Impact of Epidural Analgesia on Mortality and Morbidity After Surgery: Systematic Review and Meta-analysis of Randomized Controlled Trials

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Abstract

To quantify benefit and harm of epidural analgesia, compared with systemic opioid analgesia, in adults having surgery under general anesthesia.

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Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.

David Moher, Alessandro Liberati, Jennifer Tetzlaff … (2009)

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Estimating the mean and variance from the median, range, and the size of a sample

Stela Pudar Hozo, Benjamin Djulbegovic, Iztok Hozo (2005)

Background Usually the researchers performing meta-analysis of continuous outcomes from clinical trials need their mean value and the variance (or standard deviation) in order to pool data. However, sometimes the published reports of clinical trials only report the median, range and the size of the trial. Methods In this article we use simple and elementary inequalities and approximations in order to estimate the mean and the variance for such trials. Our estimation is distribution-free, i.e., it makes no assumption on the distribution of the underlying data. Results We found two simple formulas that estimate the mean using the values of the median (m), low and high end of the range (a and b, respectively), and n (the sample size). Using simulations, we show that median can be used to estimate mean when the sample size is larger than 25. For smaller samples our new formula, devised in this paper, should be used. We also estimated the variance of an unknown sample using the median, low and high end of the range, and the sample size. Our estimate is performing as the best estimate in our simulations for very small samples (n ≤ 15). For moderately sized samples (15 70), the formula range/6 gives the best estimator for the standard deviation (variance). We also include an illustrative example of the potential value of our method using reports from the Cochrane review on the role of erythropoietin in anemia due to malignancy. Conclusion Using these formulas, we hope to help meta-analysts use clinical trials in their analysis even when not all of the information is available and/or reported.

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Meta-analysis in clinical research.

K L'Abbé, A S Detsky, K O'Rourke (1987)

Meta-analysis is the process of combining study results that can be used to draw conclusions about therapeutic effectiveness or to plan new studies. We review important design and statistical issues of this process. The design issues include protocol development, objectives, literature search, publication bias, measures of study outcomes, and quality of the data. The statistical issues include consistency (homogeneity) of study outcomes, and techniques for pooling results from several studies. Guidelines are provided to assess the quality of meta-analyses based on our discussion of the design and statistical issues. Limitations and areas for further development of this approach are discussed; researchers should come to a general agreement on how to conduct meta-analysis. As an explicit strategy for summarizing results, meta-analysis may help clinicians and researchers better understand the findings of clinical studies.