Table 2.

Summary of Innovative Clinical Trial Designs in Heart Failure with Preserved Ejection Fraction

Trial design	Description	When to use	Pros	Cons
Enrichment	An enrichment trial involves assaying the therapeutic target (e.g., a biomarker) or some other factor that is thought to indicate an increased likelihood of responsiveness to the therapy. In this way, the clinical trial can be enriched for patients who are most likely to respond to the treatment.	When there is a clear HFpEF subgroup that can be identified based on a particular biomarker, and this subgroup is thought to be more responsive to the therapy.	Smaller number of randomized patients required than a typical RCT. Depending on study design could be used to determine utility of biomarker or multiple biomarkers.	Requires a way to determine expression of the therapeutic target. In a traditional enrichment trial design (where only the subgroup most likely to respond to the therapy is enrolled), the trial is unable to determine treatment effect for those who test negative for the biomarker.
Adaptive	Adaptive trials involve flexible trial design and the use of accumulated data to change aspects of the trial without undermining the validity and integrity of the trial. Several parameters (e.g., inclusion/exclusion criteria, sample size, drug dose and treatment schedule, endpoints, etc.) can be modified in a pre-specified fashion based on data collected and analyzed during the trial.	When there is a uncertainty regarding the optimal design of the trial, and when data can be collected and analyzed in a continuous fashion during the trial in order to determine whether certain features of the trial can be adapted.	Potentially increased clinical trial speed and efficiency, with resultant lower costs. Fewer patients exposed to harmful treatments. Greater potential chance for showing treatment efficacy.	Requirement of rigorous planning and complex statistical methods Only pre-specified trial adaptations are allowed. Potential difficulties in terms of acceptance of the trial design and results by regulatory authorities and clinicians.
Umbrella	In an umbrella trial design, a variety of targeted treatments are tested in parallel.	When multiple treatment options exist, and when enough patients can be recruited and targeted towards the various treatments in the umbrella trial.	Less screen failures because a variety of trials are available for patients. Enables a more targeted approach that comes closer to the goal of achieving precision medicine. Multiple treatments can be examined simultaneously.	A large number of patients are needed to successfully enroll in the several trials that are being conducted simultaneously. Multiple treatment options must be available.
Basket	Basket trials are focused on the underlying target and not the disease or clinical syndrome per se.	When the therapeutic target is clearly defined and can be assayed in a wide variety of patients with multiple different clinical diseases or syndromes.	Ability to target a molecular or pathophysiologic abnormality shared by several different diseases and clinical syndromes. Agnostic therapeutic approach. May be easier to enroll, larger number of eligible patients.	Different diseases may respond differently to targeting the same underlying molecular or pathophysiologic abnormality. The investigational therapeutic may have off-target effects that may be harmful depending on the disease being treated. Requires ability to precisely test for the therapeutic target May be difficult to determine appropriate trial outcomes.
Machine learning	There are several applications of machine learning to clinical trials, including unsupervised learning to identify patient clusters that may have differential treatment responses; supervised learning which may be able to determine treatment responders; and reinforcement learning, involves a computer learning decision-making by repeatedly walking through win-lose scenarios (such as a patient who does vs. does not respond to a particular therapy).	When a large amount of data collected in a previous trial or current trial is available for analysis.	May provide novel insight into which patients are most likely to respond to the investigational therapeutic.	Requires external validation. Requires a large amount of data and treatment scenarios to train the algorithm effectively in order to make predictions of treatment response.