Heart Failure Spending Function: An Investment Framework for Sequencing and Intensification of Guideline-Directed Medical Therapies

Heart failure with reduced ejection fraction is managed with increasing numbers of guideline-directed medical therapies (GDMT). Benefits tend to be additive. Burdens can also be additive. We propose a heart failure spending function as a conceptual framework for tailored intensification of GDMT that maximizes therapeutic opportunity while limiting adverse events and patient burden. Each patient is conceptualized to have reserve in physiological and psychosocial domains, which can be spent for a future return on investment. Key domains are blood pressure, heart rate, serum creatinine, potassium, and out-of-pocket costs. For each patient, GDMT should be initiated and intensified in a sequence that prioritizes medications with the greatest expected cardiac benefit while drawing on areas where the patient has ample reserves. When reserve is underspent, patients fail to gain the full benefit of GDMT. Conversely, when a reserve is fully spent, addition of new drugs or higher doses that draw upon a domain will lead to patient harm. The benefit of multiple agents drawing upon varied physiological domains should be balanced against cost and complexity. Thresholds for overspending are explored, as are mechanisms for implementing these concepts into routine care, but further health care delivery research is needed to validate and refine clinical use of the spending function. The heart failure spending function also suggests how newer therapies may be considered in terms of relative value, prioritizing agents that draw on different spending domains from existing GDMT.

An Audit of Interim Analyses of Randomized Controlled Trials [RCTs] Published in Three High Impact Factor Medical Journals over a seven-year period [2012-2018]

Background: An interim analysis is an integral component of clinical research and drug development in particular and helps reduce ‘time to market’ for intervention or stop further development of unsafe and ineffective interventions. In this audit, we evaluated the extent of the use of interim analyses in published RCTs in three leading journals and their impact on regulatory approval. Methodology: RCTs published in JAMA, NEJM, and Lancet in the year 2012 to 2018 were extracted. Each RCT was scrutinized using the filter term ‘Interim’. Both descriptive and inferential statistics were used to analyse the data. The factors [therapeutic areas, nature of interventions, source of funding, and phases of trials] associated with Interim analysis and its impact on drug approval were analysed. Results: The majority of RCTs with interim analysis belonged to oncology [27%] and cardiology [17.2%] and were related to drugs [70%]. The majority of the RCTs were in phase 3 [56.3%] and funded exclusively by the Pharmaceutical industry [36.2%]. A total of 2% and 14% of studies lead to accelerated approval and normal regulatory approval. The choice of alpha spending function was not mentioned in 44.8% of studies, and 21% of studies used the O-Brien Fleming method. A total of 18.5% of studies were stopped early. The oncology trials, drug as intervention, and Phase 3 trials were associated with the conduct of interim analysis, which was associated with significantly higher numbers of accelerated and routine regulatory approvals. Conclusion: The majority of the RCTs with interim analysis were from oncology, and most did not report a stopping rule. Interventions that were drugs [rather than devices or surgical procedures] and phase 3 trials [relative to other phases of RCTs] were associated with a significantly higher number of interim analyses which was also associated with a significantly higher number of regulatory approvals.

Comparison of Bayesian and frequentist group-sequential clinical trial designs

Background There is a growing interest in the use of Bayesian adaptive designs in late-phase clinical trials. This includes the use of stopping rules based on Bayesian analyses in which the frequentist type I error rate is controlled as in frequentist group-sequential designs. Methods This paper presents a practical comparison of Bayesian and frequentist group-sequential tests. Focussing on the setting in which data can be summarised by normally distributed test statistics, we evaluate and compare boundary values and operating characteristics. Results Although Bayesian and frequentist group-sequential approaches are based on fundamentally different paradigms, in a single arm trial or two-arm comparative trial with a prior distribution specified for the treatment difference, Bayesian and frequentist group-sequential tests can have identical stopping rules if particular critical values with which the posterior probability is compared or particular spending function values are chosen. If the Bayesian critical values at different looks are restricted to be equal, O’Brien and Fleming’s design corresponds to a Bayesian design with an exceptionally informative negative prior, Pocock’s design to a Bayesian design with a non-informative prior and frequentist designs with a linear alpha spending function are very similar to Bayesian designs with slightly informative priors.This contrasts with the setting of a comparative trial with independent prior distributions specified for treatment effects in different groups. In this case Bayesian and frequentist group-sequential tests cannot have the same stopping rule as the Bayesian stopping rule depends on the observed means in the two groups and not just on their difference. In this setting the Bayesian test can only be guaranteed to control the type I error for a specified range of values of the control group treatment effect. Conclusions Comparison of frequentist and Bayesian designs can encourage careful thought about design parameters and help to ensure appropriate design choices are made.