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Labour Q1 pain - poorly analysed and reported: a systematic review.
Järnbert-Pettersson, Hans; Vixner, Linda.
Afiliação
  • Järnbert-Pettersson H; Karolinska Institutet - Department of Clinical Science and Education, Södersjukhuset, Stockholm, Sweden. Hans.pettersson@ki.se.
  • Vixner L; Dalarna University- School of Education, Health and Social Studies, Falun, Sweden.
BMC Pregnancy Childbirth ; 18(1): 483, 2018 Dec 07.
Article em En | MEDLINE | ID: mdl-30526516
ABSTRACT

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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Projetos de Pesquisa / Estatística como Assunto / Dor do Parto Tipo de estudo: Diagnostic_studies / Prognostic_studies / Systematic_reviews Limite: Female / Humans / Pregnancy Idioma: En Revista: BMC Pregnancy Childbirth Assunto da revista: OBSTETRICIA Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Suécia

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Projetos de Pesquisa / Estatística como Assunto / Dor do Parto Tipo de estudo: Diagnostic_studies / Prognostic_studies / Systematic_reviews Limite: Female / Humans / Pregnancy Idioma: En Revista: BMC Pregnancy Childbirth Assunto da revista: OBSTETRICIA Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Suécia