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The effect of berberine on insulin resistance in women with polycystic ovary syndrome: detailed statistical analysis plan (SAP) for a multicenter randomized controlled trial
BACKGROUND: Although Traditional Chinese Medicine (TCM) has been widely used in clinical settings, a major challenge that remains in TCM is to evaluate its efficacy scientifically. This randomized controlled trial aims to evaluate the efficacy and safety of berberine in the treatment of patients wit...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5073878/ https://www.ncbi.nlm.nih.gov/pubmed/27769284 http://dx.doi.org/10.1186/s13063-016-1633-5 |
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author | Zhang, Ying Sun, Jin Zhang, Yun-Jiao Chai, Qian-Yun Zhang, Kang Ma, Hong-Li Wu, Xiao-Ke Liu, Jian-Ping |
author_facet | Zhang, Ying Sun, Jin Zhang, Yun-Jiao Chai, Qian-Yun Zhang, Kang Ma, Hong-Li Wu, Xiao-Ke Liu, Jian-Ping |
author_sort | Zhang, Ying |
collection | PubMed |
description | BACKGROUND: Although Traditional Chinese Medicine (TCM) has been widely used in clinical settings, a major challenge that remains in TCM is to evaluate its efficacy scientifically. This randomized controlled trial aims to evaluate the efficacy and safety of berberine in the treatment of patients with polycystic ovary syndrome. In order to improve the transparency and research quality of this clinical trial, we prepared this statistical analysis plan (SAP). METHODS: The trial design, primary and secondary outcomes, and safety outcomes were declared to reduce selection biases in data analysis and result reporting. We specified detailed methods for data management and statistical analyses. Statistics in corresponding tables, listings, and graphs were outlined. DISCUSSION: The SAP provided more detailed information than trial protocol on data management and statistical analysis methods. Any post hoc analyses could be identified via referring to this SAP, and the possible selection bias and performance bias will be reduced in the trial. TRIAL REGISTRATION: This study is registered at ClinicalTrials.gov, NCT01138930, registered on 7 June 2010. |
format | Online Article Text |
id | pubmed-5073878 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50738782016-10-26 The effect of berberine on insulin resistance in women with polycystic ovary syndrome: detailed statistical analysis plan (SAP) for a multicenter randomized controlled trial Zhang, Ying Sun, Jin Zhang, Yun-Jiao Chai, Qian-Yun Zhang, Kang Ma, Hong-Li Wu, Xiao-Ke Liu, Jian-Ping Trials Update BACKGROUND: Although Traditional Chinese Medicine (TCM) has been widely used in clinical settings, a major challenge that remains in TCM is to evaluate its efficacy scientifically. This randomized controlled trial aims to evaluate the efficacy and safety of berberine in the treatment of patients with polycystic ovary syndrome. In order to improve the transparency and research quality of this clinical trial, we prepared this statistical analysis plan (SAP). METHODS: The trial design, primary and secondary outcomes, and safety outcomes were declared to reduce selection biases in data analysis and result reporting. We specified detailed methods for data management and statistical analyses. Statistics in corresponding tables, listings, and graphs were outlined. DISCUSSION: The SAP provided more detailed information than trial protocol on data management and statistical analysis methods. Any post hoc analyses could be identified via referring to this SAP, and the possible selection bias and performance bias will be reduced in the trial. TRIAL REGISTRATION: This study is registered at ClinicalTrials.gov, NCT01138930, registered on 7 June 2010. BioMed Central 2016-10-21 /pmc/articles/PMC5073878/ /pubmed/27769284 http://dx.doi.org/10.1186/s13063-016-1633-5 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. |
spellingShingle | Update Zhang, Ying Sun, Jin Zhang, Yun-Jiao Chai, Qian-Yun Zhang, Kang Ma, Hong-Li Wu, Xiao-Ke Liu, Jian-Ping The effect of berberine on insulin resistance in women with polycystic ovary syndrome: detailed statistical analysis plan (SAP) for a multicenter randomized controlled trial |
title | The effect of berberine on insulin resistance in women with polycystic ovary syndrome: detailed statistical analysis plan (SAP) for a multicenter randomized controlled trial |
title_full | The effect of berberine on insulin resistance in women with polycystic ovary syndrome: detailed statistical analysis plan (SAP) for a multicenter randomized controlled trial |
title_fullStr | The effect of berberine on insulin resistance in women with polycystic ovary syndrome: detailed statistical analysis plan (SAP) for a multicenter randomized controlled trial |
title_full_unstemmed | The effect of berberine on insulin resistance in women with polycystic ovary syndrome: detailed statistical analysis plan (SAP) for a multicenter randomized controlled trial |
title_short | The effect of berberine on insulin resistance in women with polycystic ovary syndrome: detailed statistical analysis plan (SAP) for a multicenter randomized controlled trial |
title_sort | effect of berberine on insulin resistance in women with polycystic ovary syndrome: detailed statistical analysis plan (sap) for a multicenter randomized controlled trial |
topic | Update |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5073878/ https://www.ncbi.nlm.nih.gov/pubmed/27769284 http://dx.doi.org/10.1186/s13063-016-1633-5 |
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