Interpreting and understanding meta-analysis graphs
A practical guide
Ideally, clinical decision making ought to be based on the latest evidence available. However, to keep abreast with the continuously increasing number of publications in health research, a primary health care professional would need to read an unsurmountable number of articles every day covered in more than 13 million references and over 4800 biomedical and health journals in Medline alone.1 With the view to address this challenge, the systematic review method was developed.2 This article provides a practical guide for appraising systematic reviews for relevance to clinical practice and interpreting meta-analysis graphs ...view middle of the document...
Meta-analysis results are commonly displayed graphically as ‘forest plots’. Figures 1 and 2 give examples of metaanalysis graphs. Figure 1 illustrates a graph with a binary outcome variable whereas Figure 2 depicts a forest plot with a continuous outcome variable. Some features of meta-analyses using binary and continuous variables and outcome measures are compared in Table 2. The majority of meta-analyses combine data from randomised controlled trials (RCTs), which compare the outcomes between an intervention group and a control group. While outcomes for binary variables are expressed as ratios, continuous outcomes measures are usually expressed as ‘weighted mean difference (WMD)’ in metaanalyses (Table 2). The details of the meta-analysis are commonly displayed above the graph: • review: title/research question of the systematic review and meta-analysis • comparison: intervention versus control group; a range of comparisons may have been done in a systematic review, and • outcome: the primary outcome measure analysed and depicted in the graph below. Meta-analysis graphs can principally be divided into six columns. Individual study results are displayed in rows. The first column (‘study’) lists the individual study IDs included in the meta-analysis, usually the first author and year are displayed. The second column relates to the intervention groups, and the third column to the control groups. • Figure 1: in meta-analyses with binary outcomes (eg. disease/no disease) the individual study findings are displayed as ‘n/N’, whereby: n = the number of participants with the outcome (eg. Figure 1. Adverse
Critical appraisal of systematic reviews and meta-analyses
It is important to assess the methods and quality of the systematic review and appropriateness of the
Reprinted from Australian Family Physician Vol. 35, No. 8, August 2006 635
PROFESSIONAL PRACTICE Interpreting and understanding meta-analysis graphs – a practical guide
Table 1. Assessment of systematic reviews and meta-analyses
When can a systematic review and meta-analysis give further insight into primary study results? • Existing studies gave disparate results • Bigger study population (sample size) can increase power, generalisability and precision of findings (effect estimate) • Subgroup analyses may be possible and could generate new hypotheses Is the meta-analysis clinically sensible? • Did the studies summarised in the systematic review address the same research question? • Are the studies included in the meta-analysis of comparable quality (selection bias, attrition rates, confounding variables)? • Are the studies comparable (eg. population, duration/dosage of treatment)? Will the results help in caring for my patients? • Are the studied populations comparable to my patients? • Are the results clinically important? • Are all clinically important outcomes considered? • Were benefits, harms and costs considered?