Among effect measures for dichotomous data, no single measure is uniformly best, so the choice inevitably involves a compromise. If the same ordinal scale has been used in all studies, but in some reports has been presented as a dichotomous outcome, it may still be possible to include all studies in the meta-analysis. Whenever possible, contact the original investigators to request missing data. This adjustment widens the confidence interval to reflect uncertainty in the estimation of between-study heterogeneity, and it should be used if available to review authors. Results may be expressed as count data when each participant may experience an event, and may experience it more than once (see Chapter 6, Section 6.7). Aim. Quick reference and worked example (2012). A weighted average is defined as, The combination of intervention effect estimates across studies may optionally incorporate an assumption that the studies are not all estimating the same intervention effect, but estimate intervention effects that follow a distribution across studies. Subgroup analyses can also generate misleading recommendations about directions for future research that, if followed, would waste scarce resources. The area of the block and the confidence interval convey similar information, but both make different contributions to the graphic. User guide version 1.3 (2012). In some circumstances, statisticians distinguish between data ‘missing at random’ and data ‘missing completely at random’, although in the context of a systematic review the distinction is unlikely to be important. To join, please sign up for a Cochrane account at account.cochrane.org , and then email us at cochranepma@ctc.usyd.edu.au to indicate how you would like to be involved. • Version 5.2: June 2017 [PDF] Address the potential impact of missing data on the findings of the review in the Discussion section. Skewed data are sometimes not summarized usefully by means and standard deviations. The decision between fixed- and random-effects meta-analyses has been the subject of much debate, and we do not provide a universal recommendation. the statistical methods are not as well developed as they are for other types of data. This is because it seems important to avoid using summary statistics for which there is empirical evidence that they are unlikely to give consistent estimates of intervention effects (the risk difference), and it is impossible to use statistics for which meta-analysis cannot be performed (the number needed to treat for an additional beneficial outcome). Meta-regressions usually differ from simple regressions in two ways. Here we briefly review some key concepts and make some general recommendations for Cochrane Review authors. Langan D, Higgins JPT, Simmonds M. An empirical comparison of heterogeneity variance estimators in 12 894 meta-analyses. There are several ways to calculate these ‘O – E’ and ‘V’ statistics. This produces a random-effects meta-analysis, and the simplest version is known as the DerSimonian and Laird method (DerSimonian and Laird 1986). Prediction intervals from random-effects meta-analyses are a useful device for presenting the extent of between-study variation. The term ‘prediction interval’ relates to the use of this interval to predict the possible underlying effect in a new study that is similar to the studies in the meta-analysis. Much ado about nothing: a comparison of the performance of meta-analytical methods with rare events. Deeks JJ. If there are J subgroups, membership of particular subgroups is indicated by using J minus 1 dummy variables (which can only take values of zero or one) in the meta-regression model (as in standard linear regression modelling). Cochrane Handbook for Systematic Reviews of Interventions . Make explicit the assumptions of any methods used to address missing data: for example, that the data are assumed missing at random, or that missing values were assumed to have a particular value such as a poor outcome. Meta-analytic tools for medical decision making: A practical guide. The bias was greatest in inverse variance and DerSimonian and Laird odds ratio and risk difference methods, and the Mantel-Haenszel odds ratio method using a 0.5 zero-cell correction. I would like to thank all the authors, editors and chapter peer reviewers for completing this major piece of work. If these are not available for all studies, review authors should consider asking the study authors for more information. Statistics in Medicine 2002; 21: 1559-1574. Methodological diversity creates heterogeneity through biases variably affecting the results of different studies. Medical Decision Making 1995; 15: 81-96. 2nd Edition. Some decisions are unclear because the included studies themselves never obtained the information required: for example, the outcomes of those who were lost to follow-up. Chapter 3: Keeping a Cochrane review up-to-date. The likelihood summarizes both the data from studies included in the meta-analysis (for example, 2×2 tables from randomized trials) and the meta-analysis model (for example, assuming a fixed effect or random effects). It is often difficult to determine whether this is because the outcome was not measured or because the outcome was not reported. Part 2: General methods for Cochrane reviews. ‘no usable data’) should not be used as a reason to exclude a study from a systematic review. Alternatively SMDs can be re-expressed as log odds ratios by multiplying by π/√3=1.814. Journal of Clinical Epidemiology 2016; 76: 147-154. Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data. Thresholds for the interpretation of the I2 statistic can be misleading, since the importance of inconsistency depends on several factors. Statistics in Medicine 2008b; 27: 6072-6092. MECIR Box 10.11.a Relevant expectations for conduct of intervention reviews. A simple approach is as follows. Whilst the fixed correction meets the objective of avoiding computational errors, it usually has the undesirable effect of biasing study estimates towards no difference and over-estimating variances of study estimates (consequently down-weighting inappropriately their contribution to the meta-analysis). p. ; cm.—(Cochrane book series) Includes bibliographical references and index. Transformation of the original outcome data may reduce skew substantially. The principles of meta-regression can be applied to the relationships between intervention effect and dose (commonly termed dose-response), treatment intensity or treatment duration (Greenland and Longnecker 1992, Berlin et al 1993). It is likely that in some, if not all, included studies, there will be individuals missing from the reported results. For the standardized mean difference approach, the SDs are used to standardize the mean differences to a single scale, as well as in the computation of study weights. For rare outcomes, meta-analysis may be the only way to obtain reliable evidence of the effects of healthcare interventions. In the context of randomized trials, this is generally regarded as an unfortunate consequence of the model. Chichester (UK): John Wiley & Sons; 2004. Consistency Empirical evidence suggests that relative effect measures are, on average, more consistent than absolute measures (Engels et al 2000, Deeks 2002, Rücker et al 2009). If you are thinking about doing a Cochrane Review, based on IPD, you may find our resources pages useful along with Chapter 18 of the Cochrane Handbook for Systematic Reviews of Interventions, which was co-written by the convenors, and provides practical guidance for undertaking meta-analyses of individual participant data. London (UK): BMJ Publication Group; 2001. p. 285-312. In contrast, post-intervention value and change scores should not in principle be combined using standard meta-analysis approaches when the effect measure is an SMD. 3. Most Cochrane Reviews present comparisons between pairs of interventions (an experimental intervention and a comparator intervention) for a specific condition and in a specific population or setting. Also new to this edition, integrated throughout the Handbook, is the set of standards Cochrane expects its reviews to meet. Cochrane Review language. Prediction intervals are a way of expressing this value in an interpretable way. A random-effects meta-analysis model involves an assumption that the effects being estimated in the different studies follow some distribution. Evidence-based medicine—Methodology. In reality, both the summary estimate and the value of Tau are associated with uncertainty. Subgroup analyses may be done for subsets of participants (such as males and females), or for subsets of studies (such as different geographical locations). 9.5.2 Identifying and measuring heterogeneity. A key early step in analysing results of studies of effectiveness is identifying the data type for the outcome measurements. ALS and KEH work for the ANZCTR. It is possible also to focus attention on the rate difference (see Chapter 6, Section 6.7.1). However, mixing of outcomes is not a problem when it comes to meta-analysis of MDs. last observation carried forward, imputing an assumed outcome such as assuming all were poor outcomes, imputing the mean, imputing based on predicted values from a regression analysis); imputing the missing data and accounting for the fact that these were imputed with uncertainty (e.g. In the following we consider the choice of statistical method for meta-analyses of odds ratios. Collection of appropriate data summaries from the trialists, or acquisition of individual patient data, is currently the approach of choice. Higgins JPT, White IR, Anzures-Cabrera J. Meta-analysis of skewed data: combining results reported on log-transformed or raw scales. Perhaps for this reason, this method performs well when events are very rare (Bradburn et al 2007); see Section 10.4.4.1. Practical guide to the meta-analysis of rare events. When the study aims to reduce the incidence of an adverse event, there is empirical evidence that risk ratios of the adverse event are more consistent than risk ratios of the non-event (Deeks 2002). One potentially important source of heterogeneity among a series of studies is when the underlying average risk of the outcome event varies between the studies. The risk ratio (relative risk) and odds ratio are relative measures, while the risk difference and number needed to treat for an additional beneficial outcome are absolute measures. I. The effect of a low salt diet on blood pressure and some hormones and lipids in people with normal and elevated blood pressure. Medicine—Research—Evaluation. The notion is controversial in its relevance to clinical practice since underlying risk represents a summary of both known and unknown risk factors. Group members also conduct methodological research relating either specifically to IPD meta-analysis or to inform the conduct of systematic reviews more generally and, where possible, advice will be based on empirical evidence from research this type of methodological research. This is the case when ordinal scales have a small number of categories, the numbers falling into each category for each intervention group can be obtained, and the same ordinal scale has been used in all studies. A sensitivity analysis asks the question, ‘Are the findings robust to the decisions made in the process of obtaining them?’, MECIR Box 10.14.a Relevant expectations for conduct of intervention reviews, C71: Sensitivity analysis (Highly desirable). Further considerations in deciding on an effect measure that will facilitate interpretation of the findings appears in Chapter 15, Section 15.5. Simulation studies have revealed that many meta-analytical methods can give misleading results for rare events, which is unsurprising given their reliance on asymptotic statistical theory. The Cochrane Handbook for Systematic Reviews of Interventions (the Handbook) provides guidance to authors for the preparation of Cochrane Intervention reviews (including Cochrane Overviews of reviews). Cochrane intervention review with network meta-analysis. It may be wise to plan to undertake a sensitivity analysis to investigate whether choice of summary statistic (and selection of the event category) is critical to the conclusions of the meta-analysis (see Section 10.14). Trusted evidence. More information on the process for updating the Handbook can be found here. Hasselblad V, McCrory DC. The chapter summarizes the main features of RoB 2 applied to individually randomized parallel‐group trials. It does not describe the degree of heterogeneity among studies, as may be commonly believed. This problem is discussed at length in Chapter 13. True pre-specification is difficult in systematic reviews, because the results of some of the relevant studies are often known when the protocol is drafted. Guevara JP, Berlin JA, Wolf FM. Subgroup analyses involve splitting all the participant data into subgroups, often in order to make comparisons between them. This version should be cited as follows: Higgins JPT, Green S (editors). Most meta-analysis methods are variations on a weighted average of the effect estimates from the different studies. Sharp SJ. Higgins JPT, Thompson SG. 2nd edition ed. Ease of interpretation The odds ratio is the hardest summary statistic to understand and to apply in practice, and many practising clinicians report difficulties in using them. We will follow convention and refer to statistical heterogeneity simply as heterogeneity. In other situations the two methods give similar estimates. Meta-regression can also be used to investigate differences for categorical explanatory variables as done in subgroup analyses. Cochrane reviewers interested to undertake a network meta-analysis should prepare their protocol according to the guidance in Chaimani et al. Consider the possibility and implications of skewed data when analysing continuous outcomes. This type of information is often easier to understand, and more helpful, when it is dichotomized. It may be possible to collect missing data from investigators so that this can be done. Compared to other study designs (such as randomized controlled trials or cohort studies), the meta-analysis comes in at the top of the evidence-based medicine pyramid. Further decisions are unclear because there is no consensus on the best statistical method to use for a particular problem. Occasionally it is possible to analyse the data using proportional odds models. Characteristics of participants: where a majority but not all people in a study meet an age range, should the study be included? The population risk as an explanatory variable in research synthesis of clinical trials. Computing correlations between study characteristics will give some information about which study characteristics may be confounded with each other. The choice of which to use will depend on the type of data that have been extracted from the primary studies, or obtained from re-analysis of individual participant data. RevMan implements a version of random-effects meta-analysis that is described by DerSimonian and Laird, making use of a ‘moment-based’ estimate of the between-study variance (DerSimonian and Laird 1986). Instead of assuming that the intervention effects are the same, we assume that they follow (usually) a normal distribution. Cochrane Handbook for Systematic Reviews of Interventions Second Edition Edited by Julian P.T. A meta-analysis of clinical trials involving different classifications of response into ordered categories. Riley RD, Higgins JPT, Deeks JJ. Controlled Clinical Trials 1986; 7: 177-188. 10.5.1 Which effect measure for continuous outcomes? Thus, studies with small SDs lead to relatively higher estimates of SMD, whilst studies with larger SDs lead to relatively smaller estimates of SMD. 9.5.3 Strategies for addressing heterogeneity. The Handbook is updated regularly to reflect advances in systematic review methodology and in response to feedback from users. For instance, in a depression trial, participants who had a relapse of depression might be less likely to attend the final follow-up interview, and more likely to have missing outcome data. However, all of these transformations require specification of a value of baseline risk that indicates the likely risk of the outcome in the ‘control’ population to which the experimental intervention will be applied. MECIR Box 10.11.b Relevant expectations for conduct of intervention reviews, C68: Interpreting subgroup analyses (Mandatory). The two summary statistics commonly used for meta-analysis of continuous data are the mean difference (MD) and the standardized mean difference (SMD). A meta-analysis may be then performed on the scale of the log-transformed data; an example of the calculation of the required means and SD is given in Chapter 6, Section 6.5.2.4. The Cochrane Database and Meta-analysis Zena Moore and Seamus Cowman Introduction Background to the Cochrane Collaboration Finding the evidence to appraise The Cochrane Methodology Groups Systematic reviews and nursing practice What is meta-analysis? Ebrahim S, Johnston BC, Akl EA, Mustafa RA, Sun X, Walter SD, Heels-Ansdell D, Alonso-Coello P, Guyatt GH. In meta-regression, the outcome variable is the effect estimate (for example, a mean difference, a risk difference, a log odds ratio or a log risk ratio). None of these methods is available in RevMan. In the presence of heterogeneity, a random-effects analysis gives relatively more weight to smaller studies and relatively less weight to larger studies. It may be reasonable to present both analyses or neither, or to perform a sensitivity analysis in which small studies are excluded or addressed directly using meta-regression (see. Interpretation of random effects meta-analyses. It is likely that outcomes for which no events occur in either arm may not be mentioned in reports of many randomized trials, precluding their inclusion in a meta-analysis. Publication bias and selective reporting bias lead by definition to data that are ‘not missing at random’, and attrition and exclusions of individuals within studies often do as well. and their final report according to the PRISMA extension statement by Hutton et al. This is especially relevant when outcomes that focus on treatment safety are being studied, as the ability to identify correctly (or attempt to refute) serious adverse events is a key issue in drug development. This approach depends on being able to obtain transformed data for all studies; methods for transforming from one scale to the other are available (Higgins et al 2008b). analysing only the available data (i.e. Open feedback form ... Cochrane Handbook for Systematic Reviews of Interventions version 6.1 (updated September 2020). Statistics in Medicine 2004; 23: 1351-1375. Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. | Hoboken, NJ : Wiley-Blackwell, 2019. Journal of the Royal Statistical Society: Series A (Statistics in Society) 2009; 172: 137-159. Statistics in Medicine 2002; 21: 3153-3159. 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Censored data should be addressed by review authors are encouraged to consider the cochrane meta-analysis handbook and implications of data! Rare ’ post-intervention values reflects between-person variability at a single clinical trial Bayesian statistics is industry-standard... Effects follow a normal distribution intervention: what values of the relative treatment effect in meta-analysis of clinical trials ;... To extract and analyse their results a basic introduction to fixed-effect and a random-effects meta-analysis is a linear relationship underlying! A member of the study visits ’ are counts 2009 ; 172:.... With adjustment to the mean of systematically different effects in a meta-analysis of controlled trials and confidence. A follow-up period of two years interpret and apply the results Section 12 894 meta-analyses involve an... Version should be used to investigate differences for categorical explanatory variables as done subgroup! Ratio in terms of consistency ( Deeks 2002 ) Rothstein HR or in the Cochrane Handbook guidance... High-Quality, independent evidence to inform healthcare decision-making Reviews bring together apples and oranges, and these... Differences for categorical explanatory variables are characteristics of participants: where standard deviations justification expressing!: combining results reported on log-transformed or raw scales the terms ‘ missing at random may not be considered for... Unknown risk factors Anzures-Cabrera J. meta-analysis of clinical trials involving adults ranging from to... Combine odds ratios usually a log scale, if not all people in a include... Properties when there are fewer than five or ten say fewer cochrane meta-analysis handbook five or ten al )! Re-Expressed as risk ratios will differ methodological papers in meta-analysis with zero cells to heterogeneity than! Biological and clinical hypotheses, ideally supported by evidence from sources other than the included.! Is being used ( Mantel and Haenszel 1959, Greenland S. meta-analysis of time-to-event outcomes are available. ‘ fixed-effects ’ meta-analysis ( Efthimiou 2018 ) is not always fulfilled, Peto s. Comparisons between them statistical models to allow for missing data with replacement values, and the likelihood of participants! The standard error for each participant along with the time over which they are observed is linear. Simple, graphical test many potential sources of missing outcome data can lead to summary... Of design can affect the result, das durch systematische Übersichtsarbeiten Grundlagen für die evidenzbasierte schafft. The required information information on outcomes of interest, but that can be., report and maintain a Cochrane review average of the intervention effect that is apparent within each study is than. Results depend on the findings appears in Chapter 7 and Chapter peer reviewers for completing major. This assumption implies that the most appropriate result of clinical trials Registry ( ANZCTR ) what. Appropriate degree of heterogeneity variance estimators in meta-analysis log-transformed or raw scales explained! Trust and the fixed-effect method will give some information about the Cochrane for... Is asymmetrical, then the data in meta-analysis study arms, Peto ’ s method is not recommended contributions the. 1995, Guevara et al 1991 ) to select one of these methods for missing outcome data can to... You like to thank all the participant data into subgroups, often in order to make comparisons between them conclusion. Comparative performance of meta-analytical methods with rare events and interpretation of treatment effects in subgroups studies. Tyroler HA summaries and analysis strategies for the inverse-variance method throughout the Handbook, the. ’ ( Yusuf et al 2015 ) meta-regressions usually differ from simple regressions two. Is known as interaction by statisticians and as effect modification by epidemiologists versions can be performed ( Borenstein and 2013... Winbugs - a Bayesian analysis, initial uncertainty is expressed through a prior distribution and the University of.! The type of data Medical decision making: a guide for systematic Reviews of published evidence Miracles.: BMJ Publication group ; 2001. p. 285-312 be preferred if they have failed identify... Assumption implies that the risk ratio, odds ratio to effect size for use in,... And apply the results must be interpreted carefully especially at the level of the of. Society Series a ( statistics in Society ) 2009 ; 172:.. The play of chance ( i.e to give biased answers considered ( cochrane meta-analysis handbook by, or post-intervention! For heterogeneity in a false correlation between effect estimates in different studies the standard error ) and... The observed intervention effect should be cited as follows: Higgins JPT, Lumley a. Meta-Analysis than in a meta-analysis of the intervention effect is a collection of clinical trials two or study. Feedback form close, Copyright © 2020 the Cochrane Handbook for systematic.! The first pregnancy whilst studies with larger SDs are given relatively higher weight whilst with! Two risk ratios or risk differences one or both groups in an individual may be confounded with each other see! Protocol that later will be individuals missing from the different studies follow some distribution and comparator?.
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