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Background Meta-analyses based on individual patient data (IPD) are regarded as the gold standard for systematic reviews. However, the methods used for analysing and presenting results from IPD meta-analyses have received little discussion. Methods We review 44 IPD meta-analyses published during the years 1999 – 2001. We summarize whether they obtained all the data they sought, what types of approaches were used in the analysis, including assumptions of common or random effects, and how they examined the effects of covariates. Results Twenty-four out of 44 analyses focused on time-to-event outcomes, and most analyses (28) estimated treatment effects within each trial and then combined the results assuming a common treatment effect across trials. Three analyses failed to stratify by trial, analysing the data is if they came from a single mega-trial. Only nine analyses used random effects methods. Covariate-treatment interactions were generally investigated by subgrouping patients. Seven of the meta-analyses included data from less than 80% of the randomized patients sought, but did not address the resulting potential biases. Conclusions Although IPD meta-analyses have many advantages in assessing the effects of health care, there are several aspects that could be further developed to make fuller use of the potential of these time-consuming projects. In particular, IPD could be used to more fully investigate the influence of covariates on heterogeneity of treatment effects, both within and between trials. The impact of heterogeneity, or use of random effects, are seldom discussed. There is thus considerable scope for enhancing the methods of analysis and presentation of IPD meta-analysis. [ABSTRACT FROM AUTHOR]

Meta-analyses of randomized trials aim to summarise the effects of interventions across many patients, and can seem remote from the clinical issue of how individual patients should be treated and which patient groups will benefit the most from treatment. One method that attempts to address this point entails relating the overall effect in every trial to summaries of patient characteristics. This is called meta-regression. The interpretation of such analyses is not straightforward, however, because of a combination of confounding and other biases. Much more useful is to compare the outcomes for patient subgroups within trials and combine these results across trials. Unfortunately this method is rarely possible using published information, so analyses of individual patient data from trials are necessary. Also, although meta-analyses generally summarise an intervention's effect as a relative risk reduction, the groups of patients with the greatest absolute risk reduction have the most to gain. [ABSTRACT FROM AUTHOR]

Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The usual test statistic is computed by summing the squared deviations of each study's estimate from the overall meta-analytic estimate, weighting each study's contribution in the same manner as in the meta-analysis.

Discusses the relative treatment benefit and underlying risk of interpreting clinical trials using meta-analysis. Variation in treatment benefits; Advantages of evaluation of measurable patient characteristics over meta-analysis of clinical trials; Link between treatment effects and underlying risks.

Investigates the sources of heterogeneity in meta-analysis. Distinction between the concepts of clinical and statistical heterogeneity; Purpose of meta-analysis of a set of clinical trials; Concept of meta-analysis; Correlation of serum cholesterol concentration with the risk of ischemic heart disease.