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Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine
Power analysis informs a priori planning of behavioral and medical research, including for randomized clinical trials that are nomothetic (i.e., studies designed to infer results to the general population based on interindividual variabilities). Far fewer investigations and resources are available f...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9757638/ https://www.ncbi.nlm.nih.gov/pubmed/36526885 http://dx.doi.org/10.3758/s13428-022-02012-1 |
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author | Tueller, Stephen Ramirez, Derek Cance, Jessica D. Ye, Ai Wheeler, Anne C. Fan, Zheng Hornik, Christoph Ridenour, Ty A. |
author_facet | Tueller, Stephen Ramirez, Derek Cance, Jessica D. Ye, Ai Wheeler, Anne C. Fan, Zheng Hornik, Christoph Ridenour, Ty A. |
author_sort | Tueller, Stephen |
collection | PubMed |
description | Power analysis informs a priori planning of behavioral and medical research, including for randomized clinical trials that are nomothetic (i.e., studies designed to infer results to the general population based on interindividual variabilities). Far fewer investigations and resources are available for power analysis of clinical trials that follow an idiographic approach, which emphasizes intraindividual variabilities between baseline (control) phase versus one or more treatment phases. We tested the impact on statistical power to detect treatment outcomes of four idiographic trial design factors that are under researchers’ control, assuming a multiple baseline design: sample size, number of observations per participant, proportion of observations in the baseline phase, and competing statistical models (i.e., hierarchical modeling versus piecewise regression). We also tested the impact of four factors that are largely outside of researchers’ control: population size, proportion of intraindividual variability due to residual error, treatment effect size, and form of outcomes during the treatment phase (phase jump versus gradual change). Monte Carlo simulations using all combinations of the factors were sampled with replacement from finite populations of 200, 1750, and 3500 participants. Analyses characterized the unique relative impact of each factor individually and all two-factor combinations, holding all others constant. Each factor impacted power, with the greatest impact being from larger treatment effect sizes, followed respectively by more observations per participant, larger samples, less residual variance, and the unexpected improvement in power associated with assigning closer to 50% of observations to the baseline phase. This study’s techniques and R package better enable a priori rigorous design of idiographic clinical trials for rare diseases, precision medicine, and other small-sample studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-022-02012-1. |
format | Online Article Text |
id | pubmed-9757638 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-97576382022-12-19 Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine Tueller, Stephen Ramirez, Derek Cance, Jessica D. Ye, Ai Wheeler, Anne C. Fan, Zheng Hornik, Christoph Ridenour, Ty A. Behav Res Methods Article Power analysis informs a priori planning of behavioral and medical research, including for randomized clinical trials that are nomothetic (i.e., studies designed to infer results to the general population based on interindividual variabilities). Far fewer investigations and resources are available for power analysis of clinical trials that follow an idiographic approach, which emphasizes intraindividual variabilities between baseline (control) phase versus one or more treatment phases. We tested the impact on statistical power to detect treatment outcomes of four idiographic trial design factors that are under researchers’ control, assuming a multiple baseline design: sample size, number of observations per participant, proportion of observations in the baseline phase, and competing statistical models (i.e., hierarchical modeling versus piecewise regression). We also tested the impact of four factors that are largely outside of researchers’ control: population size, proportion of intraindividual variability due to residual error, treatment effect size, and form of outcomes during the treatment phase (phase jump versus gradual change). Monte Carlo simulations using all combinations of the factors were sampled with replacement from finite populations of 200, 1750, and 3500 participants. Analyses characterized the unique relative impact of each factor individually and all two-factor combinations, holding all others constant. Each factor impacted power, with the greatest impact being from larger treatment effect sizes, followed respectively by more observations per participant, larger samples, less residual variance, and the unexpected improvement in power associated with assigning closer to 50% of observations to the baseline phase. This study’s techniques and R package better enable a priori rigorous design of idiographic clinical trials for rare diseases, precision medicine, and other small-sample studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-022-02012-1. Springer US 2022-12-16 /pmc/articles/PMC9757638/ /pubmed/36526885 http://dx.doi.org/10.3758/s13428-022-02012-1 Text en © The Psychonomic Society, Inc. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Tueller, Stephen Ramirez, Derek Cance, Jessica D. Ye, Ai Wheeler, Anne C. Fan, Zheng Hornik, Christoph Ridenour, Ty A. Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine |
title | Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine |
title_full | Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine |
title_fullStr | Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine |
title_full_unstemmed | Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine |
title_short | Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine |
title_sort | power analysis for idiographic (within-subject) clinical trials: implications for treatments of rare conditions and precision medicine |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9757638/ https://www.ncbi.nlm.nih.gov/pubmed/36526885 http://dx.doi.org/10.3758/s13428-022-02012-1 |
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