Education And Debate

Meta-analysis: Principles and procedures

BMJ 1997; 315 doi: http://dx.doi.org/10.1136/bmj.315.7121.1533 (Published 06 December 1997) Cite this as: BMJ 1997;315:1533
  1. Matthias Egger, reader in social medicine and epidemiology (egger{at}bristol.ac.uk)a,
  2. George Davey Smith, professor of clinical epidemiologya,
  3. Andrew N Phillips, professor of epidemiology and biostatisticsb

    This is the second in a series of seven articles examining the procedures in conducting reliable meta-analysis in medical research

  1. a Department of Social Medicine, University of Bristol, Bristol BS8 2PR
  2. b Department of Primary Care and Population Sciences, Royal Free Hospital School of Medicine, London NW3 2PF
  1. Correspondence to: Dr Eggerm

    Introduction

    Meta-analysis is a statistical procedure that integrates the results of several independent studies considered to be “combinable.”1 Well conducted meta-analyses allow a more objective appraisal of the evidence than traditional narrative reviews, provide a more precise estimate of a treatment effect, and may explain heterogeneity between the results of individual studies.2 Ill conducted meta-analyses, on the other hand, may be biased owing to exclusion of relevant studies or inclusion of inadequate studies.3 Misleading analyses can generally be avoided if a few basic principles are observed. In this article we discuss these principles, along with the practical steps in performing meta-analysis.

    Observational study of evidence

    Meta-analysis should be viewed as an observational study of the evidence. The steps involved are similar to any other research undertaking: formulation of the problem to be addressed, collection and analysis of the data, and reporting of the results. Researchers should write in advance a detailed research protocol that clearly states the objectives, the hypotheses to be tested, the subgroups of interest, and the proposed methods and criteria for identifying and selecting relevant studies and extracting and analysing information.

    As with criteria for including and excluding patients in clinical studies, eligibility criteria have to be defined for the data to be included. Criteria relate to the quality of trials and to the combinability of treatments, patients, outcomes, and lengths of follow up. Quality and design features of a study can influence the results.4 5 Ideally, researchers should consider including only controlled trials with proper randomisation of patients that report on all initially included patients according to the intention to treat principle and with an objective, preferably blinded, outcome assessment.6 Assessing the quality of a study can be a subjective process, however, especially since the information reported is often inadequate for this purpose.7 It is therefore preferable to define only basic inclusion criteria and to perform a thorough sensitivity analysis (see below).

    The strategy for identifying the relevant studies should be clearly delineated. In particular, it has to be decided whether the search will be extended to include unpublished studies, as their results may systematically differ from published trials. As will be discussed in later articles, a meta-analysis that is restricted to published evidence may produce distorted results owing to such publication bias. For locating published studies, electronic databases are useful,8 but, used alone, they may miss a substantial proportion of relevant studies.9 10 In an attempt to identify all published controlled trials, the Cochrane Collaboration has embarked on an extensive manual search of medical journals published in English and many other languages.11 The Cochrane Controlled Trials Register12 is probably the best single electronic source of trials; however, citation indices and the bibliographies of review articles, monographs, and the located studies should also be scrutinised.

    Summary points

    Meta-analysis should be as carefully planned as any other research project, with a detailed written protocol being prepared in advance

    The a priori definition of eligibility criteria for studies to be included and a comprehensive search for such studies are central to high quality meta-analysis

    The graphical display of results from individual studies on a common scale is an important intermediate step, which allows a visual examination of the degree of heterogeneity between studies

    Different statistical methods exist for combining the data, but there is no single “correct” method

    A thorough sensitivity analysis is essential to assess the robustness of combined estimates to different assumptions and inclusion criteria

    A standardised record form is needed for data collection. It is useful if two independent observers extract the data, to avoid errors. At this stage the quality of the studies may be rated, with one of several specially designed scales.13 14 Blinding observers to the names of the authors and their institutions, the names of the journals, sources of funding, and acknowledgments leads to more consistent scores.14 This entails either photocopying papers, removing the title page, and concealing journal identifications and other characteristics with a black marker, or scanning the text of papers into a computer and preparing standardised formats.15 16

    Standardised outcome measure

    Individual results have to be expressed in a standardised format to allow for comparison between studies. If the end point is continuous—for example, blood pressure—the mean difference between the treatment and control groups is used. The size of a difference, however, is influenced by the underlying population value. An antihypertensive drug, for example, is likely to have a greater absolute effect on blood pressure in overtly hypertensive patients than in borderline hypertensive patients. Differences are therefore often presented in units of standard deviation. If the end point is binary—for example, disease versus no disease, or dead versus alive) then odds ratios or relative risks are often calculated (box). The odds ratio has convenient mathematical properties, which allow for ease in combining data and testing the overall effect for significance. Absolute measures, such as the absolute risk reduction or the number of patients needed to be treated to prevent one event,17 are more helpful when applying results in clinical practice (see below).

    Odds ratio or relative risk?

    Odds and odds ratio

    The odds is the number of patients who fulfil the criteria for a given endpoint divided by the number of patients who do not. For example, the odds of diarrhoea during treatment with an antibiotic in a group of 10 patients may be 4 to 6 (4 with diarrhoea divided by 6 without, 0.66); in a control group the odds may be 1 to 9 (0.11) (a bookmaker would refer to this as 9 to 1). The odds ratio of treatment to control group would be 6 (0.66÷0.11).

    Risk and relative risk

    The risk is the number of patients who fulfil the criteria for a given end point divided by the total number of patients. In the example above the risks would be 4 in 10 in the treatment group and 1 in 10 in the control group, giving a risk ratio, or relative risk, of 4 (0.4÷0.1).



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    Statistical methods for calculating overall effect

    The last step consists in calculating the overall effect by combining the data. A simple arithmetic average of the results from all the trials would give misleading results. The results from small studies are more subject to the play of chance and should therefore be given less weight. Methods used for meta-analysis use a weighted average of the results, in which the larger trials have more influence than the smaller ones. The statistical techniques to do this can be broadly classified into two models,18 the difference consisting in the way the variability of the results between the studies is treated. The “fixed effects” model considers, often unreasonably, that this variability is exclusively due to random variation.19 Therefore, if all the studies were infinitely large they would give identical results. The “random effects” model20 assumes a different underlying effect for each study and takes this into consideration as an additional source of variation, which leads to somewhat wider confidence intervals than the fixed effects model. Effects are assumed to be randomly distributed, and the central point of this distribution is the focus of the combined effect estimate. Although neither of two models can be said to be “correct,” a substantial difference in the combined effect calculated by the fixed and random effects models will be seen only if studies are markedly heterogeneous.18

    Bayesian meta-analysis

    Some statisticians feel that other statistical approaches are more appropriate than either of the above. One approach uses Bayes's theorem, named after an 18th century English clergyman.21 Bayesian statisticians express their belief about the size of an effect by specifying some prior probability distribution before seeing the data, and then they update that belief by deriving a posterior probability distribution, taking the data into account.22 Bayesian models are available under both the fixed and random effects assumption.23 The confidence interval (or more correctly in bayesian terminology, the 95% credible interval, which covers 95% of the posterior probability distribution) will often be wider than that derived from using the conventional models because another component of variability, the prior distribution, is introduced. Bayesian approaches are controversial because the definition of prior probability will often be based on subjective assessments and opinion.

    Heterogeneity between study results

    If the results of the studies differ greatly then it may not be appropriate to combine the results. How to ascertain whether it is appropriate, however, is unclear. One approach is to examine statistically the degree of similarity in the studies' outcomes—in other words, to test for heterogeneity across studies. In such procedures, whether the results of a study reflect a single underlying effect, rather than a distribution of effects, is assessed. If this test shows homogeneous results then the differences between studies are assumed to be a consequence of sampling variation, and a fixed effects model is appropriate. If, however, the test shows that significant heterogeneity exists between study results then a random effects model is advocated. A major limitation with this approach is that the statistical tests lack power—they often fail to reject the null hypothesis of homogeneous results even if substantial differences between studies exist. Although there is no statistical solution to this issue, heterogeneity between study results should not be seen as purely a problem for meta-analysis—it also provides an opportunity for examining why treatment effects differ in different circumstances. Heterogeneity should not simply be ignored after a statistical test is applied; rather, it should be scrutinised, with an attempt to explain it.24

    Fig 1
    Fig 1

    Total mortality from trials of ß blockers in secondary prevention after myocardial infarction. The black square and horizontal line correspond to odds ratio and 95% confidence interval for each trial. The size of the black square reflects the weight of each trial. The diamond represents the combined odds ratio and 95% confidence interval, showing 22% a reduction in the odds of death (references are available from the authors)

    Graphic display

    Results from each trial are usefully graphically displayed, together with their confidence intervals. Figure 3 represents a meta-analysis of 17 trials of ß blockers in secondary prevention after myocardial infarction. Each study is represented by a black square and a horizontal line, which correspond to the point estimate and the 95% confidence intervals of the odds ratio. The 95% confidence intervals would contain the true underlying effect in 95% of the occasions if the study was repeated again and again. The solid vertical line corresponds to no effect of treatment (odds ratio 1.0). If the confidence interval includes 1, then the difference in the effect of experimental and control treatment is not significant at conventional levels (P>0.05). The area of the black squares reflects the weight of the study in the meta-analysis. The confidence interval of all but two studies cross this line, indicating that the effect estimates were non-significant (P>0.05).

    The diamond represents the combined odds ratio, calculated using a fixed effects model, with its 95% confidence interval. The combined odds ratio shows that oral ß blockade starting a few days to a few weeks after the acute phase reduces subsequent mortality by an estimated 22% (odds ratio 0.78; 95% confidence interval 0.71 to 0.87). A dashed line is plotted vertically through the combined odds ratio. This line crosses the horizontal lines of all individual studies except one (N). This indicates a fairly homogenous set of studies. Indeed, the test for heterogeneity gives a non-significant P value of 0.2.

    A logarithmic scale was used for plotting the odds ratios in figure 3. There are several reasons that ratio measures are best plotted on logarithmic scales.25 Most importantly, the value of an odds ratio and its reciprocal—for example, 0.5 and 2—which represent odds ratios of the same magnitude but opposite directions, will be equidistant from 1.0. Studies with odds ratios below and above 1.0 will take up equal space on the graph and thus look equally important. Also, confidence intervals will be symmetrical around the point estimate.

    Relative and absolute measures of effect

    Repeating the analysis by using relative risk instead of the odds ratio gives an overall relative risk of 0.80 (95% confidence interval 0.73 to 0.88). The odds ratio is thus close to the relative risk, as expected when the outcome is relatively uncommon (see box). The relative risk reduction, obtained by subtracting the relative risk from 1 and expressing the result as a percentage, is 20% (12% to 27%). The relative measures used in this analysis ignore the absolute underlying risk. The risk of death among patients who have survived the acute phase of myocardial infarction, however, varies widely.26 For example, among patients with three or more cardiac risk factors the probability of death at two years after discharge ranged from 24% to 60%.26 Conversely, two year mortality among patients with no risk factors was less than 3%. The absolute risk reduction or risk difference reflects both the underlying risk without treatment and the risk reduction associated with treatment. Taking the reciprocal of the risk difference gives the “number needed to treat” (the number of patients needed to be treated to prevent one event).17

    For a baseline risk of 1% a year, the absolute risk difference shows that two deaths are prevented per 1000 patients treated (1). This corresponds to 500 patients (1÷0.002) treated for one year to prevent one death. Conversely, if the risk is above 10%, less than 50 patients have to be treated to prevent one death. Many clinicians would probably decide not to treat patients at very low risk, given the large number of patients that have to be exposed to the adverse effects of ß blockade to prevent one death. Appraising the number needed to treat from a patient's estimated risk without treatment and the relative risk reduction with treatment is a helpful aid when making a decision in an individual patient. A nomogram that facilitates calculation of the number needed to treat at the bedside has recently been published.27

    β Blockade in secondary prevention after myocardial infarction-absolute risk reductions and numbers needed to treat for one year to prevent one death for different levels of mortality in control group

    View this table:

    Meta-analysis using absolute effect measures such as the risk difference may be useful to illustrate the range of absolute effects across studies. The combined risk difference (and the number needed to treat calculated from it) will, however, be essentially determined by the number and size of trials in patients at low, intermediate, or high risk. Combined results will thus be applicable only to patients at levels of risk corresponding to the average risk of the trials included. It is therefore generally more meaningful to use relative effect measures for summarising the evidence and absolute measures for applying it to a concrete clinical or public health situation.

    Sensitivity analysis

    Opinions will often diverge on the correct method for performing a particular meta-analysis. The robustness of the findings to different assumptions should therefore always be examined in a thorough sensitivity analysis. This is illustrated in figure 4 for the meta-analysis of ß blockade after myocardial infarction. Firstly, the overall effect was calculated by different statistical methods, by using both a fixed and a random effects model. The 4 shows that the overall estimates are virtually identical and that confidence intervals are only slightly wider with the random effects model. This is explained by the relatively small amount of variation between trials in this meta-analysis.

    Fig 2
    Fig 2

    Sensitivity analysis of meta-analysis of ß blockers in secondary prevention after myocardial infarction (see text for explanation)

    Secondly, methodological quality was assessed in terms of how patients were allocated to active treatment or control groups, how outcome was assessed, and how the data were analysed.6 The maximum credit of nine points was given if patient allocation was truly random, if assessment of vital status was independent of treatment group, and if data from all patients initially included were analysed according to the intention to treat principle. Figure 4 shows that the three low quality studies (≤7 points) showed more benefit than the high quality trials. Exclusion of these three studies, however, leaves the overall effect and the confidence intervals practically unchanged.

    Thirdly, significant results are more likely to get published than non-significant findings,28 and this can distort the findings of meta-analyses. The presence of such publication bias can be identified by stratifying the analysis by study size—smaller effects can be significant in larger studies. If publication bias is present, it is expected that, of published studies, the largest ones will report the smallest effects. Figure 4 shows that this is indeed the case, with the smallest trials (50 or fewer deaths) showing the largest effect. However, exclusion of the smallest studies has little effect on the overall estimate.

    Finally, two studies (J and N; see 3) were stopped earlier than anticipated on the grounds of the results from interim analyses. Estimates of treatment effects from trials that were stopped early are liable to be biased away from the null value. Bias may thus be introduced in a meta-analysis that includes such trials.29 Exclusion of these trials, however, affects the overall estimate only marginally.

    The sensitivity analysis thus shows that the results from this meta-analysis are robust to the choice of the statistical method and to the exclusion of trials of poorer quality or of studies stopped early. It also suggests that publication bias is unlikely to have distorted its findings.

    Conclusions

    Meta-analysis should be seen as structuring the processes through which a thorough review of previous research is carried out. The issues of completeness and combinability of evidence, which need to be considered in any review,30 are made explicit. Was it sensible to have combined the individual trials that comprise the meta-analysis? How robust is the result to changes in assumptions? Does the conclusion reached make clinical and pathophysiological sense? Finally, has the analysis contributed to the process of making rational decisions about the management of patients? It is these issues that we explore further in later articles in this series.

    Acknowledgments

    Funding: ME was supported by the Swiss National Science Foundation.

    Notes

    The department of social medicine at the University of Bristol and the department of primary care and population sciences at the Royal Free Hospital School of Medicine, London, are part of the Medical Research Council's health services research collaboration.

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