
Estimating the sample mean and standard deviation from commonly reported quantiles in metaanalysis
Researchers increasingly use metaanalysis to synthesize the results of ...
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Testing normality using the summary statistics with application to metaanalysis
As the most important tool to provide highlevel evidencebased medicine...
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Optimally estimating the sample mean and standard deviation from the fivenumber summary
When reporting the results of clinical studies, some researchers may cho...
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Detecting the skewness of data from the sample size and the fivenumber summary
For clinical studies with continuous outcomes, when the data are potenti...
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Twosample aggregate data metaanalysis of medians
We consider the problem of metaanalyzing twogroup studies that report ...
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How to estimate the sample mean and standard deviation from the five number summary?
In some clinical studies, researchers may report the five number summary...
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Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
We present a novel family of deep neural architectures, named partially ...
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ABCMETAapp: R Shiny Application for Simulationbased Estimation of Mean and Standard Deviation for Metaanalysis via Approximate Bayesian Computation (ABC)
Background and Objective: In metaanalysis based on continuous outcome, estimated means and corresponding standard deviations from the selected studies are key inputs to obtain a pooled estimate of the mean and its confidence interval. We often encounter the situation that these quantities are not directly reported in the literatures. Instead, other summary statistics are reported such as median, minimum, maximum, quartiles, and study sample size. Based on available summary statistics, we need to estimate estimates of mean and standard deviation for metaanalysis. Methods: We developed a R Shiny code based on approximate Bayesian computation (ABC), ABCMETA, to deal with this situation. Results: In this article, we present an interactive and userfriendly R Shiny application for implementing the proposed method (named ABCMETAapp). In ABCMETAapp, users can choose an underlying outcome distribution other than the normal distribution when the distribution of the outcome variable is skewed or heavy tailed. We show how to run ABCMETAapp with examples. Conclusions: ABCMETAapp provides a R Shiny implementation. This method is more flexible than the existing analytical methods since estimation can be based on five different distribution (Normal, Lognormal, Exponential, Weibull, and Beta) for the outcome variable.
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