Conditional moving linear regression: Modeling the recruitment process for ALLHAT

ABSTRACT

Effective recruitment is a prerequisite for successful execution of a clinical trial. ALLHAT, a large hypertension treatment trial (N = 42,418), provided an opportunity to evaluate adaptive modeling of recruitment processes using conditional moving linear regression. Our statistical modeling of recruitment, comparing Brownian and fractional Brownian motion, indicates that fractional Brownian motion combined with moving linear regression is better than classic Brownian motion in terms of higher conditional probability of achieving a global recruitment goal in 4-week ahead projections. Further research is needed to evaluate how recruitment modeling can assist clinical trialists in planning and executing clinical trials.

KEYWORDS:

• ALLHAT
• Brownian motion
• Fractional Brownian motion
• Recruitment
• prediction.

MATHEMATICS SUBJECT CLASSIFICATION:

• 62M10
• 62P10

Acknowledgments

The authors thank Dr. Ellen Breckenridge, The University of Texas School of Public Health, for providing editorial assistance in the preparation of this manuscript.

Declaration of interest

The authors report no financial conflicts of interest.

Funding

This study was supported by contracts NO1-HC-35130 and HHSN268201100036C with the National Heart, Lung, and Blood Institute. The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack trial investigators acknowledge study medications contributed by Pfizer, Inc. (amlodipine and doxazosin), AstraZeneca (atenolol and lisinopril), and Bristol-Myers Squibb (pravastatin), and financial support provided by Pfizer, Inc.