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Determining sample size for progression criteria for pragmatic pilot RCTs: the hypothesis test strikes back!

M Lewis1,2, K Bromley3,4, C J Sutton5

  • 1Biostatistics Group, School of Medicine, Keele University, Room 1.111, David Weatherall Building, Keele, Staffordshire, ST5 5BG, UK. a.m.lewis@keele.ac.uk.

Pilot and Feasibility Studies
|February 4, 2021
PubMed
Summary

This study introduces a hypothesis testing framework for pilot randomized controlled trials (RCTs) to evaluate feasibility outcomes. It provides a formal method for sample size determination, enhancing the reliability of pilot study progression criteria.

Keywords:
Outcome and process assessmentPilotsSample size, Statistics

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Area of Science:

  • Clinical Trials Methodology
  • Statistical Inference in Research
  • Pilot Study Design

Background:

  • Current CONSORT guidelines discourage hypothesis testing for clinical outcomes in pilot trials due to insufficient power.
  • Pilot trials primarily focus on descriptive analysis of feasibility and process outcomes like recruitment and adherence.
  • Ambiguity exists regarding appropriate sample sizes for pilot trials, particularly for feasibility outcome evaluation.

Purpose of the Study:

  • To develop a formal hypothesis testing approach for binary feasibility outcomes in pilot randomized controlled trials (RCTs).
  • To establish a method for determining sample size in pilot trials based on predefined progression criteria.
  • To provide a statistical framework for evaluating feasibility outcomes that inform progression to main trials.

Main Methods:

  • A hypothesis testing approach was constructed for binary feasibility outcomes, using a 'traffic light' system (RED, AMBER, GREEN zones).
  • The method tests against an unacceptable (RED zone) outcome, assuming an acceptable (GREEN zone) outcome, to determine sample size with high power.
  • Sample size is calculated to ensure high power to reject the RED zone if the GREEN zone is true, with statistical significance indicating acceptable outcomes.

Main Results:

  • For treatment fidelity, assuming RED zone ≤50% and GREEN zone ≥75%, a sample size of n=34 provides 90% power to detect acceptable fidelity.
  • Observed outcomes in the RED zone (0-50%) are statistically non-significant, AMBER zone (51-74%) may be significant or non-significant, and GREEN zone (75-100%) is significant.
  • This demonstrates how statistical significance can be used to formally evaluate predefined feasibility criteria.

Conclusions:

  • The proposed methodology offers a formal framework for hypothesis testing and sample size indication for process outcome evaluation in pilot RCTs.
  • This approach addresses the ambiguity in sample size determination for pilot trials by linking it to predefined progression criteria.
  • A composite approach assessing multiple process outcomes can be integrated within this formal framework for robust pilot trial evaluation.