Family medicine
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The trustworthiness of meta-analysis, a set of techniques used to quantitatively combine results from different studies, has recently been questioned. Problems with meta-analysis stem from bias in selecting studies to include in a meta-analysis and from combining study results when it is inappropriate to do so. ⋯ Funnel plots display the relationship of effect size versus sample size and help determine whether there is likely to have been selection bias in including studies in the meta-analysis. The L'Abbé plot displays the outcomes in both the treatment and control groups of included studies and helps to decide whether the studies are too heterogeneous to appropriately combine into a single measure of effect.
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Confounding is one of the most common and important biases in primary care research. This article explains the genesis and effects of two common misconceptions of confounding: 1) Confounding can be assessed with a statistical test. 2) All covariates should be included in a multivariate model to control confounding. Assessment of confounding by testing the statistical significance of baseline differences or the significance of a potential confounding factor in a multivariate model can produce underestimates or overestimates of the true association between an exposure and an outcome. ⋯ This may produce underestimates or overestimates of the effect in question, as well as artificially widened confidence intervals. Both of these misconceptions can lead to profound misinterpretation of research results. To prevent problems resulting from these misunderstandings, researchers should consider drawing causal models prior to conducting the research and use the change-in-estimate criterion, rather than a statistical test, to detect confounding.