Abstract

BackgroundModelling 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.MethodsSix 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.ResultsAmong 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.ConclusionsStudies 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.

Highlights

  • Modelling and analysing repeated measures data, such as women’s experiences of pain during labour, is a complex topic

  • Independent t-tests assume that two groups are compared while Analysis of variance (ANOVA) or Analysis of covariance (ANCOVA) are used if three or more groups are compared, and these are often followed by independent t-tests to study pairwise comparisons

  • A Visual Analog Scale (VAS) [19] was the most-often used tool to measure labour pain, the description of the scale differed among the studies, or the scale was not clearly described

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Summary

Introduction

Modelling and analysing repeated measures data, such as women’s experiences of pain during labour, is a complex topic. 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. Most women in labour require pain relief, which can be Comparing labour pain between different treatment groups needs consideration, both regarding when and how often pain assessments are made, and how statistical analyses have been conducted to reflect the reality of women’s pain varying during labour. Women’s pain varies both between individual women and within the same woman over time as well as between different treatment groups. Pain increases with time, individual progression can differ from woman to woman as a result of both biological factors and the interventions that are administered

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