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Meta-analysis fixed/random-effects

The fixed effects model considers the studies that have been combined as the totality of all the studies conducted. An alternative approach considers the collection of studies included in the meta-analysis as a random selection of the studies that have been conducted or a random selection of those that could have been conducted. This results in a slightly changed methodology, termed the random effects model The mathematics for the two models is a little different and the reader is referred to Fleiss (1993), for example, for further details. The net effect, however, of using a random effects model is to produce a slightly more conservative analysis with wider confidence intervals. [Pg.234]

Nevertheless, analyses as carried out by statisticians wedded to the type III philosophy show signs of many concessions to a type II approach. For example, it is a common habit to combine small centres for analysis. This, of course, down-weights their influence on the final result, thus producing an answer more like the type II approach. Furthermore, for meta-analyses, nobody uses an analysis which weights the trials equally (Senn, 2000). It is true that a random effects analysis (see below) is sometimes advocated, but where a fixed effects analysis is employed it is essentially a type II analysis which is used. A similar concession is made when fitting baselines and baseline-by-treatment interactions. (The way type II and type III analyses behave where continuous outcomes... [Pg.220]

Choices between fixed- or random-effect estimators arise wherever we have data sets with multiple levels within the experimental units for example, patients within trials for a meta-analysis, episodes per patient for a series of n-of-1 trials or patients within centres for a multicentre trial. The issue is an extremely complex one and it is difficult to give hard and fast rules as to which is appropriate. [Pg.222]

The basic idea behind a conventional meta-analysis is that it is based on within-trial contrasts. This eliminates the main effect of trial and such elimination corresponds to declaring the main effect of trial as fixed in a linear model. In this way the idea of concurrent control, which randomized trials themselves espouse, is observed in the meta-analysis. [Pg.266]

In discussion of meta-analysis, there is often much attention given to the random effects model versus the fixed effects model. Random effects models assume that the true treatment effects of the individual trials represent a random sample from some population. The random effects model estimates the population mean of the treatment effects and accounts for the variation in the observed effects. It is sometimes stated that the fixed effects model assumes that the individual trial effects are constant. However, this is not a necessary assumption. An alternative view is that the fixed effects model estimates the mean of the true treatment effects of the trials in the meta-analysis. Senn (2000) discussed the analogy with center effects in multicenter trials. In safety, random effects models may be problematic because of the need to estimate between-trial effects with sparse data. Additionally, the random effects model is less statistically powerful than the fixed effects model, albeit the hypotheses are different. In the fixed effects model, the variance estimate should account for trial effect differences either through stratification, conditioning, or modeling of fixed effects. [Pg.242]

This section presents the two most widely used statistical methods for meta-analysis, namely, the fixed effecfs model and the random effects model. In addition, we want to emphasize that an analysis based on crude pooling of adverse event numbers across different studies to compare treatment groups should be avoided, as the analysis is vulnerable to the mischief of Simpson s paradox (Chuang-Stein and Beltangady, 2011). [Pg.302]

Pooled estimate of relative risk imder the fixed effect model (Figure 16.2a) found that black patients had a relative risk of angioedema of 3.0 (95% C3 2.5-S.7) compared with nonblack patients. The pooled estimate and the Cl from the random effect model were almost equal to those from the fixed effects model because the P statistics did not suggest noticeable heterogeneity among the studies. Meta-analysis using odds ratio provided similar results as risk ratio because the proportion of patients with angioedema was very low in all studies. [Pg.310]

Meta-analysts must decide before conducting the analysis whether to employ a fixed-effects or a random-effects analysis model. These dilfer in the degree of influence each individual study s treatment effect is allowed to exert mathematically on the new treatment effect point estimate calcnlated by the meta-analysis this degree of inflnence is operationalized by the weight assigned to each treatment effect. [Pg.119]

The analysis of the data assembled in the database was made by a statistical meta-analysis approach (St-Pierre, 2001 Sauvant et al, 2008). Experiments were treated as random effects whereas tannin levels were treated as fixed effects using the following model ... [Pg.461]


See other pages where Meta-analysis fixed/random-effects is mentioned: [Pg.305]    [Pg.95]    [Pg.214]    [Pg.161]    [Pg.264]    [Pg.15]    [Pg.309]    [Pg.119]    [Pg.120]    [Pg.120]    [Pg.457]    [Pg.652]   
See also in sourсe #XX -- [ Pg.263 ]




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Effect Analysis

Fixed effect

Fixed-effects analysis

Meta effect

Meta-analysis

Random effects

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