怎样阅读系统评价中meta分析树状图.pdf

Reprinted from Australian Family Physician Vol. 35, No. 8, August 2006 635 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. 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 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. Meta-analysis graphs Meta-analysis results are commonly displayed graphically as ‘forest plots’. Figures 1 and 2 give examples of meta- analysis 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 meta- analyses (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 Karin Ried PhD, MSc, GDPH, is Research Fellow the CI does not include 0 for continuous outcome variables, measured as WMD. Statistical significance of the overall result Figure 2. Meta-analysis of continuous outcome measures Review: Medicines for condition X Comparison: 01 Medicine Z versus placebo 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] 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 Favours intervention Favours control Details of review Study IDs N = total number in group Mean (standard deviation) of outcome Outcome effect measure Shown graphically and numerically Fixed effect model used for meta-analysis Influence of studies on overall meta-analysis p value indicating level ofstatistical significance Heterogeneity (I2) = diversity between studies Line of no effect Scale of treatment effect Overall effect Interpreting and understanding。