Background Statistical tests of heterogeneity are very popular in meta-analyses, as

Background Statistical tests of heterogeneity are very popular in meta-analyses, as heterogeneity might indicate subgroup effects. bilaterality and treatment effects was observed Rabbit Polyclonal to PPP4R2 (which was also found in an Wedelolactone IC50 individual patient data meta-analysis of the included trials: p-value for conversation 0.021). Conclusions A modification of the forest plot, by including an additional (vertical) axis indicating the proportion of a certain subgroup variable, is usually a qualitative, visual, and easy-to-interpret method to explore potential subgroup effects in studies included in meta-analyses. Background Practice guidelines increasingly rely on systematic reviews and meta-analyses. The ultimate purpose of a meta-analysis is usually to produce an overall estimate of the effect of an intervention by quantitatively combining study results. However, several issues arise in the process of integrating evidence. One of the Wedelolactone IC50 main issues concerns heterogeneity, i.e. the extent to which different studies give comparable or different results. Statistical assessments are routinely available to evaluate the presence of statistical heterogeneity (between-study heterogeneity) in meta-analysis [1-3]. Strictly speaking, however, one is not really interested in statistical heterogeneity. What one is interested in is usually clinical heterogeneity, i.e., specific causes that underlie heterogeneity across studies, especially since the direction and magnitude of the effect in the meta-analysis is usually often used to guide decisions about clinical practice for a wide range of patients. Yet, relevant subgroup effects may not be revealed by a test for (statistical) heterogeneity. In meta-regression analysis the relation between a certain subgroup characteristic and the size of the treatment effect can in fact be quantified, but such analyses might be difficult to conduct or interpret, and Wedelolactone IC50 rely on several assumptions. Furthermore, the observed treatment effect and subgroup variables are actually estimates, rather than true values. Ordinary meta-regression analysis (weighted least squares) does not take measurement errors in treatment and subgroup variables adequately into account and may consequently give a biased estimate of the slope of the regression line [4]. We will show that clinically relevant subgroup effects can be explored in a simple manner by modifying the forest Wedelolactone IC50 plot. Methods Assessments for heterogeneity Several tests have been developed to assess heterogeneity. The so-called Cochrane’s Q (or Cochrane’s 2 test) weights the observed variation in treatment effects by the inverse of the variation in each study [5]. A large value of Q indicates large differences between studies, and hence, the effects from the included studies can be considered heterogeneous [2]. A modification of Cochrane’s Q is the measure I2, which is the ratio of variation that exceeds chance variation and the total variation in the treatment effects. Possible values for I2 range from zero to one, with a high value for I2 indicating much heterogeneity. Both Q and I2 are standardized steps, meaning that they don’t depend around the metric of the effect size. A third measure of heterogeneity, indicating the variance of the true effect sizes is usually T2, where (similar to Q and I2) large values of T2 indicate heterogeneity. This method of estimating the variance between studies (T2) is also known as the method of moments, or the DerSimonian and Laird method [6]. A fourth measure is the prediction interval, which indicates the distribution of true effect sizes and is based on T2 [2]. Cochrane’s Q is usually sensitive to the number of studies and especially when the number of studies included in a meta-analysis is usually small, Cochrane’s Q too often leads to false-positive conclusions (too large type I error) [7]. The modification I2 takes account of the number of included studies and has a correct probability of a type I error [3]. The measure T2 is usually insensitive to the number of studies as well, but sensitive to the metric of the effect size [2]. Currently, I2 appears to be used routinely in most published meta-analyses. Interestingly, the observed amount of heterogeneity depends on the effect measure that is considered in a meta-analysis: little heterogeneity when considering odds ratios implies large heterogeneity when considering risk differences and vice versa [8]. The reason for this is analogous to effect measure modification in a single study: if odds ratios are the same between strata (e.g., age categories) of a single study, risk differences are likely to differ between strata. Consequences of heterogeneity Assessments for heterogeneity indicate whether the variation in observed effects is usually either large or small. When heterogeneity is usually low (non-significant) for.