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Interpreting and Understanding Meta-Analysis Graphs – a Practical Guide

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Interpreting and Understanding Meta-Analysis Graphs – a Practical Guide PROFESSIONAL PRACTICE Interpreting and understanding Research meta-analysis graphs A practical guide Karin Ried PhD, MSc, GDPH, is Research Fellow & PHCRED Program Manager, Discipline of General Ideally, clinical decision making ought to be based meta-analysis before diving into the fine points of the Practice, The University of on the latest evidence available. However, to keep meta-analysis results and drawing conclusions on patient Adelaide, South Australia. abreast with the continuously increasing number of treatment. Table 1 can guide the assessment. [email protected] publications in health research, a primary health care Meta-analysis graphs professional would need to read an unsurmountable number of articles every day covered in more than 13 Meta-analysis results are commonly displayed graphically million references and over 4800 biomedical and health as ‘forest plots’. Figures 1 and 2 give examples of meta- journals in Medline alone.1 With the view to address analysis graphs. Figure 1 illustrates a graph with a binary this challenge, the systematic review method was outcome variable whereas Figure 2 depicts a forest plot developed.2 This article provides a practical guide for with a continuous outcome variable. Some features of appraising systematic reviews for relevance to clinical meta-analyses using binary and continuous variables and practice and interpreting meta-analysis graphs as part outcome measures are compared in Table 2. of quantitative systematic reviews. The majority of meta-analyses combine data from randomised controlled trials (RCTs), which compare the A systematic review is a synthesis of primary research outcomes between an intervention group and a control studies investigating a clearly formulated clinical question group. While outcomes for binary variables are expressed using systematic, explicit and reproducible methods. The as ratios, continuous outcomes measures are usually Cochrane Library is probably the most comprehensive expressed as ‘weighted mean difference (WMD)’ in meta- collection of regularly updated systematic reviews in the analyses (Table 2). health field and is freely accessible in Australia.3 The details of the meta-analysis are commonly Some systematic reviews qualify for a quantitative displayed above the graph: statistical summary of comparable study findings, • review: title/research question of the systematic the meta-analysis. While useful guides to systematic review and meta-analysis review methodology and critical appraisal of • comparison: intervention versus control group; a systematic reviews are plentiful,4–6 there is a paucity range of comparisons may have been done in a of practical guides to appraisal of meta-analysis for systematic review, and the nonstatistician. • outcome: the primary outcome measure analysed This article provides a practical guide to appraisal and depicted in the graph below. of meta-analysis graphs, and has been developed as Meta-analysis graphs can principally be divided into six part of the Primary Health Care Research Evaluation columns. Individual study results are displayed in rows. The Development (PHCRED) capacity building program for first column (‘study’) lists the individual study IDs included training general practitioners and other primary health in the meta-analysis, usually the first author and year are care professionals in research methodology. displayed. The second column relates to the intervention groups, and the third column to the control groups. Critical appraisal of systematic reviews and • Figure 1: in meta-analyses with binary outcomes meta-analyses (eg. disease/no disease) the individual study findings It is important to assess the methods and quality of are displayed as ‘n/N’, whereby: n = the number of the systematic review and appropriateness of the participants with the outcome (eg. Figure 1. Adverse Reprinted from Australian Family Physician Vol. 35, No. 8, August 2006 635 PROFESSIONAL PRACTICE Interpreting and understanding meta-analysis graphs – a practical guide effects) in the intervention (column 2) or Table 1. Assessment of systematic reviews and meta-analyses control group (column 3), and N = the total When can a systematic review and meta-analysis give further insight into primary number of participants in the intervention study results? (column 2) or control group (column 3) • Existing studies gave disparate results • Figure 2: in meta-analyses with continuous • Bigger study population (sample size) can increase power, generalisability and outcome variables (eg. fasting blood precision of findings (effect estimate) glucose level) the individual study findings • Subgroup analyses may be possible and could generate new hypotheses are given as ‘N’ and ‘mean (SD)', whereby Is the meta-analysis clinically sensible? N = the total number of participants in the • Did the studies summarised in the systematic review address the same research intervention (column 2) or control group question? (column 3), and mean SD = the arithmetic • Are the studies included in the meta-analysis of comparable quality (selection bias, attrition rates, confounding variables)? mean and standard deviation (SD) of the • Are the studies comparable (eg. population, duration/dosage of treatment)? outcome measure in either the intervention Will the results help in caring for my patients? (column 2) or control group (column 3). • Are the studied populations comparable to my patients? The fourth column visually displays the • Are the results clinically important? study results. The line in the middle is called • Are all clinically important outcomes considered? ‘the line of no effect’, which has the value of • Were benefits, harms and costs considered? either 1 in case of a binary outcome variable (eg. odds ratio (OR) or relative risk [RR]), or 0 Outcome effect measure Shown graphically and numerically Details of review Study IDs Fixed effect model used for meta-analysis N = total number in group n = number in group with the outcome Review: Supplementation with M in condition X Influence of studies on Comparison: 01 Supplement M versus placebo overall meta-analysis Outcome: 01 Adverse effects Study Intervention group Control group Relative risk (fixed) Weight Relative risk (fixed) n/N n/N 95% CI (%) 95% CI Study A 1/141 2/142 17.8 0.50 [0.05, 5.49] Study B 7/27 9/29 77.7 0.84 [0.36, 1.93] Study C 1/100 0/100 4.5 3.00 [0.12, 72.77] Total (95% CI) 268 271 100.0 0.87 [0.41, 1.87] Total events: 9 (supplement M), 11 (control) Overall effect 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 0.01 0.1 1 10 100 p value indicating level of Favours intervention Favours control statistical significance Scale of treatment effect Heterogeneity (I2) = diversity between studies Line of no effect Figure 1. Meta-analysis of binary outcome measure 636 Reprinted from Australian Family Physician Vol. 35, No. 8, August 2006 Interpreting and understanding meta-analysis graphs – a practical guide PROFESSIONAL PRACTICE Outcome effect measure Shown graphically and numerically Study IDs Details of review N = total number in group Fixed effect model used for meta-analysis Mean (standard deviation) of outcome Review: Medicines for condition X Influence of studies on Comparison: 01 Medicine Z versus placebo overall meta-analysis Outcome: 01 Fasting blood glucose levels (mmol/L) Study Intervention group Control group Weighted mean difference Weight WMD (fixed) N mean (SD) N mean (SD) (fixed) 95% CI (%) 95% CI Study A 34 9.77 (2.93) 34 10.29 (3.43) 27.5 –0.52 [–2.04, 1.00] Study B 36 8.40 (1.90) 36 8.90 (3.00) 46.9 –0.50 [–1.66, 0.66] Study C 30 10.26 (2.96) 30 12.09 (3.24) 25.6 –1.83 [–3.40, –0.26] Total (95% CI) 100 100 100.0 –0.85 [–1.64, –0.05] Overall effect 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 –4.0 –2.0 0 2.0 4.0 p value indicating level ofstatistical Favours intervention Favours control significance Scale of treatment effect Heterogeneity (I2) = diversity between studies Line of no effect Figure 2. Meta-analysis of continuous outcome measures in case of a continuous outcome variable (eg. indicates the weighting or influence of analysis. The middle of the diamond sits WMD). There is no difference between the the study on the overall results of the meta- on the value for the overall effect estimate (eg. intervention and the control group, if OR or RR analysis of all included studies. The higher OR, RR or WMD) and the width of the diamond = 1 or WMD = 0. the percentage weight, the bigger the box, depicts the width of the overall CI. The overall The boxes are situated in line with the the more influence the study has on the numerical results are given in column six. outcome value of the individual studies, overall results. The influence or ‘weight’ of The total number of participants in the also called the effect estimates (eg. OR, RR a study on the overall results is determined intervention groups (column 2) and the control or WMD). The value axis is at the bottom of by the study’s sample size and the precision groups (column 3) is also summarised in the the graph. The size of the box is directly of the study results provided as a CI. In same row. related to the 'weighting' of the study in the general, the bigger the sample size and the If the diamond doesn’t cross the ‘line of meta-analysis. narrower the CI, the greater the weight of no effect’, the calculated difference between The horizontal lines (whiskers) through the study. the intervention and control groups can the boxes depict the length of the confidence The sixth column gives the numerical results be considered as statistically significant.
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