Standard errors meta-analysis

In meta-analysis, weighted averages are often used in order to incorporate standard errors (SEs) from measurements of parameters from independent studies. The weight for a given estimate is set equal to 1/SE. ... 

Methods of statistical meta-analysis may be useful for combining information across studies. There are 2 principal varieties of meta-analytic estimation (Normand 1995). In a hxed-effects analysis the observed variation among estimates is attributable to the statistical error associated with the individual estimates. An important step is to compute a weighted average of unbiased estimates, where the weight for an estimate is computed by means of its standard error estimate. In a random-effects analysis one allows for additional variation, beyond statistical error, making use of a htted random-effects model. 

A meta-analysis for continuous data cannot be calculated unless the pertinent standard deviations are known. Unfortunately, clinical reports often give the sample size and mean ratings for the various groups but do not report the standard deviations (or standard error of the mean), which are necessary for effect size calculations. Thus, investigators should always report the indices of variability (e.g., confidence intervals, SDs) for the critical variables related to their primary hypothesis. 

Using meta-analytic techniques based on the means and the standard errors presented graphically in the poster, we estimated pooled data of the four effective dosages of quetiapine both for the BPRS and the CGI severity of illness change scores from baseline to endpoint. Quetiapine produced an improvement of 0.43 effect-size units in comparison with placebo, a difference that was highly statistically significant and about the same improvement as haloperidol. Thus, based on the BPRS or PANSS, quetiapine was similar to neuroleptics in efficacy (i.e., differences were nonsignificant). Based on our meta-analysis, quetiapine is clearly superior to... 

There are important statistical considerations in meta-analyses for the evaluation of safety. In particular, statistical methods must be valid in the presence of sparse data. As discussed previously, safety outcomes may be infrequent. Some trials may not have any relevant events. We refer to these trials as zero-event trials. The statistical methods should provide estimates with good bias properties and with valid standard errors and confidence intervals in the presence of low event coimts and zero-event trials. In any meta-analysis, the overall estimator and associated standard errors and confidence intervals should be stratified by the trials. Simple pooling of data across the trials can result in misleading results because of Simspon s paradox. With stratification, the randomize comparisons within trials are maintained. 

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