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  <titleInfo>
    <nonSort>The </nonSort>
    <title>prevention and treatment of missing data in clinical trials</title>
  </titleInfo>
  <name type="corporate">
    <namePart>National Research Council (U.S.)</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
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  <name type="corporate">
    <namePart>National Research Council (U.S.)</namePart>
    <namePart>Committee on National Statistics.</namePart>
  </name>
  <name type="corporate">
    <namePart>National Academies Press (U.S.)</namePart>
  </name>
  <typeOfResource>text</typeOfResource>
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    <place>
      <placeTerm type="text">Washington, D.C</placeTerm>
    </place>
    <publisher>National Academies Press</publisher>
    <dateIssued>2010</dateIssued>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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  <physicalDescription>
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    <extent>144 p. : ill.</extent>
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  <abstract>"Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data."--Publisher's description.</abstract>
  <tableOfContents>Introduction and background -- Trial designs to reduce the frequency of missing data -- Trial strategies to reduce the frequency of missing data -- Drawing inferences from incomplete data -- Principles and methods of sensitivity analyses -- Conclusions and recommendations.</tableOfContents>
  <tableOfContents>HKBU library</tableOfContents>
  <note type="statement of responsibility">Panel on Handling Missing Data in Clinical Trials, Committee on National Statistics, Division of Behavioral and Social Sciences, National Research Council of the National Academies.</note>
  <note type="venue">YT2025 M09</note>
  <subject authority="lcsh">
    <topic>Clinical trials</topic>
    <topic>Statistical methods</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Experimental design</topic>
  </subject>
  <subject authority="mesh">
    <topic>Randomized Controlled Trials as Topic</topic>
    <topic>statistics &amp; numerical data</topic>
  </subject>
  <subject authority="mesh">
    <topic>Biomedical Research</topic>
    <topic>standards</topic>
  </subject>
  <classification authority="lcc">R853 N277 P 2010</classification>
  <identifier type="isbn">9780309158145</identifier>
  <identifier type="isbn">0309158141</identifier>
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    <recordCreationDate encoding="marc">120511</recordCreationDate>
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