Cited by 280Statistical Methods in Clinical TrialsStatistical Methods in EpidemiologyMeta-analysis and systematic reviews

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index; none of the 45 sources its claims cite is checked for support yet.

Guidance for using pilot studies to inform the design of intervention trials with continuous outcomes

Open access
Bell, Melanie L, Whitehead, Amy L, Julious, Steven A·2018·Clinical Epidemiology

Abstract

BACKGROUND: A pilot study can be an important step in the assessment of an intervention by providing information to design the future definitive trial. Pilot studies can be used to estimate the recruitment and retention rates and population variance and to provide preliminary evidence of efficacy potential. However, estimation is poor because pilot studies are small, so sensitivity analyses for the main trial's sample size calculations should be undertaken. METHODS: We demonstrate how to carry out easy-to-perform sensitivity analysis for designing trials based on pilot data using an example. Furthermore, we introduce rules of thumb for the size of the pilot study so that the overall sample size, for both pilot and main trials, is minimized. RESULTS: The example illustrates how sample size estimates for the main trial can alter dramatically by plausibly varying assumptions. Required sample size for 90% power varied from 392 to 692 depending on assumptions. Some scenarios were not feasible based on the pilot study recruitment and retention rates. CONCLUSION: Pilot studies can be used to help design the main trial, but caution should be exercised. We recommend the use of sensitivity analyses to assess the robustness of the design assumptions for a main trial.

Overview

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Written from the paper’s full text.

TL;DR

Pilot studies can inform the design of definitive trials with continuous outcomes by estimating recruitment/retention, population variance, and preliminary efficacy potential, but their small size makes estimates imprecise. The paper recommends sensitivity analyses and stepped rules of thumb for pilot sample size, illustrated by an example where required main-trial sample size for 90% power ranged from 392 to 692 depending on assumptions.

Key findings

  • In the worked example, main-trial sample size for 90% power varied from 392 to 692 depending on assumptions about the SD and dropout rate; for 80% power, sample sizes ranged from 296 to 518.
  • The pilot example had a recruitment rate of 50% of eligible patients, at 25 recruitments per month, and 80% retention (40 of 50 participants completed all three assessments).
  • Based on a target standardized effect size of 4/14 = 0.29 for a 90% powered main trial, the stepped rules of thumb indicated 25 patients per arm for the pilot.
  • Feasibility assessment showed that recruiting over 1.5 years at 25 participants per month would enroll 450 participants, falling below several sample size estimates in the sensitivity analysis.

Methods

  • Methodological guidance paper with a worked example using a continuous outcome; it focuses on external pilot studies whose results are not combined with the main trial.
  • The example is a hypothetical two-arm RCT comparing a new supportive care regimen for cancer patients to usual care, with quality of life at 3 months measured by FACT-G; the pilot had 50 participants, with 100 potential participants, 70 referred, 60 eligible, and 50 agreeing to participate.
  • Main analysis demonstrates sample size sensitivity analyses varying the SD (pilot SD, upper 80% CI limit, literature estimate) and dropout rates (observed 20%, >5%, <5%), plus stepped rules of thumb for pilot sample size based on target effect size and future trial size.

Limitations

  • Stated: pilot studies are small, so effect size and SD estimates are imprecise; using a pilot study’s SD to design a future sample size has been shown to often result in an underpowered study, and preliminary efficacy evidence from a pilot should not be overstated.
  • Stated: sensitivity analyses are needed because sample size estimates can alter dramatically by plausibly varying assumptions, and some scenarios may not be feasible based on pilot recruitment and retention rates.
  • Evident from the text: the worked example is explicitly hypothetical (“Suppose…”), so its numerical results are illustrative rather than empirical findings from an actual pilot study.

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Integrity signals

Registry

Crossref · 10.2147/clep.s146397

Cites

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Figures

Figure 1 Mean difference in FACT-G scores between pilot study intervention and control arms with confidence intervals. Abbreviations: FACT-G, Functional Assessment of Cancer Therapy-Genera; MID, minimum important difference.
Figure 1 Mean difference in FACT-G scores between pilot study intervention and control arms with confidence intervals. Abbreviations: FACT-G, Functional Assessment of Cancer Therapy-Genera; MID, minimum important difference.CC BY-NC · PubMed Central

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Metadata

Authors
Bell, M. L., Whitehead, A. L., & Julious, S. A.
Affiliations
  • University of Arizona (US)
  • University of Sheffield (GB)
Corresponding author
Bell, Melanie L
Venue
Clinical Epidemiology
Published
2018-01
Volume
Volume 10
Pages
153-157
ISSN
1179-1349 (ISSN-L)
OpenAlex
W2783709266
Cited by
280
References
32
Citations by year
2026: 37 · 2025: 52 · 2024: 22 · 2023: 48 · 2022: 43 · 2021: 37 · 2020: 26 · 2019: 12 · 2018: 3
FWCI
16.18
Type
journal article
Language
en
Open access
Locations
  • Clinical Epidemiology (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
  • ePrints Soton (University of Southampton) (acceptedVersion)
  • UA Campus Repository (The University of Arizona) (acceptedVersion, cc-by-nc)
  • Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
Funders
  • University of Southampton
  • National Cancer Institute (P30CA023074)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords
pilot study, trial design, sample size determination, sensitivity analysis, continuous outcomes, population variance estimation

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