Statistical considerations for cardiovascular clinical trials straddling the continuum of pandemic phases.

In 2020, the NIH and FDA issued guidance documents that laid the foundation for human subject research during an unprecedented pandemic. To bridge these general considerations to actual applications in cardiovascular interventional device trials, the PAndemic Impact on INTErventional device ReSearch (PAIINTERS) Working Group was formed in early 2021 under the Predictable And Sustainable Implementation Of National CardioVascular Registries (PASSION CV Registries). The PAIINTER's Part I report, published by Rymer et al. [5], provided a comprehensive overview of the operational impact on interventional studies during the first year of the Pandemic. PAIINTERS Part II focused on potential statistical issues related to bias, variability, missing data, and study power when interventional studies may start and end in different pandemic phases. Importantly, the paper also offers practical mitigation strategies to adjust or minimize the impact for both SATs and RCTs, providing a valuable resource for researchers and professionals involved in cardiovascular clinical trials.

A propensity score-integrated approach for leveraging external data in a randomized controlled trial with time-to-event endpoints.

In a randomized controlled trial with time-to-event endpoint, some commonly used statistical tests to test for various aspects of survival differences, such as survival probability at a fixed time point, survival function up to a specific time point, and restricted mean survival time, may not be directly applicable when external data are leveraged to augment an arm (or both arms) of an RCT. In this paper, we propose a propensity score-integrated approach to extend such tests when external data are leveraged. Simulation studies are conducted to evaluate the operating characteristics of three propensity score-integrated statistical tests, and an illustrative example is given to demonstrate how these proposed procedures can be implemented.

Automated Atrial Fibrillation Diagnosis by Echocardiography without ECG: Accuracy and Applications of a New Deep Learning Approach.

BACKGROUND: Automated rhythm detection on echocardiography through artificial intelligence (AI) has yet to be fully realized. We propose an AI model trained to identify atrial fibrillation (AF) using apical 4-chamber (AP4) cines without requiring electrocardiogram (ECG) data. METHODS: Transthoracic echocardiography studies of consecutive patients ≥ 18 years old at our tertiary care centre were retrospectively reviewed for AF and sinus rhythm. The study was first interpreted by level III-trained echocardiography cardiologists as the gold standard for rhythm diagnosis based on ECG rhythm strip and imaging assessment, which was also verified with a 12-lead ECG around the time of the study. AP4 cines with three cardiac cycles were then extracted from these studies with the rhythm strip and Doppler information removed and introduced to the deep learning model ResNet(2+1)D with an 80:10:10 training-validation-test split ratio. RESULTS: 634 patient studies (1205 cines) were included. After training, the AI model achieved high accuracy on validation for detection of both AF and sinus rhythm (mean F1-score = 0.92; AUROC = 0.95). Performance was consistent on the test dataset (mean F1-score = 0.94, AUROC = 0.98) when using the cardiologist's assessment of the ECG rhythm strip as the gold standard, who had access to the full study and external ECG data, while the AI model did not. CONCLUSIONS: AF detection by AI on echocardiography without ECG appears accurate when compared to an echocardiography cardiologist's assessment of the ECG rhythm strip as the gold standard. This has potential clinical implications in point-of-care ultrasound and stroke risk stratification.

The Finkelstein-Schoenfeld Test: A Note on Some Overlooked Issues Concerning Power.

In this note, we express our viewpoint regarding power considerations, via simulation studies, in clinical study design using hierarchical composite endpoint and Finkelstein-Schoenfeld test.

Propensity score-incorporated adaptive design approaches when incorporating real-world data.

The propensity score-integrated composite likelihood (PSCL) method is one method that can be utilized to design and analyze an application when real-world data (RWD) are leveraged to augment a prospectively designed clinical study. In the PSCL, strata are formed based on propensity scores (PS) such that similar subjects in terms of the baseline covariates from both the current study and RWD sources are placed in the same stratum, and then composite likelihood method is applied to down-weight the information from the RWD. While PSCL was originally proposed for a fixed design, it can be extended to be applied under an adaptive design framework with the purpose to either potentially claim an early success or to re-estimate the sample size. In this paper, a general strategy is proposed due to the feature of PSCL. For the possibility of claiming early success, Fisher's combination test is utilized. When the purpose is to re-estimate the sample size, the proposed procedure is based on the test proposed by Cui, Hung, and Wang. The implementation of these two procedures is demonstrated via an example.

Principled leveraging of external data in the evaluation of diagnostic devices via the propensity score-integrated composite likelihood approach.

In the area of diagnostics, it is common practice to leverage external data to augment a traditional study of diagnostic accuracy consisting of prospectively enrolled subjects to potentially reduce the time and/or cost needed for the performance evaluation of an investigational diagnostic device. However, the statistical methods currently being used for such leveraging may not clearly separate study design and outcome data analysis, and they may not adequately address possible bias due to differences in clinically relevant characteristics between the subjects constituting the traditional study and those constituting the external data. This paper is intended to draw attention in the field of diagnostics to the recently developed propensity score-integrated composite likelihood approach, which originally focused on therapeutic medical products. This approach applies the outcome-free principle to separate study design and outcome data analysis and can mitigate bias due to imbalance in covariates, thereby increasing the interpretability of study results. While this approach was conceived as a statistical tool for the design and analysis of clinical studies for therapeutic medical products, here, we will show how it can also be applied to the evaluation of sensitivity and specificity of an investigational diagnostic device leveraging external data. We consider two common scenarios for the design of a traditional diagnostic device study consisting of prospectively enrolled subjects, which is to be augmented by external data. The reader will be taken through the process of implementing this approach step-by-step following the outcome-free principle that preserves study integrity.

Some Thoughts on the Use of Predictive Probability in Clinical Trials.

In this short note, we express our viewpoint regarding declaring study success based on Bayesian predictive probability of study success.

Improving precision and power in randomized trials with a two-stage study design: Stratification using clustering method.

In a randomized controlled trial (RCT), it is possible to improve precision and power and reduce sample size by appropriately adjusting for baseline covariates. There are multiple statistical methods to adjust for prognostic baseline covariates, such as an ANCOVA method. In this paper, we propose a clustering-based stratification method for adjusting for the prognostic baseline covariates. Clusters (strata) are formed only based on prognostic baseline covariates, not outcome data nor treatment assignment. Therefore, the clustering procedure can be completed prior to the availability of outcome data. The treatment effect is estimated in each cluster, and the overall treatment effect is derived by combining all cluster-specific treatment effect estimates. The proposed implementation of the procedure is described. Simulations studies and an example are presented.

Successful treatment of suspected early form of chronic Chagas cardiomyopathy: a case report.

Background: Chagas disease, caused by the protozoan Trypanosoma cruzi, is the most common parasitic aetiology of non-ischaemic cardiomyopathy in the Americas, causing significant morbidity and mortality. The clinical spectrum ranges from early asymptomatic disease to severe cardiac manifestations including dilated cardiomyopathy, heart failure, dysrhythmias, conduction abnormalities, thromboembolism, and sudden death. Case summary: We present a case of Chagas disease in a 75-year-old patient originally from El Salvador who presented to our Canadian tertiary centre with heart failure and atrial fibrillation/flutter. The patient had dilated cardiomyopathy with severely reduced systolic function, which was thought to be early Chagas cardiomyopathy after confirmatory positive serologies for T. cruzi. The patient demonstrated significant clinical improvement and recovery of systolic function with benznidazole therapy that was sustained up to 12 months on follow up. Discussion: The American Heart Association recommends considering treatment of early chronic Chagas cardiomyopathy with anti-trypanosomal therapy. Our case highlights the importance of multidisciplinary collaboration in the diagnosis of early Chagas cardiomyopathy and critical timing of benznidazole, as effectiveness is limited in late disease due to myocardial cell-death programme. Although the historical BENEFIT study is known to not have shown mortality reduction, we advocate that the significant reduction in cardiovascular-related hospitalizations should be considered for symptomatic patients with early Chagas cardiomyopathy with the potential benefit of improving cardiac function and avoiding need for heart transplantation.

Propensity score-integrated approach to survival analysis: leveraging external evidence in single-arm studies.

External data, referred to as data external to the traditional clinical study being planned, include but are not limited to real-world data (RWD) and data collected from clinical studies being conducted in the past or in other countries. The external data are sometimes leveraged to augment a single-arm, prospectively designed study when appropriate. In such an application, recently developed propensity score-integrated approaches including PSPP and PSCL can be used for study design and data analysis when the clinical outcomes are binary or continuous. In this paper, the propensity score-integrated Kaplan-Meier (PSKM) method is proposed for a similar situation but the outcome of interest is time-to-event. The propensity score methodology is used to select external subjects that are similar to those in the current study in terms of baseline covariates and to stratify the selected subjects from both data sources into more homogeneous strata. The stratum-specific PSKM estimators are obtained based on all subjects in the stratum with the external data being down-weighted, and then these estimators are combined to obtain an overall PSKM estimator. A simulation study is conducted to assess the performance of the PSKM method, and an illustrative example is presented to demonstrate how to implement the proposed method.

Estimands in observational studies: Some considerations beyond ICH E9 (R1).

The document ICH E9 (R1) has brought much attention to the concept of estimand in the clinical trials community. ICH stands for International Conference for Harmonization. In this article, we draw attention to one facet of estimand that is not discussed in that document but is crucial in the context of observational studies, namely weighting for covariate balance. How weighting schemes are connected to estimand, or more specifically to one of its five attributes identified in ICH E9 (R1), the attribute of population, is illustrated using the Rubin Causal Model. Three estimands are examined from both theoretical and practical perspectives. Factors that may be considered in choosing among these estimands are discussed.

Leverage multiple real-world data sources in single-arm medical device clinical studies.

The interest in utilizing real-world data (RWD) has been considerably increasing in medical product development and evaluation. With proper usage and analysis of high-quality real-world data, real-world evidence (RWE) can be generated to inform regulatory and healthcare decision-making. This paper proposes a study design and data analysis approach for a prospective, single-arm clinical study that is supplemented with patients from multiple real-world data sources containing patient-level covariate and outcome data. After the amount of information to be borrowed from each real-world data source is determined, the propensity score-integrated composite likelihood method is applied to obtain an estimate of the parameter of interest based on data from the prospective clinical study and this real-world data source. This method is applied to each real-world data source. The final estimate of the parameter of interest is then obtained by taking a weighted average of all these estimates. The performance of the proposed approach is evaluated via a simulation study. A hypothetical example is presented to illustrate how to implement the proposed approach.

Augmenting Both Arms of a Randomized Controlled Trial Using External Data: An Application of the Propensity Score-Integrated Approaches.

Leveraging external data is a topic that have recently received much attention. The propensity score-integrated approaches are a methodological innovation for this purpose. In this paper we adapt these approaches, originally introduced to augment single-arm studies with external data, for the augmentation of both arms of a randomized controlled trial (RCT) with external data. After recapitulating the basic ideas, we provide a step-by-step tutorial of how to implement the propensity score-integrated approaches, from study design to outcome analysis, in the RCT setting in such a way that the study integrity and objectively are maintained. Both the Bayesian (power prior) approach and the frequentist (composite likelihood) approach are included. Some extensions and variations of these approaches are also outlined at the end of this paper.

Propensity score-integrated power prior approach for augmenting the control arm of a randomized controlled trial by incorporating multiple external data sources.

In this paper, a propensity score-integrated power prior approach is developed to augment the control arm of a two-arm randomized controlled trial (RCT) with subjects from multiple external data sources such as real-world data (RWD) and historical clinical studies containing subject-level outcomes and covariates. The propensity scores for the subjects in the external data sources versus the subjects in the RCT are first estimated, and then subjects are placed in different strata based on their estimated propensity scores. Within each propensity score stratum, a power prior is formulated with the information contributed by the external data sources, and Bayesian inference on the treatment effect is obtained. The proposed approach is implemented under the two-stage study design framework utilizing the outcome-free principle to ensure the integrity of a study. An illustrative example is provided to demonstrate the implementation of the proposed approach.

Leveraging real-world evidence for determining performance goals for medical device studies.

Performance goals are numerical target values pertaining to effectiveness or safety endpoints in single-arm medical device clinical studies. Typically, performance goals are determined at the planning stage of the investigational study under consideration based on summarized outcome information from existing relevant clinical trials. In recent years, there is a growing interest in leveraging real-world evidence in medical product development. In this article, we introduce a new method for proposing performance goals by leveraging real-world evidence. The method applies entropy balancing to address possible patient dissimilarities between the study's target patient population and existing real-world patients, and can take into account operation differences between clinical studies and real-world clinical practice. An illustrative example is provided to demonstrate how to implement the proposed method for performance goal determination while leveraging real-world evidence.

Effectiveness of a simple medication adjustment protocol for optimizing peri-cardioversion rate control: A derivation and validation cohort study.

BACKGROUND: Rate control medications are foundational in the management of persistent atrial fibrillation (AF). There are no guidelines for adjusting these medications prior to elective direct-current cardioversion (DCCV). OBJECTIVE: To derive and validate a preprocedural medication adjustment protocol that maintains peri-DCCV rate control and minimizes risk of postconversion bradycardia, pauses, need for pacing, and cardiopulmonary resuscitation (CPR). METHODS: Consecutive patients with persistent AF awaiting elective DCCV across 2 hospitals were screened for inclusion into derivation, validation, and control cohorts. In the derivation cohort, each patient taking an atrioventricular (AV) nodal blocker had medications adjusted based on heart rate (HR) 2 days before DCCV, and the magnitude of dose adjustment was compared with peri-DCCV HR. The adjustment protocol that achieved the highest percentage of optimal peri-DCCV rate control was tested prospectively in the validation cohort and compared to a standard-of-care control group. RESULTS: The optimal protocol from the derivation cohort (n = 71), based on the 2-day pre-DCCV HR, was to (1) CONTINUE AV nodal blocker for HR ≥ 100 beats per minute (bpm), (2) reduce dose by ONE increment when 80-99 bpm, (3) reduce dose by TWO increments when 60-79 bpm, and (4) HOLD when <60 bpm. In the prospective validation cohort (n = 106), this protocol improved peri-DCCV rate control (82% vs 62%, P < .001) compared to current standard of care (n = 107). There were no conversion pauses ≥5 seconds, need for pacing, or CPR post-DCCV. CONCLUSION: This simple preprocedural medication adjustment protocol provides an effective strategy of optimizing peri-DCCV rate control in patients with AF.

Evaluation of diagnostic tests for low prevalence diseases: a statistical approach for leveraging real-world data to accelerate the study.

The evaluation of diagnostic tests usually involves statistical inference for its sensitivity. As sensitivity is defined as the probability that the test result will be positive when the target condition is present, the key study design consideration of sample size is the determination of the number of subjects with the target condition such that the estimation has adequate precision, or the hypothesis testing has adequate power. Traditionally, one may rely on prospective screening of subjects to obtain the required sample size, which means that if the prevalence of the disease is very low, a large number of subjects would need to be screened, increasing the study duration and cost. In this paper, we consider the possibility of substantially reducing the length and cost of a clinical study by leveraging subjects from a real-world data (RWD) source, focusing specifically on the diagnostic test for the cancer of interest. Using the propensity score methodology, we developed a procedure which ensures that the real-world subjects being leveraged are similar to their prospectively enrolled counterparts, thereby making the leveraging more justified. The procedure allows the down-weighting of the real-world subjects, which can be achieved by either using a Frequentist's method based on the composite likelihood or a Bayesian method based on the power prior. The proposed approach can be applied to the evaluation of any diagnostic test and it is not limited to the current clinical study regarding a cancer diagnostic test. Notably, this paper is in close alignment with a recently released draft framework by the Medical Device Innovation Consortium (MDIC) on real-world clinical evidence and in vitro diagnostics, being a showcase of appropriately leveraging real-world data in diagnostic test evaluation for diseases with low prevalence to support regulatory decision-making.