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Background: The coronavirus disease 2019 pandemic highlighted the need to conduct efficient randomized clinical trials with interim monitoring guidelines for efficacy and futility. Several randomized coronavirus disease 2019 trials, including the Multiplatform Randomized Clinical Trial (mpRCT), used Bayesian guidelines with the belief that they would lead to quicker efficacy or futility decisions than traditional "frequentist" guidelines, such as spending functions and conditional power. We explore this belief using an intuitive interpretation of Bayesian methods as translating prior opinion about the treatment effect into imaginary prior data. These imaginary observations are then combined with actual observations from the trial to make conclusions. Using this approach, we show that the Bayesian efficacy boundary used in mpRCT is actually quite similar to the frequentist Pocock boundary. Methods: The mpRCT's efficacy monitoring guideline considered stopping if, given the observed data, there was greater than 99% probability that the treatment was effective (odds ratio greater than 1). The mpRCT's futility monitoring guideline considered stopping if, given the observed data, there was greater than 95% probability that the treatment was less than 20% effective (odds ratio less than 1.2). The mpRCT used a normal prior distribution that can be thought of as supplementing the actual patients' data with imaginary patients' data. We explore the effects of varying probability thresholds and the prior-to-actual patient ratio in the mpRCT and compare the resulting Bayesian efficacy monitoring guidelines to the well-known frequentist Pocock and O'Brien–Fleming efficacy guidelines. We also contrast Bayesian futility guidelines with a more traditional 20% conditional power futility guideline. Results: A Bayesian efficacy and futility monitoring boundary using a neutral, weakly informative prior distribution and a fixed probability threshold at all interim analyses is more aggressive than the commonly used O'Brien–Fleming efficacy boundary coupled with a 20% conditional power threshold for futility. The trade-off is that more aggressive boundaries tend to stop trials earlier, but incur a loss of power. Interestingly, the Bayesian efficacy boundary with 99% probability threshold is very similar to the classic Pocock efficacy boundary. Conclusions: In a pandemic where quickly weeding out ineffective treatments and identifying effective treatments is paramount, aggressive monitoring may be preferred to conservative approaches, such as the O'Brien–Fleming boundary. This can be accomplished with either Bayesian or frequentist methods. [ABSTRACT FROM AUTHOR]

Background: Clinical trials are increasingly using Bayesian methods for their design and analysis. Inference in Bayesian trials typically uses simulation-based approaches such as Markov Chain Monte Carlo methods. Markov Chain Monte Carlo has high computational cost and can be complex to implement. The Integrated Nested Laplace Approximations algorithm provides approximate Bayesian inference without the need for computationally complex simulations, making it more efficient than Markov Chain Monte Carlo. The practical properties of Integrated Nested Laplace Approximations compared to Markov Chain Monte Carlo have not been considered for clinical trials. Using data from a published clinical trial, we aim to investigate whether Integrated Nested Laplace Approximations is a feasible and accurate alternative to Markov Chain Monte Carlo and provide practical guidance for trialists interested in Bayesian trial design. Methods: Data from an international Bayesian multi-platform adaptive trial that compared therapeutic-dose anticoagulation with heparin to usual care in non-critically ill patients hospitalized for COVID-19 were used to fit Bayesian hierarchical generalized mixed models. Integrated Nested Laplace Approximations was compared to two Markov Chain Monte Carlo algorithms, implemented in the software JAGS and stan, using packages available in the statistical software R. Seven outcomes were analysed: organ-support free days (an ordinal outcome), five binary outcomes related to survival and length of hospital stay, and a time-to-event outcome. The posterior distributions for the treatment and sex effects and the variances for the hierarchical effects of age, site and time period were obtained. We summarized these posteriors by calculating the mean, standard deviations and the 95% equitailed credible intervals and presenting the results graphically. The computation time for each algorithm was recorded. Results: The average overlap of the 95% credible interval for the treatment and sex effects estimated using Integrated Nested Laplace Approximations was 96% and 97.6% compared with stan, respectively. The graphical posterior densities for these effects overlapped for all three algorithms. The posterior mean for the variance of the hierarchical effects of age, site and time estimated using Integrated Nested Laplace Approximations are within the 95% credible interval estimated using Markov Chain Monte Carlo but the average overlap of the credible interval is lower, 77%, 85.6% and 91.3%, respectively, for Integrated Nested Laplace Approximations compared to stan. Integrated Nested Laplace Approximations and stan were easily implemented in clear, well-established packages in R, while JAGS required the direct specification of the model. Integrated Nested Laplace Approximations was between 85 and 269 times faster than stan and 26 and 1852 times faster than JAGS. Conclusion: Integrated Nested Laplace Approximations could reduce the computational complexity of Bayesian analysis in clinical trials as it is easy to implement in R, substantially faster than Markov Chain Monte Carlo methods implemented in JAGS and stan, and provides near identical approximations to the posterior distributions for the treatment effect. Integrated Nested Laplace Approximations was less accurate when estimating the posterior distribution for the variance of hierarchical effects, particularly for the proportional odds model, and future work should determine if the Integrated Nested Laplace Approximations algorithm can be adjusted to improve this estimation. [ABSTRACT FROM AUTHOR]

Consider the choice of outcome for overall treatment benefit in a clinical trial which measures the first time to each of several clinical events. We describe several new variants of the win ratio that incorporate the time spent in each clinical state over the common follow‐up, where clinical state means the worst clinical event that has occurred by that time. One version allows restriction so that death during follow‐up is most important, while time spent in other clinical states is still accounted for. Three other variants are described; one is based on the average pairwise win time, one creates a continuous outcome for each participant based on expected win times against a reference distribution and another that uses the estimated distributions of clinical state to compare the treatment arms. Finally, a combination testing approach is described to give robust power for detecting treatment benefit across a broad range of alternatives. These new methods are designed to be closer to the overall treatment benefit/harm from a patient's perspective, compared to the ordinary win ratio. The new methods are compared to the composite event approach and the ordinary win ratio. Simulations show that when overall treatment benefit on death is substantial, the variants based on either the participants' expected win times (EWTs) against a reference distribution or estimated clinical state distributions have substantially higher power than either the pairwise comparison or composite event methods. The methods are illustrated by re‐analysis of the trial heart failure: a controlled trial investigating outcomes of exercise training. [ABSTRACT FROM AUTHOR]

A study was conducted to evaluate the efficacy and safety of using exercise training in improving the condition of patients with chronic heart failure. Results indicated that exercise training was effective in modest significant reductions in hospitalization and mortality associated with cardiac failure.

Background: The GUIDE-IT trial was, a multicenter, randomized, parallel group, unblinded study that randomized patients to having heart failure therapy titrated to achieve an NT-proBNP <1000 pg/mL or to usual clinical care.Methods and Results: We performed pre-specified subgroup analysis to look for the race and ethnicity-based differences in clinical outcomes of patients who were able to achieve GDMT or target NT-proBNP concentration of ≤1000 pg/mL at 90 days of follow-up. There were 894 patients enrolled in GUIDE-IT study. Of these, 733 participants had available data on 90-day guideline directed triple therapy and 616 on NT-proBNP. 35% of the patients were Black and 6% were Hispanic. Black patients were younger, had more comorbidities, lower EF, and higher NYHA class compared with non-Black. Adjusting for 90-day NT-proBNP and important baseline covariates, Black patients were at a higher risk than non-Black patients for HF hospitalization [HR, 2.19; 95% CI, 1.51-3.17; p < 0.0001], but at a similar risk for mortality [HR, 0.85.; 95% CI, 0.44-1.66; p = 0.64]. Similar results were seen adjusting for 90-day GDMT [HF hospitalization: Black vs non-Black, HR: 1.97; 1.41-2.77, P < 0.0001; mortality: HR: 0.70; 0.39-1.26, p = 0.23]. There were no significant differences between Hispanic and non-Hispanic patients with respect to heart failure hospitalization, cardiovascular or all-cause mortality. Over the study period, Black and Hispanic patients experienced smaller changes in physical function and quality of life as measured by the Kansas City Cardiomyopathy Questionnaire overall score.Conclusion: Compared to non-Black patients, Black patients in GUIDE-IT study had a higher risk of heart failure hospitalization, but a comparable risk of mortality, despite improved use of GDMT and achievement of similar biomarker targets. [ABSTRACT FROM AUTHOR]

Aims: N‐terminal pro‐b‐type natriuretic peptide (NT‐proBNP) values may be influenced by patient factors beyond the severity of illness, including atrial fibrillation (AF), renal dysfunction, or increased body mass index (BMI). We hypothesized that these factors may influence the achievement of NT‐proBNP targets and clinical outcomes. Methods: A total of 894 patients with heart failure with reduced ejection fraction were enrolled in The Guiding Evidence‐Based Therapy Using Biomarker Intensified Treatment trial. NT‐proBNP was analysed every 3 months. Results: Forty per cent of patients had AF, the median estimated glomerular filtration rate (eGFR) was 59 mL/min/1.73 m2 [interquartile range (IQR) 43–76], and median BMI was 29 kg/m2 (IQR 25–34). Patients with AF, eGFR < 60 mL/min/1.73 m2, or a BMI < 29 kg/m2 had a higher level of NT‐proBNP at randomization and over all study visits (all P values < 0.001). Over 18 months, the rate of change of NT‐proBNP was less for patients with AF (compared with those without AF, P = 0.037) and patients with an eGFR < 60 mL/min/1.73 m2 (compared with eGFR > 60 mL/min/1.73 m2, P < 0.001). The rate of change of NT‐proBNP was similar for patients with a BMI above or below the median value. Using the 90 day NT‐proBNP, patients with AF, lower eGFR, or lower BMI were less likely to achieve the target NT‐proBNP < 1000 pg/mL than patients without AF, higher eGFR, or higher BMI, respectively. None of these differed between the Usual Care or Guided Care arm for AF, eGFR, or BMI (Pinteractions all NS). Conclusions: Patients with AF, a lower BMI, or worse renal function are less likely to achieve a lower or target NT‐proBNP. Clinicians should be aware of these factors both when interpreting NT‐proBNP levels and making therapeutic decisions about heart failure therapies. [ABSTRACT FROM AUTHOR]

Background/aims: The two-by-two factorial design randomizes participants to receive treatment A alone, treatment B alone, both treatments A and B(AB), or neither treatment (C). When the combined effect of A and B is less than the sum of the A and B effects, called a subadditive interaction, there can be low power to detect the A effect using an overall test, that is, factorial analysis, which compares the A and AB groups to the C and B groups. Such an interaction may have occurred in the Action to Control Cardiovascular Risk in Diabetes blood pressure trial (ACCORD BP) which simultaneously randomized participants to receive intensive or standard blood pressure, control and intensive or standard glycemic control. For the primary outcome of major cardiovascular event, the overall test for efficacy of intensive blood pressure control was nonsignificant. In such an instance, simple effect tests of A versus C and B versus C may be useful since they are not affected by a subadditive interaction, but they can have lower power since they use half the participants of the overall trial. We investigate multiple testing procedures which exploit the overall tests' sample size advantage and the simple tests' robustness to a potential interaction. Methods: In the time-to-event setting, we use the stratified and ordinary logrank statistics' asymptotic means to calculate the power of the overall and simple tests under various scenarios. We consider the A and B research questions to be unrelated and allocate 0.05 significance level to each. For each question, we investigate three multiple testing procedures which allocate the type 1 error in different proportions for the overall and simple effects as well as the AB effect. The Equal Allocation 3 procedure allocates equal type 1 error to each of the three effects, the Proportional Allocation 2 procedure allocates 2/3 of the type 1 error to the overall A (respectively, B) effect and the remaining type 1 error to the AB effect, and the Equal Allocation 2 procedure allocates equal amounts to the simple A (respectively, B) and AB effects. These procedures are applied to ACCORD BP. Results: Across various scenarios, Equal Allocation 3 had robust power for detecting a true effect. For ACCORD BP, all three procedures would have detected a benefit of intensive glycemia control. Conclusions: When there is no interaction, Equal Allocation 3 has less power than a factorial analysis. However, Equal Allocation 3 often has greater power when there is an interaction. The R package factorial2x2 can be used to explore the power gain or loss for different scenarios. [ABSTRACT FROM AUTHOR]

Background: Most heart failure (HF) risk scores have been derived from cohorts of stable HF patients and may not incorporate up to date treatment regimens or deep phenotype characterization that change baseline risk over the short- and long-term follow-up period. We undertook the current analysis of participants in the GUIDE-IT (Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment) trial to address these limitations.Methods and Results: The GUIDE-IT study randomized 894 high-risk patients with HF and reduced ejection fraction (≤ 40%) to biomarker-guided treatment strategy vs. usual care. We performed risk modelling using Cox proportional hazards models and analysed the relationship between 35 baseline clinical factors and the primary composite endpoint of cardiovascular (CV) death or HF hospitalization, the secondary endpoint of all-cause mortality, and the exploratory endpoint of 90-day HF hospitalization or death. Prognostic relationships for continuous variables were examined and key predictors were identified using a backward variable selection process. Predictive models and risk scores were developed. Over a median follow-up of 15 months, the cumulative number of HF hospitalizations and CV deaths was 328 out of 894 patients (Kaplan-Meier event rate 34.5% at 12 months). Frequency of all-cause deaths was 143 out of 894 patients (Kaplan-Meier event rate 12.2% at 12 months). Outcomes for the primary and secondary endpoints between strategy arms of the study were similar. The most important predictor that was present in all three models was the baseline natriuretic peptide level. Hispanic ethnicity, low sodium and high heart rate were present in two of the three models. Other important predictors included the presence or absence of a device, New York Heart Association class, HF duration, black race, co-morbidities (sleep apnoea, elevated creatinine, ischaemic heart disease), low blood pressure, and a high congestion score.Conclusion: Risk models using readily available clinical information are able to accurately predict short- and long-term CV events and may be useful in optimizing care and enriching patients for clinical trials.Clinical Trial Registration: ClinicalTrials.gov ID number NCT01685840. [ABSTRACT FROM AUTHOR]

Assessing coronary artery calcium (CAC) is a valuable tool for individualizing cardiac risk assessment. In CAC scanning, this technical report assesses the use of a true model-based iterative reconstruction algorithm using forward projected model-based iterative reconstruction ("FIRST") and assess whether FIRST allows for reduced radiation dose CAC scanning on 320-detector row computed tomography (320-CT). Here, 100 consecutive patients prospectively underwent reduced and standard dose scans. For the patients (59 ± 9 years, 61% male) stratified by Agatston categories 0, 1-10, 11-100, 101-400,> 400, agreement between reduced dose with FIRST versus standard dose with FBP was excellent at 81% (95% CI: 73-88%) with kappa 0.74 (95% CI: 0.64-0.85). Median radiation exposure was 75% lower for reduced (0.35 mSv) versus standard dose (1.37 mSv) scans. In conclusion, agreement was excellent for reduced dose with FIRST and standard dose with FBP in 320-detector row CT CAC imaging in well-established categories of cardiovascular risk. These methods make it possible to reduce radiation exposure by 75%. [ABSTRACT FROM AUTHOR]

We investigate different primary efficacy analysis approaches for a 2-armed randomized clinical trial when interest is focused on a time to event primary outcome that is subject to a competing risk. We extend the work of Friedlin and Korn (2005) by considering estimation as well as testing and by simulating the primary and competing events' times from both a cause-specific hazards model as well as a joint subdistribution-cause-specific hazards model. We show that the cumulative incidence function can provide useful prognostic information for a particular patient but is not advisable for the primary efficacy analysis. Instead, it is preferable to fit a Cox model for the primary event which treats the competing event as an independent censoring. This is reasonably robust for controlling type I error and treatment effect bias with respect to the true primary and competing events' cause-specific hazards model, even when there is a shared, moderately prognostic, unobserved baseline frailty for the primary and competing events in that model. However, when it is plausible that a strongly prognostic frailty exists, combining the primary and competing events into a composite event should be considered. Finally, when there is an a priori interest in having both the primary and competing events in the primary analysis, we compare a bivariate approach for establishing overall treatment efficacy to the composite event approach. The ideas are illustrated by analyzing the Women's Health Initiative clinical trials sponsored by the National Heart, Lung, and Blood Institute. [ABSTRACT FROM AUTHOR]