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

Abstract

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. The discussed quality process is tailored to the confirmatory study using a 2-stage design with an emphasis on the interaction process among stakeholders. In an example, the quality process covers a 2-step assessment of the similarity in patient characteristics between the current study and the external data source, and between the treatment and augmented control groups.

Keywords

randomized controlled trial external data 2-stage design independent statistician 2-step assessment quality process

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References

Yue

. Designing premarket observational comparative studies using existing data as controls: challenges and opportunities. J Biopharm Stat. 2014;24:994–1010.

Yue

Campbell

Zuckerman

. Utilizing national and international registries to enhance pre-market medical device regulatory evaluation. J Biopharm Stat. 2016;26:1136–1145.

Mukhi

Yue

. A note on good practice of objective propensity score design for premarket nonrandomized medical device studies with an example. Stat Biopharm Res. 2016;8:282–286.

Yue

. Premarket applications: registries. In: Chow

, ed. Encyclopedia of Biopharmaceutical Statistics. 4th ed. Boca Raton, FL: Chapman and Hall/CRC; 2018.

Guidance for Industry and Food and Drug Administration Staff. Use of real world evidence to support regulatory decision making for medical devices. https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm513027.pdf. Published August 31, 2017.

Austin

. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28:3083–3107.

Fong

Ratkovic

Imai

Hazlett

Yang

Peng

. Package “CBPS.” https://cran.r-project.org/web/packages/CBPS/CBPS.pdf. Published June 17, 2018.

Helmreich

Pruzek

. PSAgraphics: an R package to support propensity score analysis. J Stat Software. 2009;29(6). http://www.jstatsoft.org/