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

BACKGROUND: The current CONSORT guidelines for reporting pilot trials do not recommend hypothesis testing of clinical outcomes on the basis that a pilot trial is under-powered to detect such differences and this is the aim of the main trial. It states that primary evaluation should focus on descript...

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Autores principales: Lewis, M., Bromley, K., Sutton, C. J., McCray, G., Myers, H. L., Lancaster, G. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856754/
https://www.ncbi.nlm.nih.gov/pubmed/33536076
http://dx.doi.org/10.1186/s40814-021-00770-x
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author Lewis, M.
Bromley, K.
Sutton, C. J.
McCray, G.
Myers, H. L.
Lancaster, G. A.
author_facet Lewis, M.
Bromley, K.
Sutton, C. J.
McCray, G.
Myers, H. L.
Lancaster, G. A.
author_sort Lewis, M.
collection PubMed
description BACKGROUND: The current CONSORT guidelines for reporting pilot trials do not recommend hypothesis testing of clinical outcomes on the basis that a pilot trial is under-powered to detect such differences and this is the aim of the main trial. It states that primary evaluation should focus on descriptive analysis of feasibility/process outcomes (e.g. recruitment, adherence, treatment fidelity). Whilst the argument for not testing clinical outcomes is justifiable, the same does not necessarily apply to feasibility/process outcomes, where differences may be large and detectable with small samples. Moreover, there remains much ambiguity around sample size for pilot trials. METHODS: Many pilot trials adopt a ‘traffic light’ system for evaluating progression to the main trial determined by a set of criteria set up a priori. We construct a hypothesis testing approach for binary feasibility outcomes focused around this system that tests against being in the RED zone (unacceptable outcome) based on an expectation of being in the GREEN zone (acceptable outcome) and choose the sample size to give high power to reject being in the RED zone if the GREEN zone holds true. Pilot point estimates falling in the RED zone will be statistically non-significant and in the GREEN zone will be significant; the AMBER zone designates potentially acceptable outcome and statistical tests may be significant or non-significant. RESULTS: For example, in relation to treatment fidelity, if we assume the upper boundary of the RED zone is 50% and the lower boundary of the GREEN zone is 75% (designating unacceptable and acceptable treatment fidelity, respectively), the sample size required for analysis given 90% power and one-sided 5% alpha would be around n = 34 (intervention group alone). Observed treatment fidelity in the range of 0–17 participants (0–50%) will fall into the RED zone and be statistically non-significant, 18–25 (51–74%) fall into AMBER and may or may not be significant and 26–34 (75–100%) fall into GREEN and will be significant indicating acceptable fidelity. DISCUSSION: In general, several key process outcomes are assessed for progression to a main trial; a composite approach would require appraising the rules of progression across all these outcomes. This methodology provides a formal framework for hypothesis testing and sample size indication around process outcome evaluation for pilot RCTs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40814-021-00770-x.
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spelling pubmed-78567542021-02-04 Determining sample size for progression criteria for pragmatic pilot RCTs: the hypothesis test strikes back! Lewis, M. Bromley, K. Sutton, C. J. McCray, G. Myers, H. L. Lancaster, G. A. Pilot Feasibility Stud Methodology BACKGROUND: The current CONSORT guidelines for reporting pilot trials do not recommend hypothesis testing of clinical outcomes on the basis that a pilot trial is under-powered to detect such differences and this is the aim of the main trial. It states that primary evaluation should focus on descriptive analysis of feasibility/process outcomes (e.g. recruitment, adherence, treatment fidelity). Whilst the argument for not testing clinical outcomes is justifiable, the same does not necessarily apply to feasibility/process outcomes, where differences may be large and detectable with small samples. Moreover, there remains much ambiguity around sample size for pilot trials. METHODS: Many pilot trials adopt a ‘traffic light’ system for evaluating progression to the main trial determined by a set of criteria set up a priori. We construct a hypothesis testing approach for binary feasibility outcomes focused around this system that tests against being in the RED zone (unacceptable outcome) based on an expectation of being in the GREEN zone (acceptable outcome) and choose the sample size to give high power to reject being in the RED zone if the GREEN zone holds true. Pilot point estimates falling in the RED zone will be statistically non-significant and in the GREEN zone will be significant; the AMBER zone designates potentially acceptable outcome and statistical tests may be significant or non-significant. RESULTS: For example, in relation to treatment fidelity, if we assume the upper boundary of the RED zone is 50% and the lower boundary of the GREEN zone is 75% (designating unacceptable and acceptable treatment fidelity, respectively), the sample size required for analysis given 90% power and one-sided 5% alpha would be around n = 34 (intervention group alone). Observed treatment fidelity in the range of 0–17 participants (0–50%) will fall into the RED zone and be statistically non-significant, 18–25 (51–74%) fall into AMBER and may or may not be significant and 26–34 (75–100%) fall into GREEN and will be significant indicating acceptable fidelity. DISCUSSION: In general, several key process outcomes are assessed for progression to a main trial; a composite approach would require appraising the rules of progression across all these outcomes. This methodology provides a formal framework for hypothesis testing and sample size indication around process outcome evaluation for pilot RCTs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40814-021-00770-x. BioMed Central 2021-02-03 /pmc/articles/PMC7856754/ /pubmed/33536076 http://dx.doi.org/10.1186/s40814-021-00770-x Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology
Lewis, M.
Bromley, K.
Sutton, C. J.
McCray, G.
Myers, H. L.
Lancaster, G. A.
Determining sample size for progression criteria for pragmatic pilot RCTs: the hypothesis test strikes back!
title Determining sample size for progression criteria for pragmatic pilot RCTs: the hypothesis test strikes back!
title_full Determining sample size for progression criteria for pragmatic pilot RCTs: the hypothesis test strikes back!
title_fullStr Determining sample size for progression criteria for pragmatic pilot RCTs: the hypothesis test strikes back!
title_full_unstemmed Determining sample size for progression criteria for pragmatic pilot RCTs: the hypothesis test strikes back!
title_short Determining sample size for progression criteria for pragmatic pilot RCTs: the hypothesis test strikes back!
title_sort determining sample size for progression criteria for pragmatic pilot rcts: the hypothesis test strikes back!
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7856754/
https://www.ncbi.nlm.nih.gov/pubmed/33536076
http://dx.doi.org/10.1186/s40814-021-00770-x
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