848 Akobeng judgeme paticnts some ot the iss ece of research may()The add mn6 nentharm pavents prererence义 of the trea CONCLUSIONS Risk and relative risk whole rang vant findings on a particular topic.Meta group wh power and ratio or relative risk is a ratio of tv herietoiaeofodtiretiatondrelotimeritk paticnts REFERENCES t group as d97i2837e 41786-95 :Mc he rela vantages and dis CD S g odd 0.W 315 her poper Precision of the treatment effect:confide ence intervals 10 11 2 .S repo used.The 9%CI of 13 48 159 315 rogro IRJ.Th of 1 the red:it it does not ind n AD,ed ted N there is a statistically significant difference 200 www.archdischild.comODDS RATIOS AND RELATIVE RISKS Odds and odds ratio The odds for a group is defined as the number of patients in the group who achieve the stated end point divided by the number of patients who do not. For example, the odds of acne resolution during treatment with an antibiotic in a group of 10 patients may be 6 to 4 (6 with resolution of acne divided by 4 without = 1.5); in a control group the odds may be 3 to 7 (0.43). The odds ratio, as the name implies, is a ratio of two odds. It is simply defined as the ratio of the odds of the treatment group to the odds of the control group. In our example, the odds ratio of treatment to control group would be 3.5 (1.5 divided by 0.43). Risk and relative risk Risk, as opposed to odds, is calculated as the number of patients in the group who achieve the stated end point divided by the total number of patients in the group. Risk ratio or relative risk is a ratio of two ‘‘risks’’. In the example above the risks would be 6 in 10 in the treatment group (6 divided by 10 = 0.6) and 3 in 10 in the control group (0.3), giving a risk ratio, or relative risk of 2 (0.6 divided by 0.3). Interpretation of odds ratios and relative risk An odds ratio or relative risk greater than 1 indicates increased likelihood of the stated outcome being achieved in the treatment group. If the odds ratio or relative risk is less than 1, there is a decreased likelihood in the treatment group. A ratio of 1 indicates no difference—that is, the outcome is just as likely to occur in the treatment group as it is in the control group.11 As in all estimates of treatment effect, odds ratios or relative risks reported in meta-analysis should be accompanied by confidence intervals. Readers should understand that the odds ratio will be close to the relative risk if the end point occurs relatively infrequently, say in less than 20%.15 If the outcome is more common, then the odds ratio will considerably overestimate the relative risk. The advantages and disadvantages of odds ratios v relative risks in the reporting of the results of meta￾analysis have been reviewed elsewhere.12 Precision of the treatment effect: confidence intervals As stated earlier, confidence intervals should accompany estimates of treatment effects. I discussed the concept of confidence intervals in the second article of the series.8 Ninety five per cent confidence intervals are commonly reported, but other intervals such as 90% or 99% are also sometimes used. The 95% CI of an estimate (for example, of odds ratios or relative risks) will be the range within which we are 95% certain that the true population treatment effect will lie. The width of a confidence interval indicates the precision of the estimate. The wider the interval, the less the precision. A very long interval makes us less sure about the accuracy of a study in predicting the true size of the effect. If the confidence interval for relative risk or odds ratio for an estimate includes 1, then we have been unable to demon￾strate a statistically significant difference between the groups being compared; if it does not include 1, then we say that there is a statistically significant difference. APPLICABILITY OF RESULTS TO PATIENTS Health care professionals should always make judgements about whether the results of a particular study are applicable to their own patient or group of patients. Some of the issues that one need to consider before deciding whether to incorporate a particular piece of research evidence into clinical practice were discussed in the second article of the series.8 These include similarity of study population to your population, benefit v harm, patients preferences, availability, and costs. CONCLUSIONS Systematic reviews apply scientific strategies to provide in an explicit fashion a summary of all studies addressing a specific question, thereby allowing an account to be taken of the whole range of relevant findings on a particular topic. Meta￾analysis, which may accompany a systematic review, can increase power and precision of estimates of treatment effects. People working in the field of paediatrics and child health should understand the fundamental principles of systematic reviews and meta-analyses, including the ability to apply critical appraisal not only to the methodologies of review articles, but also to the applicability of the results to their own patients. Competing interests: none declared REFERENCES 1 Akobeng AK. Evidence based child health 1. Principles of evidence based medicine. Arch Dis Child 2005;90:837–40. 2 Cook DJ, Mulrow CD, Haynes RB. Systematic reviews: synthesis of best evidence for clinical decisions. Ann Intern Med 1997;126:376–80. 3 Pai M, McCulloch M, Gorman JD, et al. Systematic reviews and meta￾analyses: an illustrated, step-by-step guide. Natl Med J India 2004;17:86–95. 4 McGovern DPB. Systematic reviews. In: McGovern DPB, Valori RM, Summerskill WSM, eds. Key topics in evidence based medicine. Oxford: BIOS Scientific Publishers, 2001:17–9. 5 McAlister FA, Clark HD, van Walraven C, et al. The medical review article revisited: has the science improved? Ann Intern Med 1999;131:947–51. 6 Sackett DL, Strauss SE, Richardson WS, et al. Evidence-based medicine: how to practice and teach EBM. London: Churchill-Livingstone, 2000. 7 Mulrow CD. Systematic reviews: rationale for systematic reviews. BMJ 1994;309:597–9. 8 Akobeng AK. Evidence based child health 2. Understanding randomised controlled trials. Arch Dis Child 2005;90:840–4. 9 Greenhalgh T. How to read a paper: papers that summarise other papers (systematic reviews and meta-analyses). BMJ 1997;315:672–5. 10 Muir Gray JA. Evidence based healthcare. How to make health policy and management decisions. London: Churchill Livingstone, 2001:125–6. 11 Lang TA, Secic M. How to report statistics in medicine. Philadelphia: American College of Physicians, 1997. 12 Deeks JJ, Altman DG, Bradburn MJ. Statistical methods for examining heterogeneity and combining results from several studies in meta-analysis. In: Egger M, Smith GD, Altman DG, eds. Systematic reviews in healthcare: meta￾analysis in context. London: BMJ Publishing Group, 2001:285–312. 13 Lewis S, Clarke M. Forest plots: trying to see the wood and the trees. BMJ 2001;322:1479–80. 14 D’Souza AL, Rajkumar C, Cooke J, et al. Probiotics in prevention of antibiotic associated diarrhoea: meta-analysis. BMJ 2002;324:1361. 15 Egger M, Smith GD, Phillips AN. Meta-analysis: principles and procedures. BMJ 1997;315:1533–7. 16 Critical Appraisal Skills Programme. Appraisal Tools. Oxford, UK. http:// www.phru.nhs.uk/casp/appraisa.htm (accessed 10 Dec 2004). 17 Tubman TRJ, Thompson SW. Glutamine supplementation for prevention of morbidity in preterm infants. The Cochrane Database of Systematic Reviews 2001, Issue 4. 18 Dickersin K, Scherer R, Lefebvre C. Systematic reviews: identifying relevant studies for systematic reviews. BMJ 1994;309:1286–91. 19 Clarke M, Oxman AD, eds. Selecting studies. Cochrane reviewers’ handbook 4.2.0 [updated March 2003]. In: The Cochrane library, issue 2. Oxford: Update Software, 2003. 848 Akobeng www.archdischild.com