Conditional moving linear regression: Modeling the recruitment process for ALLHAT |
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| Authors: | Dejian Lai Qiang Zhang Jose-Miguel Yamal Paula T Einhorn Barry R Davis |
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| Institution: | 1. Coordinating Center for Clinical Trials, Biostatistics Division, The University of Texas School of Public Health, Houston, Texas, USA;2. NRG Oncology Statistics and Data Management Center, Philadelphia, Pennsylvania, USA;3. Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA |
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| 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. |
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| Keywords: | ALLHAT Brownian motion Fractional Brownian motion Recruitment prediction |
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