Medical Device Clinical Studies Research Articles

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.

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

ABSTRACT 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.

Homogeneity assessment for pivotal medical device clinical studies

ABSTRACTA 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. Specific considerations are also given to randomized controlled trials, non-randomized comparative studies, and single-arm studies. We point out that judicious use of statistical methods, in conjunction with sound clinical judgment, is essential in handling the issue of homogeneity of treatment effect in medical device clinical studies.

Sex Differences in Utilisation and Response to Implantable Device Therapy.

Multiple studies have demonstrated that implantable cardioverter-defibrillators (ICDs) and cardiac resynchronisation therapy (CRT) provide significant mortality and morbidity benefits to eligible patients irrespective of gender. However, female patients are less likely to receive this life-saving therapy and are significantly under-represented in cardiac device trials. Various performance improvement programmes have proved that this gender disparity can be reduced and these therapies should be offered to all eligible patients regardless of sex. Efforts should be made to enrol more women in clinical trials and sex-specific analysis in medical device clinical studies should be encouraged. In this article we review the data on sex differences in clinical outcomes with ICDs and CRT and explore the reasons for this sex-based disparity.

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. In this article, challenges and opportunities in the design of such studies using propensity score methodology are discussed from regulatory perspectives.

Why research sex differences and similarities?

Why research sex differences and similarities?

Evaluating Sex Differences in Medical Device Clinical Trials

THE SAFETY AND EFFECTIVENESS OF MEDICAL TREATments can differ in men and women for many reasons related to different epidemiologic characteristics, physiology, and body size. In general, women have higher bleeding rates and procedural morbidity and mortality than men, which means that their risk/benefit ratios for many implanted medical devices can differ from men. Therefore, sex-specific safety and effectiveness data are necessary for informed patient decision making. In 2008, the US Food and Drug Administration (FDA) convened 2 workshops on this topic that included multiple stakeholders— patient groups, medical device industry representatives, academia, and government officials. In December 2011, the FDA released a draft guidance informed by these workshops, entitled “Evaluation of Sex Differences in Medical Device Clinical Studies.” This document discusses the underrepresentation of women in medical device clinical trials and provides recommendations for increasing enrollment of women and performing sex-specific analyses. A 90-day public comment period began on December 19, 2011. The FDA draft guidance document provides a number of helpful guidelines to support adequate enrollment of women in clinical trials. For studies that have not yet been completed, the draft guidance recommends keeping enrollment open until a prespecified number of women are enrolled. Following this recommendation so that women are enrolled in proportion to disease prevalence would do the most to ensure sufficient data about the effect of a medical device on women. The draft guidance document also recommends that outcomes data be analyzed by sex. By not limiting its guidance to new studies, the FDA is indicating that it hopes changes will occur more immediately. This approach is laudable; the difference between men and women in terms of effectiveness and safety of medical devices has long been understudied. The recommendations in this draft guidance may help ensure safe and effective use of medical devices in both sexes. However, the notation on this guidance stating that it “Contains Nonbinding Recommendations” raises the fundamental question of whether these important guidelines will be followed. This is not a new question. In 1994, FDA issued a “Gender Bias” directive stating that FDA Summaries of Safety and Effectiveness Data, which are released on premarket approval for all high-risk medical devices, should examine clinical trial data for differences in safety and efficacy by sex. However, an examination of compliance with this directive found that it is routinely ignored. Among 78 high-risk cardiovascular devices approved during the years 2000 through 2007, the overall patient population in these studies was 67% men with no increase in the enrollment of women over time, although women make up 46% of all patients with cardiovascular disease. The analyses by sex specified in the 1994 FDA directive occurred in only 41% of studies (51/123). When these analyses were completed, 26% (12/47) identified some difference in device safety or effectiveness by sex. In 28% of device studies (34/123), no sex distribution of the patient population was stated. As a result, clinically important sex-specific differences related to use of many medical devices are unknown. For example, the implantable cardioverter-defibrillator (ICD), a device shown to be lifesaving in selected populations, is commonly implanted in men and women. At the time of device approval, there were gaps in the data because of very low enrollment of women in the pivotal studies. In 2003 the Medicare Coverage and Advisory Committee was convened pursuant to a manufacturer’s request for expanded Medicare coverage of ICDs for primary prevention (which was granted). During the meeting, when asked why there were so few women in the pivotal Multicenter Automatic Defibrillator Implantation Trial II, the trial’s principal investigator stated, “we are contemplating a trial in the

Special Issue on Medical Device Clinical Studies – Guest Editor's Note

A medical device is an item for treating or diagnosing a health condition whose intended use is not achieved primarily by chemical or biological action within the body (Section 201(h) of the Federa...

Statistical and Regulatory Issues in Nonrandomized Medical Device Clinical Studies

While randomized, well-controlled, clinical trials have been viewed as the gold standard in the evaluation of medical products, it is not uncommon for medical device clinical studies to depart from the paradigm of randomized trials, due to ethical or practical reasons. In nonrandomized studies, the advantages of well-designed and conducted randomized clinical trials are no longer available, and consequently the statistical inference obtained from such studies may carry a lower level of scientific assurance, compared to randomized trials. This paper provides a brief overview of nonrandomized medical device clinical studies in terms of design and statistical analysis as well as regulatory issues, including some challenges that frequently arise in those endeavors.

Design Effects for Highly Clustered Count Data with Varying Length of Follow-up – Applying to the Analysis of ICD Shocks

Highly clustered count data are commonly seen in medical device clinical studies such as cardiac rhythm management. For instance, anti-arrhythmia shocks delivered from an implantable cardioverter-defibrillator (ICD) often occur as “storms”, i.e., multiple shocks within a short period of time. There are unique challenges in designing and analyzing clinical studies with such response data. In general, there is less amount of data information available when events occur as “storms” as compared to independent events, and the efficiency loss may be substantial in practice. In addition, event incidences of subjects may be highly unbalanced and length of follow-up may vary from subject to subject. In this paper, the design effect for such highly clustered and unbalanced count data is studied. Varying length of subject follow-up is taken into account by the use of a random variable that may depend on external covariates. Sample size adjustment for event correlation and imbalance is discussed. The practical application of the strategy is illustrated through a clinical study of ICD shock therapy.

Discussion of: Statistical and Regulatory Issues with the Application of Propensity Score Analysis to Nonrandomized Medical Device Clinical Studies by Lilly Q. Yue

We thank the author for a nice review and discussion on the current application of the propensity score analysis on medical device clinical studies. Nonrandomized clinical studies are commonly seen...

Discussion of: Statistical and Regulatory Issues with the Application of Propensity Score Analysis to Nonrandomized Medical Device Clinical Studies

Discussion of: Statistical and Regulatory Issues with the Application of Propensity Score Analysis to Nonrandomized Medical Device Clinical Studies

Statistical and Regulatory Issues with the Application of Propensity Score Analysis to Nonrandomized Medical Device Clinical Studies

Propensity score analysis is a versatile statistical method used mainly in observational studies for improving treatment comparison by adjusting for up to a relatively large number of potentially confounding covariates. Recently, there has been an increased interest in applying this method to nonrandomized medical device clinical studies. In the application of the methodology, some statistical and regulatory issues arise in both study design and analysis of study results, such as the need for pre-specifying clinically relevant covariates to be measured, appropriate patient populations, and the essential elements of statistical analysis, planning sample size in the context of propensity score methodology, handling missing covariates in generating propensity scores, and assessing the success of the propensity score method by evaluating treatment group overlap in terms of the distributions of propensity scores. In this paper, the advantages and limitations of this methodology will be revisited, and the above issues will be discussed and illustrated with examples from a regulatory perspective.

Discussion of: Statistical and Regulatory Issues with the Application of Propensity Score Analysis to Nonrandomized Medical Device Clinical Studies

Discussion of: Statistical and Regulatory Issues with the Application of Propensity Score Analysis to Nonrandomized Medical Device Clinical Studies

Discussion of: Statistical and Regulatory Issues with the Application of Propensity Score Analysis to Nonrandomized Medical Device Clinical Studies

As indicated by Dr. Yue, nonrandomized studies are more common in medical device studies than in drug and biological product studies. Informative analyses of such registries compared to prior histo...

Discussion of: Statistical and Regulatory Issues with the Application of Propensity Score Analysis to Nonrandomized Medical Device Clinical Studies

Lilly Yue's article on Statistical and Regulatory Issues with the Application of Propensity Score Analysis to nonrandomized Medical Device Clinical Studies provides a nice overview of some of the i...

The FDA's role in medical device clinical studies of human subjects

This paper provides an overview of the United States Food and Drug Administration's (FDA) role as a regulatory agency in medical device clinical studies involving human subjects. The FDA's regulations and responsibilities are explained and the device application process discussed. The specific medical device regulatory authorities are described as they apply to the development and clinical study of retinal visual prosthetic devices. The FDA medical device regulations regarding clinical studies of human subjects are intended to safeguard the rights and safety of subjects. The data gathered in pre-approval clinical studies provide a basis of valid scientific evidence in order to demonstrate the safety and effectiveness of a medical device. The importance of a working understanding of applicable medical device regulations from the beginning of the device development project is emphasized particularly for novel, complex products such as implantable visual prosthetic devices.

EQUIVALENCE IN TEST ASSAY METHOD COMPARISONS FOR THE REPEATED-MEASURE, MATCHED-PAIR DESIGN IN MEDICAL DEVICE STUDIES: STATISTICAL CONSIDERATIONS1*

In medical device clinical studies, including therapeutic device and in vitro test assay method studies, the investigator is frequently interested in demonstrating the equivalence of clinical response, generally in continuous measurements, between a standard assay method and a new assay method over various occasions or times. The new assay method may be less invasive or more convenient or cheaper to use than the standard assay method. In this paper, several statistical approaches are discussed, including various repeated-measure regression models, the simultaneous 95% confidence interval for paired mean differences derived from Hotelling's multivariate T 2 analysis for repeated-measure, paired data, repeatability and reproducibility studies, and concordance correlation coefficient. * The views expressed are those of the author, and not necessarily those of the U.S. Food and Drug Administration.

Statistical considerations for survival analysis from medical device clinical studies.

Appropriate statistical methods are presented to estimate the 95% lower confidence limit of true event-free probability, with heavy censoring in the small sample size situation, and to compare crude probability, linearized rate, and life-table probability. The problem of survival experience extrapolation beyond the last observed follow-up is also discussed.