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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 as part of quantitative systematic reviews. A systematic review is a synthesis of primary research studies investigating a clearly formulated clinical question using systematic, explicit and reproducible methods. The Cochrane Library is probably the most comprehensive collection of regularly updated systematic reviews in the health field and is freely accessible in Australia.3 Some systematic reviews qualify for a quantitative statistical summary of comparable study findings, the meta-analysis. While useful guides to systematic review methodology and critical appraisal of systematic reviews are plentiful, 4–6 there is a paucity of practical guides to appraisal of meta-analysis for the nonstatistician. This article provides a practical guide to appraisal of meta-analysis graphs, and has been developed as part of the Primary Health Care Research Evaluation Development (PHCRED) capacity building program for training general practitioners and other primary health care professionals in research methodology. meta-analysis before diving into the fine points of the meta-analysis results and drawing conclusions on patient treatment. Table 1 can guide the assessment.

PROFESSIONAL PRACTICE Research

Karin Ried PhD, MSc, GDPH, is Research Fellow & PHCRED Program Manager, Discipline of General Practice, The University of Adelaide, South Australia. karin.ried@adelaide.edu.au

Meta-analysis graphs
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?

effects) in the intervention (column 2) or control group (column 3), and N = the total number of participants in the intervention (column 2) or control group (column 3) • Figure 2: in meta-analyses with continuous outcome variables (eg. fasting blood glucose level) the individual study findings are given as ‘N’ and ‘mean (SD)', whereby N = the total number of participants in the intervention (column 2) or control group (column 3), and mean SD = the arithmetic mean and standard deviation (SD) of the outcome measure in either the intervention (column 2) or control group (column 3). The fourth column visually displays the study results. The line in the middle is called ‘the line of no effect’, which has the value of either 1 in case of a binary outcome variable (eg. odds ratio (OR) or relative risk [RR]), or 0

Study IDs

Details of review N = total number in group n = number in group with the outcome

Outcome effect measure Shown graphically and numerically Fixed effect model used for meta-analysis

Review: Supplementation with M in condition X Comparison: 01 Supplement M versus placebo Outcome: 01 Adverse effects Study Intervention group n/N 1/141 7/27 1/100 Control group n/N 2/142 9/29 0/100

Influence of studies on overall meta-analysis

Relative risk (fixed) 95% CI

Weight (%) 17.8 77.7 4.5

Relative risk (fixed) 95% CI 0.50 [0.05, 5.49] 0.84 [0.36, 1.93] 3.00 [0.12, 72.77]

Study A Study B Study C

Total (95% CI)

268

271

100.0 Overall effect

0.87 [0.41, 1.87]

Total events: 9 (supplement M), 11 (control) Test for heterogeneity Chi-square=0.79 df=2 p=0.67 I2=0.0% Test for overall effect z=0.35 p=0.7

p value indicating level of statistical significance Heterogeneity (I2) = diversity between studies
Figure 1. Meta-analysis of binary outcome measure

0.01 0.1 1 Favours intervention

10 100 Favours control Scale of treatment effect

Line of no effect

636 Reprinted from Australian Family Physician Vol. 35, No. 8, August 2006

Interpreting and understanding meta-analysis graphs – a practical guide PROFESSIONAL PRACTICE

Study IDs

Details of review N = total number in group Mean (standard deviation) of outcome

Outcome effect measure Shown graphically and numerically Fixed effect model used for meta-analysis

Review: Medicines for condition X Comparison: 01 Medicine Z versus placebo Outcome: 01 Fasting blood glucose levels (mmol/L) Study Intervention group N mean (SD) 34 9.77 (2.93) 36 8.40 (1.90) 30 10.26 (2.96) Control group N mean (SD) 34 10.29 (3.43) 36 8.90 (3.00) 30 12.09 (3.24)

Influence of studies on overall meta-analysis

Weighted mean difference (fixed) 95% CI

Weight (%) 27.5 46.9 25.6

WMD (fixed) 95% CI –0.52 [–2.04, 1.00] –0.50 [–1.66, 0.66] –1.83 [–3.40, –0.26]

Study A Study B Study C

Total (95% CI)

100

100

100.0 Overall effect

–0.85 [–1.64, –0.05]

Test for heterogeneity Chi-square=2.03 df=2 p=0.36 I2=1.4% Test for overall effect z=2.09 p=0.04

p value indicating level ofstatistical significance Heterogeneity (I2) = diversity between studies
Figure 2. Meta-analysis of continuous outcome measures

–4.0 –2.0 0 Favours intervention

2.0 4.0 Favours control Scale of treatment effect

Line of no effect

in case of a continuous outcome variable (eg. WMD). There is no difference between the intervention and the control group, if OR or RR = 1 or WMD = 0. The boxes are situated in line with the outcome value of the individual studies, also called the effect estimates (eg. OR, RR or WMD). The value axis is at the bottom of the graph. The size of the box is directly related to the 'weighting' of the study in the meta-analysis. The horizontal lines (whiskers) through the boxes depict the length of the confidence intervals (CI). The longer the lines, the wider the CI, the less precise the study results. Arrows indicate that the CI is wider than there is space in the graph. The weight (in %) in the fifth column

indicates the weighting or influence of the study on the overall results of the metaanalysis of all included studies. The higher the percentage weight, the bigger the box, the more influence the study has on the overall results. The influence or ‘weight’ of a study on the overall results is determined by the study’s sample size and the precision of the study results provided as a CI. In general, the bigger the sample size and the narrower the CI, the greater the weight of the study. The sixth column gives the numerical results for each study (eg. OR or RR and 95% CI) which are identical to the graphical display in the fourth column. The diamond in the last row of the graph illustrates the overall result of the meta-

analysis. The middle of the diamond sits on the value for the overall effect estimate (eg. OR, RR or WMD) and the width of the diamond depicts the width of the overall CI. The overall numerical results are given in column six. The total number of participants in the intervention groups (column 2) and the control groups (column 3) is also summarised in the same row. If the diamond doesn’t cross the ‘line of no effect’, the calculated difference between the intervention and control groups can be considered as statistically significant. Numerically, the CI does not include 1 for binary outcome variables, measured as OR or RR; the CI does not include 0 for continuous outcome variables, measured as WMD. Statistical significance of the overall result

Reprinted from Australian Family Physician Vol. 35, No. 8, August 2006 637

PROFESSIONAL PRACTICE Interpreting and understanding meta-analysis graphs – a practical guide

Table 2. Comparison of meta-analyses of either binary or continuous variables and outcome effect measures
Examples for variables Outcome effect measure Line of no effect Treatment scale a) If outcome effect measure is adverse (eg. disease present, weight gain) or b) if decreased outcome effect measure is desirable (eg. lowered blood pressure) Treatment scale c) If outcome effect measure is desirable (eg. stopped smoking, increased walking speed) Binary Yes/no, disease/no disease, alive/dead Ratios, often RR or OR 1 Favours intervention on left hand side of treatment scale (ratio 0)

is also expressed with the probability value (p value) in the 'test for overall effect'. Commonly, the result is regarded as statistically significant if p

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