How to interpret a meta-analysis?

Abstract

There is an enormous and ever-growing quantity of healthcare information available and practitioners must transform it into knowledge to be able to use it in their clinical practice. Even readers who do not conduct scientific studies themselves need to understand the scientific method in detail to be able to critically evaluate scientific articles. Evidence-based healthcare (EBH) can be defined as the link between good scientific research and clinical practice and systematic reviews constitute one of the forms of research excellence proposed within EBH. Systematic reviews employ rigorous methods that reduce the occurrence of bias. Systematic reviews with meta-analyses generally optimize the results found, because quantitative analysis of the studies included in the review yields additional information. In this paper, we will discuss how to interpret a meta-analysis and how to apply subset and sensitivity analysis strategies and we will also describe possible sources of heterogeneity and common errors that can affect a meta-analysis.

The effects of auditory stimulation on heart rate variability in healthy individuals with normal hearing and with hearing loss: a systematic review and meta-analysis

“…It is important to highlight the power of meta-analysis, because when analyzing, for example, the effect size of six cross-sectional studies that evaluated the RMSSD index of HRV, none of the studies showed statistically significant effects, although when the data from all of the studies weew groped together, the sample size increased and, consequently, the statistical power improved, demonstrating the effects of acoustic stimulation on the RMSSD index. However, the results obtained (primary and secondary outcomes) must be interpreted with caution when considering the effect size of clinical outcomes close to the vertical line of the null hypothesis (31) .…”

Analysis of data with dependent measures in clinical and experimental studies

“…This is relevant in the comparison of clinical trials or in the evaluation of the results of meta-analyses, because the participants' baseline conditions (for example, age, weight, comorbidities, disease severity, and metabolic status) may differ between studies, interfering in the achievement of outcomes, irrespective of the treatment analyzed. 24,25 Moreover, although logistic models can be adjusted for other covariates, this type of correction is not usually employed in the analysis of clinical trials.…”

Análise de dados com medidas dependentes em estudos clínicos e experimentais

“…No exemplo da Figura 2 (Tabela 1), apesar de o tratamento C promover uma maior redução numérica da área das úlceras, essa foi a intervenção que menos induziu cicatrização completa, o que pode se dever à própria condição prévia de úlceras com sujeitos apresentando maior área inicial, mas também a condições clínicas que interferissem na cicatrização. Isso é relevante na comparação de ensaios clínicos ou na avaliação do resultado de metanálises, porque as condições basais dos participantes (por exemplo, idade, peso, comorbidades, gravidade da doença, status metabólico) podem ser diferentes entre os estudos, interferindo na obtenção de desfechos, independentemente do tratamento avaliado 24,25 . Ainda, apesar de modelos logísticos poderem ser ajustados para outras covariáveis, essa não é uma correção usualmente empregada na análise de ensaios clínicos.…”