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Conditional moving linear regression: Modeling the recruitment process for ALLHAT

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Listed:
  • Dejian Lai
  • Qiang Zhang
  • Jose-Miguel Yamal
  • Paula T. Einhorn
  • Barry R. Davis

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.

Suggested Citation

  • Dejian Lai & Qiang Zhang & Jose-Miguel Yamal & Paula T. Einhorn & Barry R. Davis, 2017. "Conditional moving linear regression: Modeling the recruitment process for ALLHAT," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(18), pages 8943-8951, September.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:18:p:8943-8951
    DOI: 10.1080/03610926.2016.1197251
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