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

Self Cite

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. Since 2018, the scope of the propensity score methodology has been broadened so that it can be used for the purpose of leveraging external data to augment a single-arm or randomized traditional clinical study. All these statistical approaches, collectively referred to as propensity score-based methods in this article, have been considered in the design of medical device regulatory studies and stimulated related research, as evidenced by the latest trends in journal publications. We will provide a tutorial on the propensity score-based methods from the basic idea to their implementation in regulatory settings for causal inference and external data leveraging, along with step-by-step descriptions of the procedures of the two-stage outcome-free design through examples, which can be used as templates for real study proposals.

Section: Discussionmentioning

confidence: 99%

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

Yue

2023

Self Cite

“…Here, we assume that the external data to be leveraged are from a single source. Generalization to the scenario where there are multiple external data sources can be found in Lu et al 10 The PS so defined is still a balancing score, which means that, as in the case of comparative studies, when the PSs are balanced between the external subjects and the current study subjects, all the observed covariates are balanced in expectation across the two groups. To take advantage of this balancing property, one can form strata in which estimated PSs are relatively homogeneous, so that within each PS stratum the distribution of observed covariates among the external subjects is close to that among subjects in the current study, and balance for all covariates within each stratum is therefore expected.…”

Section: Propensity Score and Composite Likelihoodmentioning

confidence: 99%

“…The scheme that we propose is for the investigator of the study to identify an independent statistician to perform the PS design to whom no outcome data are provided. The independent statistician shares with the investigator the responsibility of upholding the outcome‐free principle 9–14 . Given their important role, the independent statistician needs to be identified in the study proposal.…”

Section: Propensity Score and Composite Likelihoodmentioning

confidence: 99%

See 1 more Smart Citation

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

Song

Chen

et al. 2023

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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.

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

Chen,

Lu,

Wang

et al. 2024

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.