Both use the moment-based approach to estimating the amount of between-studies variation. Subgroup analyses of subsets of participants within studies are uncommon in systematic reviews based on published literature because sufficient details to extract data about separate participant types are seldom published in reports. Chapter 10: Analysing data and undertaking meta-analyses | Cochrane Training. Computing correlations between study characteristics will give some information about which study characteristics may be confounded with each other. An underlying assumption associated with the use of rates is that the risk of an event is constant across participants and over time. The standard practice in meta-analysis of odds ratios and risk ratios is to exclude studies from the meta-analysis where there are no events in both arms. 6), and can be used for conducting a meta-analysis in advanced statistical software packages (Whitehead and Jones 1994). For instance, if some quality-of-life questionnaires were lost in the postal system, this would be unlikely to be related to the quality of life of the trial participants who completed the forms.
Many studies are too small to provide convincing evidence about intervention effects in isolation. Dear guest, you are not a registered member. If random-effects models are used for the analysis within each subgroup, then the statistics relate to variation in the mean effects in the different subgroups. 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). American Journal of Epidemiology 1999; 150: 469-475. As this is a common situation in Cochrane Reviews, the Mantel-Haenszel method is generally preferable to the inverse variance method in fixed-effect meta-analyses. It uses an inverse-variance approach, but uses an approximate method of estimating the log odds ratio, and uses different weights. Chapter 10 review states of matter answer key. It is essential to consider the extent to which the results of studies are consistent with each other (see MECIR Box 10. Thus, larger studies, which have smaller standard errors, are given more weight than smaller studies, which have larger standard errors. Their performance has been judged suboptimal either through results being biased, confidence intervals being inappropriately wide, or statistical power being too low to detect substantial differences. Rate data occur if counts are measured for each participant along with the time over which they are observed. Furthermore, failure to report that outcomes were measured may be dependent on the unreported results (selective outcome reporting bias; see Chapter 7, Section 7. Uncheck the procedures we don't know yet (prediction intervals, and 1-way ANOVA, chi-square tests), press Submit, and have fun! 2), this may be viewed as an investigation of how a categorical study characteristic is associated with the intervention effects in the meta-analysis.
This assumption implies that the observed differences among study results are due solely to the play of chance (i. that there is no statistical heterogeneity). Chapter 10 key issue 1. Conclusions about differences in effect due to differences in dose (or similar factors) are on stronger ground if participants are randomized to one dose or another within a study and a consistent relationship is found across similar studies. Methods are available for dealing with this, and for combining data from scales that are related but have different definitions for their categories (Whitehead and Jones 1994). This is particularly advantageous when the number of studies in the meta-analysis is small, say fewer than five or ten. Meta-analyses are usually illustrated using a forest plot. Authors should be particularly cautious about claiming that a dose-response relationship does not exist, given the low power of many meta-regression analyses to detect genuine relationships.
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. This will happen whenever the I 2 statistic is greater than zero, even if the heterogeneity is not detected by the Chi2 test for heterogeneity (see Section 10. Risk difference methods superficially appear to have an advantage over odds ratio methods in that the risk difference is defined (as zero) when no events occur in either arm. It is advisable to perform analyses both with and without outlying studies as part of a sensitivity analysis (see Section 10. In the context of randomized trials, this is generally regarded as an unfortunate consequence of the model. Prev Sci 2013; 14: 134-143. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. Epidemiologic Reviews 1987; 9: 1-30. Chapter 10 assessment answer key. In both cases, the implications of notable heterogeneity should be addressed. These events may not happen at all, but if they do happen there is no theoretical maximum number of occurrences for an individual. For example, if the eligibility of some studies in the meta-analysis is dubious because they do not contain full details, sensitivity analysis may involve undertaking the meta-analysis twice: the first time including all studies and, second, including only those that are definitely known to be eligible.
A meta-analysis of clinical trials involving different classifications of response into ordered categories. DiGuiseppi C, Higgins JPT. Private interests often lobby government for particularized benefits, which are narrowly distributed. Other examples of missing summary data are missing sample sizes (particularly those for each intervention group separately), numbers of events, standard errors, follow-up times for calculating rates, and sufficient details of time-to-event outcomes. Some possible reasons for missing data. As Ralph and Piggy discuss Simon's murder the following morning, Ralph clutches the conch shell to him for solace, but the once-potent symbol of order and civilization is now useless. The area of the block and the confidence interval convey similar information, but both make different contributions to the graphic. Meta-regression can also be used to investigate differences for categorical explanatory variables as done in subgroup analyses. When there is little information, either because there are few studies or if the studies are small with few events, a random-effects analysis will provide poor estimates of the amount of heterogeneity (i. of the width of the distribution of intervention effects). Subgroup analyses may be done as a means of investigating heterogeneous results, or to answer specific questions about particular patient groups, types of intervention or types of study. 3; see also Chapter 8, Section 8. Occasionally authors encounter a situation where data for the same outcome are presented in some studies as dichotomous data and in other studies as continuous data. Lord of the Flies Chapter 10 Summary & Analysis. Missing data can also affect subgroup analyses.
Prediction intervals have proved a popular way of expressing the amount of heterogeneity in a meta-analysis (Riley et al 2011). This should only be done informally by comparing the magnitudes of effect. 05, is sometimes used to determine statistical significance. Reconsider the effect measure. A common practical problem associated with including change-from-baseline measures is that the SD of changes is not reported. Meta-regressions usually differ from simple regressions in two ways. This Chi2 (χ2, or chi-squared) test is included in the forest plots in Cochrane Reviews. Hence, subgroup analyses suffer the limitations of any observational investigation, including possible bias through confounding by other study-level characteristics. Alternatively, Poisson regression approaches can be used (Spittal et al 2015). Chapter 10 Review Test and Answers. Sensitivity analyses are sometimes confused with subgroup analysis. The square root of this number (i. Tau) is the estimated standard deviation of underlying effects across studies. Although some sensitivity analyses involve restricting the analysis to a subset of the totality of studies, the two methods differ in two ways. The presence of heterogeneity affects the extent to which generalizable conclusions can be formed. It is unclear, though, when working with published results, whether failure to mention a particular adverse event means there were no such events, or simply that such events were not included as a measured endpoint.
Address the potential impact of missing data on the findings of the review in the Discussion section. Lobbyists also target the executive and judiciary branches. The analysis again can be performed using the generic inverse-variance method (Hasselblad and McCrory 1995, Guevara et al 2004). Note that the ability to enter estimates and standard errors creates a high degree of flexibility in meta-analysis.
Different meta-analysts may analyse the same data using different prior distributions and obtain different results. Change-from-baseline outcomes may also be preferred if they have a less skewed distribution than post-intervention measurement outcomes. However, deciding on a cut-point may be arbitrary, and information is lost when continuous data are transformed to dichotomous data. The length of the creek between 1, 600 meters and 1, 300 meters elevation is 2. 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. The use of network meta-analysis to compare more than two interventions is addressed in Chapter 11.
Selection of summary statistics for continuous data is principally determined by whether studies all report the outcome using the same scale (when the mean difference can be used) or using different scales (when the standardized mean difference is usually used). The methods we describe in the remainder of this chapter are for subgroups of studies. Here, allocation sequence concealment, being either adequate or inadequate, is a categorical characteristic at the study level. Meta-regression may best be used for this purpose, although it is not implemented in RevMan (see Section 10. It is difficult to establish the validity of any particular distributional assumption, and this is a common criticism of random-effects meta-analyses. A pragmatic approach is to plan to undertake both a fixed-effect and a random-effects meta-analysis, with an intention to present the random-effects result if there is no indication of funnel plot asymmetry. Similarly, summary data for an outcome, in a form that can be included in a meta-analysis, may be missing.
However, calculation of a change score requires measurement of the outcome twice and in practice may be less efficient for outcomes that are unstable or difficult to measure precisely, where the measurement error may be larger than true between-person baseline variability. Skew can sometimes be diagnosed from the means and SDs of the outcomes. False negative and false positive significance tests increase in likelihood rapidly as more subgroup analyses are performed. Prognostic factors are not good candidates for subgroup analyses unless they are also believed to modify the effect of intervention. To overcome these challenges, group leaders may offer incentives to members or potential members to help them mobilize. Bayesian statistics is an approach to statistics based on a different philosophy from that which underlies significance tests and confidence intervals. The random-effects method and the fixed-effect method will give identical results when there is no heterogeneity among the studies.
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