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Dynamic data-enabled stratified sampling for trial invitations with application in NHS-Galleri.

Adam R Brentnall1, Chris Mathews2, Sandy Beare2

  • 1Wolfson Institute of Population Health, Centre for Evaluation and Methods, Queen Mary University of London, London, UK.

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|April 25, 2023
PubMed
Summary
This summary is machine-generated.

A new data-enabled algorithm targets health research invitations to reduce the healthy volunteer effect and improve participant equity. This strategy helps ensure study results are more representative of the general population.

Keywords:
Cancerequityinvitationsrecruitmentscreening

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Area of Science:

  • Health Services Research
  • Clinical Trial Recruitment
  • Health Equity

Background:

  • Participants in health research, like cancer screening trials, often exhibit better health than the general population, potentially biasing results.
  • Healthy volunteer effects can reduce statistical power and skew findings in clinical studies.
  • Data-driven recruitment strategies are crucial for minimizing bias and enhancing equity in health research.

Purpose of the Study:

  • To develop and implement a data-enabled algorithm for targeted trial invitations.
  • To address and mitigate the 'healthy volunteer effect' in health research.
  • To promote equity in participant recruitment by ensuring representation from diverse societal and ethnic groups.

Main Methods:

  • A computer algorithm was designed using a linear programming model to optimize invitation numbers across different demographic groups and recruitment sites.
  • The algorithm considers distinct recruitment clusters (e.g., general practitioners, geographical areas) and population strata (e.g., age, sex bands).
  • It dynamically solves the optimization problem, incorporating public data for objective function weights and constraints.

Main Results:

  • The algorithm was applied to the NHS-Galleri multi-cancer screening trial, aiming to recruit 140,000 participants in England.
  • Invitations were sampled based on algorithm-generated lists, with sampling distributions adjusted to favor underrepresented groups for equity.
  • The approach incorporated a minimum expected event rate for the primary outcome to counteract healthy volunteer effects.

Conclusions:

  • The developed invitation algorithm represents a novel, data-enabled approach to enhance health research recruitment.
  • This strategy effectively addresses challenges related to healthy volunteer bias and participant inequity.
  • The algorithm's framework is adaptable for application in various other clinical trials and research studies.