Meta-analyses are often, but not always, important components of a systematic review procedure. For example, Wanous and colleagues examined four pairs of meta-analyses on the four topics of (a) job performance and satisfaction relationship, (b) realistic job previews, (c) correlates of role conflict and ambiguity, and (d) the job satisfaction and absenteeism relationship, and illustrated how various judgement calls made by the researchers produced different results. This makes meta-analysis malleable in the sense that these methodological choices made in completing a meta-analysis are not determined but may affect the results. In performing a meta-analysis, an investigator must make choices which can affect the results, including deciding how to search for studies, selecting studies based on a set of objective criteria, dealing with incomplete data, analyzing the data, and accounting for or choosing not to account for publication bias. For example, if there are two groups of patients experiencing different treatment effects studies in two randomised control trials (RCTs) reporting conflicting results, the meta-analytic average is representative of neither group, similarly to averaging the weight of apples and oranges, which is neither accurate for apples nor oranges. Meta-analysis has also been criticized for averaging differences among heterogeneous studies because these differences could potentially inform clinical decisions. If individual studies are systematically biased due to questionable research practices (e.g., data dredging, data peeking, dropping studies) or the publication bias at the journal level, the meta-analytic estimate of the overall treatment effect may not reflect the actual efficacy of a treatment. However, there are some methodological problems with meta-analysis. Not only can meta-analyses provide an estimate of the unknown effect size, it also has the capacity to contrast results from different studies and identify patterns among study results, sources of disagreement among those results, or other interesting relationships that may come to light with multiple studies. Meta-analytic results are considered the most trustworthy source of evidence by the evidence-based medicine literature. The aim then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived. Meta-analyses can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error. ![]() May use bayesian frameworks or meta-regression analysis.A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. Creates a web-like analysis called a Network Diagram or Network Comparison. Synthesis: Statistical analysis when possible (heterogeneity a potential problem with indirect comparisons)-uses different statistical methods than a systematic review. Still includes Risk of Bias and quality of evidence assessments. Requires a lot more screening of trials.Īppraisal: Formal quality assessment of all studies. ![]() Selection: Same as a systematic review-based upon clear inclusion/exclusion criteria. See our Systematic Review Search Service for help conducting the search! ![]() Sources and searches: Requires a large search to locate all of the trials using transparent & reproducible methods. Combines indirect (triangular approach) or direct comparisons (pairwise approach). Question: Addresses PICO for multiple interventions or treatments (3 or more). ![]() Depends on many factors such as but not limited to: resources available, the quantity and quality of the literature, and the expertise or experience of reviewers" (Grant et al.
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