Meta-analysis in the assessment of treatment outcome

Abstract

Randomized clinical trials provide the most reliable estimates of the benefits and harms of treatments. Limited sample sizes restrict their power to allow informative analyses of secondary outcomes, or patient subgroups. The overall results of trials only apply to the average patient and clinical application ignores the individual patient differences.Meta-analysis in the context of a systematic review can produce more precise estimates of effect by combining the results of primary studies. This is particularly valuable for investigating rare, but important outcomes such as suicide. Variations between the trial-specific results can be investigated by meta-regression. Individual patient data meta-analyses (IPDMAs) are potentially much more powerful designs because they allow analysis of patient-level variables. As more genetic factors are identified that might account for treatment variability between individuals, IPDMAs offer a powerful strategy that can be used on existing trial data sets. Despite practical difficulties, IPDMAs are increasingly being conducted.

Bench-to-bedside review: Avoiding pitfalls in critical care meta-analysis – funnel plots, risk estimates, types of heterogeneity, baseline risk and the ecologic fallacy

“…If trials are split into too many subgroups or too many factors are incorporated into meta-regressions, the probability of a false-positive conclusion due to multiple comparisons increases. Conversely, the small number of trials on which most of these analyses are based means power is limited, so true associations may be missed [ 26 ]. As with subgroup analyses in clinical trials, splitting a meta-analysis into subgroups is considered by many to be hypothesis generating at best.…”