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Labour Q1 pain – poorly analysed and reported: a systematic review

BACKGROUND: Modelling and analysing repeated measures data, such as women’s experiences of pain during labour, is a complex topic. Traditional end-point analyses such as t-tests, ANOVA, or repeated measures [rANOVA] have known disadvantages. Modern and more sophisticated statistical methods such as...

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Autores principales: Järnbert-Pettersson, Hans, Vixner, Linda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6286546/
https://www.ncbi.nlm.nih.gov/pubmed/30526516
http://dx.doi.org/10.1186/s12884-018-2089-2
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author Järnbert-Pettersson, Hans
Vixner, Linda
author_facet Järnbert-Pettersson, Hans
Vixner, Linda
author_sort Järnbert-Pettersson, Hans
collection PubMed
description BACKGROUND: Modelling and analysing repeated measures data, such as women’s experiences of pain during labour, is a complex topic. Traditional end-point analyses such as t-tests, ANOVA, or repeated measures [rANOVA] have known disadvantages. Modern and more sophisticated statistical methods such as mixed effect models provide flexibility and are more likely to draw correct conclusions from data. The aim of this study is to study how labour pain is analysed in repeated measures design studies, and to increase awareness of when and why modern statistical methods are suitable with the aim of encouraging their use in preference of traditional methods. METHODS: Six databases were searched with the English language as a restriction. Study eligibility criteria included: Original studies published between 1999 and 2016, studying pregnant women in labour with the aim to compare at least two methods for labour pain management, with at least two measurements of labour pain separated by time, and where labour pain was analysed. After deduplication, all records (n = 2800) were screened by one of the authors who excluded ineligible publication types, leaving 737 records remaining for full-text screening. A sample of 309 studies was then randomly selected and screened by both authors. RESULTS: Among the 133 (of 309) studies that fulfilled the study eligibility criteria, 7% used mixed effect models, 20% rANOVA, and 73% used end-point analysis to draw conclusions regarding treatment effects for labour pain between groups. The most commonly used end-point analyses to compare groups regarding labour pain were t-tests (57, 43%) and ANOVA (41, 31%). We present a checklist for clinicians to clarify when mixed effect models should be considered as the preferred choice for analysis, in particular when labour pain is measured. CONCLUSIONS: Studies that aim to compare methods for labour pain management often use inappropriate statistical methods, and inaccurately report how the statistical analyses were carried out. The statistical methods used in analyses are often based on assumptions that are not fulfilled or described. We recommend that authors, reviewers, and editors pay greater attention to the analysis when designing and publishing studies evaluating methods for pain relief during labour. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12884-018-2089-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-62865462018-12-14 Labour Q1 pain – poorly analysed and reported: a systematic review Järnbert-Pettersson, Hans Vixner, Linda BMC Pregnancy Childbirth Research Article BACKGROUND: Modelling and analysing repeated measures data, such as women’s experiences of pain during labour, is a complex topic. Traditional end-point analyses such as t-tests, ANOVA, or repeated measures [rANOVA] have known disadvantages. Modern and more sophisticated statistical methods such as mixed effect models provide flexibility and are more likely to draw correct conclusions from data. The aim of this study is to study how labour pain is analysed in repeated measures design studies, and to increase awareness of when and why modern statistical methods are suitable with the aim of encouraging their use in preference of traditional methods. METHODS: Six databases were searched with the English language as a restriction. Study eligibility criteria included: Original studies published between 1999 and 2016, studying pregnant women in labour with the aim to compare at least two methods for labour pain management, with at least two measurements of labour pain separated by time, and where labour pain was analysed. After deduplication, all records (n = 2800) were screened by one of the authors who excluded ineligible publication types, leaving 737 records remaining for full-text screening. A sample of 309 studies was then randomly selected and screened by both authors. RESULTS: Among the 133 (of 309) studies that fulfilled the study eligibility criteria, 7% used mixed effect models, 20% rANOVA, and 73% used end-point analysis to draw conclusions regarding treatment effects for labour pain between groups. The most commonly used end-point analyses to compare groups regarding labour pain were t-tests (57, 43%) and ANOVA (41, 31%). We present a checklist for clinicians to clarify when mixed effect models should be considered as the preferred choice for analysis, in particular when labour pain is measured. CONCLUSIONS: Studies that aim to compare methods for labour pain management often use inappropriate statistical methods, and inaccurately report how the statistical analyses were carried out. The statistical methods used in analyses are often based on assumptions that are not fulfilled or described. We recommend that authors, reviewers, and editors pay greater attention to the analysis when designing and publishing studies evaluating methods for pain relief during labour. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12884-018-2089-2) contains supplementary material, which is available to authorized users. BioMed Central 2018-12-07 /pmc/articles/PMC6286546/ /pubmed/30526516 http://dx.doi.org/10.1186/s12884-018-2089-2 Text en © The Author(s). 2018 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 Research Article
Järnbert-Pettersson, Hans
Vixner, Linda
Labour Q1 pain – poorly analysed and reported: a systematic review
title Labour Q1 pain – poorly analysed and reported: a systematic review
title_full Labour Q1 pain – poorly analysed and reported: a systematic review
title_fullStr Labour Q1 pain – poorly analysed and reported: a systematic review
title_full_unstemmed Labour Q1 pain – poorly analysed and reported: a systematic review
title_short Labour Q1 pain – poorly analysed and reported: a systematic review
title_sort labour q1 pain – poorly analysed and reported: a systematic review
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6286546/
https://www.ncbi.nlm.nih.gov/pubmed/30526516
http://dx.doi.org/10.1186/s12884-018-2089-2
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