Conditional moving linear regression: Modeling the recruitment process for ALLHAT

Dejian Lai, Qiang Zhang, Jose-Miguel Yamal, Paula T. Einhorn and Barry R. Davis

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 18, 8943-8951

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.

Date: 2017
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DOI: 10.1080/03610926.2016.1197251

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