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  <titleInfo>
    <title>Applied meta-analysis with R and Stata</title>
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  <name type="personal">
    <namePart>Chen, Ding-Geng</namePart>
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  <name type="personal">
    <namePart>Peace, Karl E.</namePart>
    <namePart type="date">1941-</namePart>
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    <dateIssued encoding="marc">2021</dateIssued>
    <edition>Second edition.</edition>
    <issuance>monographic</issuance>
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  <abstract>Review of the First Edition: The authors strive to reduce theory to a minimum, which makes it a self-learning text that is comprehensible for biologists, physicians, etc. who lack an advanced mathematics background. Unlike in many other textbooks, R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses, graphics, and simulations directly relating to meta-analysisA useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs. --Journal of Applied Statistics Statistical Meta-Analysis with R and Stata, Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions, which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions. What's New in the Second Edition: Adds Stata programs along with the R programs for meta-analysis Updates all the statistical meta-analyses with R/Stata programs Covers fixed-effects and random-effects MA, meta-regression, MA with rare-event, and MA-IPD vs MA-SS Adds five new chapters on multivariate MA, publication bias, missing data in MA, MA in evaluating diagnostic accuracy, and network MA Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health, medical research, governmental agencies, and the pharmaceutical industry.</abstract>
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  <note type="statement of responsibility">Ding-Geng (Din) Chen, Karl E. Peace.</note>
  <subject authority="lcsh">
    <titleInfo>
      <title>Stata</title>
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  <subject authority="lcsh">
    <topic>Meta-analysis</topic>
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  <subject authority="lcsh">
    <topic>R (Computer program language)</topic>
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  <subject authority="bisacsh">
    <topic>MATHEMATICS / Probability &amp; Statistics / General</topic>
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  <subject authority="bisacsh">
    <topic>MEDICAL / Pharmacology</topic>
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  <classification authority="lcc">R853.M48</classification>
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      <title>Chapman &amp; Hall/CRC biostatistics series</title>
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