients (R²_trial = 0.006, 95% CI [0.00; 1.00]). A weak correlation between NCB and all cause-mortality was found in NVAF and acute VTE, whereas no correlation was observed in clinical situations where the mortality rate was low. Consequently, NCB should not be considered a surrogate outcome for all cause-mortality in anticoagulation trials.

SUBMITTER: Kilo R

PROVIDER: S-EPMC8290002 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:To examine whether the benefit of statins varied according to cardiovascular (CV) and non-CV mortality of the treated population.Meta-analysis and meta-regression of 16 randomized placebo-controlled trials.Community and hospital.Statin- (n = 59,671) and placebo-treated (n = 59,707) individuals with and without CV disease (mean age 55 to 75).Meta-regression was used to model relative risks (RRs) of major CV events (myocardial infarction and stroke) and total mortality for statins versus placebo as a function of CV and non-CV mortality risks of the study population.Every 1% increase in 5-year non-CV mortality risk of the study population was associated with a 3.7% (95% confidence interval (CI) = 1.2 to 6.3%) greater RR of major CV events and a 4.4% (95% CI = 2.1 to 6.9%) greater RR of total mortality. (Higher RRs indicate smaller benefits.) CV mortality was not associated with statin effects (P > .05). In stratified analysis according to CV (?5.3% vs <5.3%) and non-CV mortality (?3.8% vs <3.8%) of the study population, statins had little mortality benefit in populations with high non-CV mortality, regardless of CV mortality (random-effects pooled RR = 0.81, 95% CI = 0.72 to 0.91, for low CV and low non-CV mortality; random-effects pooled RR = 0.90, 95% CI = 0.76 to 1.06 for low CV and high non-CV mortality; random-effects pooled RR = 0.79, 95% CI = 0.72 to 0.87 for high CV and low non-CV mortality; random-effects pooled RR = 0.94, 95% CI = 0.87 to 1.02 for high CV and high non-CV mortality). The CV event reduction was also attenuated in populations with high non-CV mortality (random-effects pooled RR = 0.67, 95% CI = 0.60 to 0.75, for low CV and low non-CV mortality; random-effects pooled RR = 0.73, 95% CI = 0.66 to 0.81 for low CV and high non-CV mortality; random-effects pooled RR = 0.77, 95% CI = 0.69 to 0.87 for high CV and low non-CV mortality; random-effects pooled RR = 0.83, 95% CI = 0.74 to 0.92 for high CV and high non-CV mortality).Benefits of statins may depend on the non-CV mortality risk of the treated population. This should be confirmed using individual-level data.

Project description:BackgroundHistorically, a priori power and sample size calculations have not been routinely performed cost-effectiveness analyses (CEA), partly because the absence of published cost and effectiveness correlation and variance data, which are essential for power and sample size calculations. Importantly, the empirical correlation between cost and effectiveness has not been examined with respect to the estimation of value-for-money in clinical literature. Therefore, it is not well established if cost-effectiveness studies embedded within randomized-controlled-trials (RCTs) are under- or over-powered to detect changes in value-for-money. However, recently guidelines (such as those from ISPOR) and funding agencies have suggested sample size and power calculations should be considered in CEAs embedded in clinical trials.MethodsWe examined all RCTs conducted by the Canadian Cancer Trials Group with an embedded cost-effectiveness analysis. Variance and correlation of effectiveness and costs were derived from original-trial data. The incremental net benefit method was used to calculate the power of the cost-effectiveness analysis, with exploration of alternative correlation and willingness-to-pay values.ResultsWe identified four trials for inclusion. We observed that a hypothetical scenario of correlation coefficient of zero between cost and effectiveness led to a conservative estimate of sample size. The cost-effectiveness analysis was under-powered to detect changes in value-for-money in two trials, at willingness-to-pay of $100,000. Based on our observations, we present six considerations for future economic evaluations, and an online program to help analysts include a priori sample size and power calculations in future clinical trials.ConclusionThe correlation between cost and effectiveness had a potentially meaningful impact on the power and variance of value-for-money estimates in the examined cost-effectiveness analyses. Therefore, the six considerations and online program, may facilitate a priori power calculations in embedded cost-effectiveness analyses in future clinical trials.

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Meta-regression of randomized control trials with antithrombotics: weak correlation between net clinical benefit and all cause-mortality.

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