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.

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.

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.

Propensity score-based methods for causal inference and external data leveraging in regulatory settings: From basic ideas to implementation.

The breakthrough propensity score methodology was formulated by Rosenbaum and Rubin in the 1980s for the mitigation of confounding bias in non-randomized comparative studies to facilitate causal inference for treatment effects. The methodology had been used mainly in epidemiological and social science studies that may often be exploratory, until it was adopted by FDA/CDRH in 2002 and applied in the evaluation of medical device pre-market confirmatory studies, including those with a control group extracted from a well-designed and executed registry database or historical clinical studies. Around 2013, following the Rubin outcome-free study design principle, the two-stage propensity score design framework was developed for medical device studies to safeguard study integrity and objectivity, thereby strengthening the interpretability of study results.

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.

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.

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.

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.

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.

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.

Propensity score-integrated composite likelihood approach for augmenting the control arm of a randomized controlled trial by incorporating real-world data.

In this paper, a propensity score-integrated composite likelihood (PSCL) approach is developed for cases in which the control arm of a two-arm randomized controlled trial (RCT) (treated vs control) is augmented with patients from real-world data (RWD) containing both clinical outcomes and covariates at the patient-level. RWD patients who were treated with the same therapy as the control arm of the RCT are considered for the augmentation. The PSCL approach first estimates the propensity score for every patient as the probability of the patient being in the RCT rather than the RWD, and then stratifies all patients into strata based on the estimated propensity scores.

A Study Design for Augmenting the Control Group in a Randomized Controlled Trial: A Quality Process for Interaction Among Stakeholders.

There is an increasing demand for utilization of external data, such as historical study data and patient registry data, to augment the control group in a randomized controlled trial. While such a study design could reduce the time and cost, how to maintain the study validity and integrity is one major statistical challenge that needs to be carefully addressed. We discuss a study design quality process to enhance the study validity and integrity when using this approach.

Propensity score-integrated composite likelihood approach for incorporating real-world evidence in single-arm clinical studies.

In medical product development, there has been an increased interest in utilizing real-world data which have become abundant with recent advances in biomedical science, information technology, and engineering. High-quality real-world data may be analyzed to generate real-world evidence that can be utilized in the regulatory and healthcare decision-making. In this paper, we consider the case in which a single-arm clinical study, viewed as the primary data source, is supplemented with patients from a real-world data source containing both clinical outcome and covariate data at the patient-level.

Homogeneity assessment for pivotal medical device clinical studies.

A question that routinely arises in medical device clinical studies is the homogeneity across demographic subgroups, geographical regions, or investigational sites of the enrolled patients in terms of treatment effects or outcome variables. The main objective of this paper is to discuss statistical concepts and methods for the assessment of such homogeneity and to provide the practitioner a statistical framework and points to consider in conducting homogeneity assessment. Demographic subgroups, geographical regions, and investigational sites are discussed separately as each has its unique issues.

Propensity score-integrated power prior approach for incorporating real-world evidence in single-arm clinical studies.

We are now at an amazing time for medical product development in drugs, biological products and medical devices. As a result of dramatic recent advances in biomedical science, information technology and engineering, ``big data'' from health care in the real-world have become available. Although big data may not necessarily be attuned to provide the preponderance of evidence to a clinical study, high-quality real-world data can be transformed into scientific evidence for regulatory and healthcare decision-making using proven analytical methods and techniques, such as propensity score methodology and Bayesian inference.

Good statistical practice in utilizing real-world data in a comparative study for premarket evaluation of medical devices.

Observational (non-randomized) comparative studies have been adopted in the pre-market safety/effectiveness evaluation of medical devices. There has been an increased interest in utilizing this design with the growing available real-world data. However, in such studies, biases that are introduced in every stage and aspect of study need to be addressed.

Statistical innovations in diagnostic device evaluation.

Due to rapid technological development, innovations in diagnostic devices are proceeding at an extremely fast pace. Accordingly, the needs for adopting innovative statistical methods have emerged in the evaluation of diagnostic devices. Statisticians in the Center for Devices and Radiological Health at the Food and Drug Administration have provided leadership in implementing statistical innovations.

Utilizing national and international registries to enhance pre-market medical device regulatory evaluation.

Regulatory decisions are made based on the assessment of risk and benefit of medical devices at the time of pre-market approval and subsequently, when post-market risk-benefit balance needs reevaluation. Such assessments depend on scientific evidence obtained from pre-market studies, post-approval studies, post-market surveillance studies, patient perspective information, as well as other real world data such as national and international registries. Such registries provide real world evidence and are playing a more and more important role in enhancing the safety and effectiveness evaluation of medical devices.

Statistical innovations in the medical device world sparked by the FDA.

The world of medical devices while highly diverse is extremely innovative, and this facilitates the adoption of innovative statistical techniques. Statisticians in the Center for Devices and Radiological Health (CDRH) at the Food and Drug Administration (FDA) have provided leadership in implementing statistical innovations. The innovations discussed include: the incorporation of Bayesian methods in clinical trials, adaptive designs, the use and development of propensity score methodology in the design and analysis of non-randomized observational studies, the use of tipping-point analysis for missing data, techniques for diagnostic test evaluation, bridging studies for companion diagnostic tests, quantitative benefit-risk decisions, and patient preference studies.

Designing premarket observational comparative studies using existing data as controls: challenges and opportunities.

Due to the special nature of medical device clinical studies, observational (nonrandomized) comparative studies play important roles in the premarket safety/effectiveness evaluation of medical devices. While historical data collected in earlier investigational device exemption studies of a previously approved medical device have been used to form control groups in comparative studies, high-quality registry data are emerging to provide opportunities for the premarket evaluation of new devices. However, in such studies, various biases could be introduced in every stage and aspect of study and may compromise the objectivity of study design and validity of study results.

Regulatory issues of propensity score methodology application to drug and device safety studies.

While randomized, well-controlled, clinical trials have been viewed as the gold standard in the evaluation of medical products, including drugs, biological products, and medical devices, it is not uncommon for safety assessment to be performed using observational studies, for ethical or practical reasons. In observational studies, various biases could be introduced in every stage and aspect of study, and consequently the resulting statistical inference may carry a lower level of scientific assurance, compared to randomized trials. To ensure the objectivity of study design and interpretability of the results, it is critical to address the challenges of such studies.