Codesystem-attribute-estimate-type.xml

Definition for Code System AttributeEstimateType

<?xml version="1.0" encoding="UTF-8"?>

<CodeSystem xmlns="http://hl7.org/fhir">
  <id value="attribute-estimate-type"/> 
  <meta> 
    <lastUpdated value="2021-12-20T14:08:35+11:00"/> 
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  <text> 
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      <p> This code system http://terminology.hl7.org/CodeSystem/attribute-estimate-type defines
         the following codes:</p> 
      
      
      <table class="codes">
        
        
        <tr> 
          
          
          <td style="white-space:nowrap">
            
            
            <b> Code</b> 
          
          
          </td> 
          
          
          <td> 
            
            
            <b> Display</b> 
          
          
          </td> 
          
          
          <td> 
            
            
            <b> Definition</b> 
          
          
          </td> 
        
        
        </tr> 
        

        
        <tr> 
          
          
          <td style="white-space:nowrap">0000419
            
            
            <a name="attribute-estimate-type-0000419"> </a> 
          
          
          </td> 
          
          
          <td> Cochran's Q statistic</td> 
          
          
          <td> A measure of heterogeneity across study computed by summing the squared deviations of
             each study's estimate from the overall meta-analytic estimate, weighting each study's
             contribution in the same manner as in the meta-analysis.</td> 
        
        
        </tr> 



        
        <tr> 
          
          
          <td style="white-space:nowrap">C53324
            
            
            <a name="attribute-estimate-type-C53324"> </a> 
          
          
          </td> 
          
          
          <td> Confidence interval</td> 
          
          
          <td> A range of values considered compatible with the observed data at the specified confidence
             level</td> 
        
        
        </tr> 



        
        <tr> 
          
          
          <td style="white-space:nowrap">0000455
            
            
            <a name="attribute-estimate-type-0000455"> </a> 
          
          
          </td> 
          
          
          <td> Credible interval</td> 
          
          
          <td> An interval of a posterior distribution which is such that the density at any point inside
             the interval is greater than the density at any point outside and that the area under
             the curve for that interval is equal to a prespecified probability level. For any probability
             level there is generally only one such interval, which is also often known as the highest
             posterior density region. Unlike the usual confidence interval associated with frequentist
             inference, here the intervals specify the range within which parameters lie with a certain
             probability. The bayesian counterparts of the confidence interval used in frequentists
             statistics.</td> 
        
        
        </tr> 



        
        <tr> 
          
          
          <td style="white-space:nowrap">0000420
            
            
            <a name="attribute-estimate-type-0000420"> </a> 
          
          
          </td> 
          
          
          <td> I-squared</td> 
          
          
          <td> The percentage of total variation across studies that is due to heterogeneity rather than
             chance. I2 can be readily calculated from basic results obtained from a typical meta-analysis
             as i2 = 100%×(q - df)/q, where q is cochran's heterogeneity statistic and df the degrees
             of freedom. Negative values of i2 are put equal to zero so that i2 lies between 0% and
             100%. A value of 0% indicates no observed heterogeneity, and larger values show increasing
             heterogeneity. Unlike cochran's q, it does not inherently depend upon the number of studies
             considered. A confidence interval for i² is constructed using either i) the iterative
             non-central chi-squared distribution method of hedges and piggott (2001); or ii) the test-based
             method of higgins and thompson (2002). The non-central chi-square method is currently
             the method of choice (higgins, personal communication, 2006) – it is computed if the 'exact'
             option is selected.</td> 
        
        
        </tr> 



        
        <tr> 
          
          
          <td style="white-space:nowrap">C53245
            
            
            <a name="attribute-estimate-type-C53245"> </a> 
          
          
          </td> 
          
          
          <td> Interquartile range</td> 
          
          
          <td> The difference between the 3d and 1st quartiles is called the interquartile range and
             it is used as a measure of variability (dispersion).</td> 
        
        
        </tr> 



        
        <tr> 
          
          
          <td style="white-space:nowrap">C44185
            
            
            <a name="attribute-estimate-type-C44185"> </a> 
          
          
          </td> 
          
          
          <td> P-value</td> 
          
          
          <td> The probability of obtaining the results obtained, or more extreme results, if the hypothesis
             being tested and all other model assumptions are true</td> 
        
        
        </tr> 



        
        <tr> 
          
          
          <td style="white-space:nowrap">C38013
            
            
            <a name="attribute-estimate-type-C38013"> </a> 
          
          
          </td> 
          
          
          <td> Range</td> 
          
          
          <td> The difference between the lowest and highest numerical values; the limits or scale of
             variation.</td> 
        
        
        </tr> 



        
        <tr> 
          
          
          <td style="white-space:nowrap">C53322
            
            
            <a name="attribute-estimate-type-C53322"> </a> 
          
          
          </td> 
          
          
          <td> Standard deviation</td> 
          
          
          <td> A measure of the range of values in a set of numbers. Standard deviation is a statistic
             used as a measure of the dispersion or variation in a distribution, equal to the square
             root of the arithmetic mean of the squares of the deviations from the arithmetic mean.</td> 
        
        
        </tr> 



        
        <tr> 
          
          
          <td style="white-space:nowrap">0000037
            
            
            <a name="attribute-estimate-type-0000037"> </a> 
          
          
          </td> 
          
          
          <td> Standard error of the mean</td> 
          
          
          <td> The standard deviation of the sample-mean's estimate of a population mean. It is calculated
             by dividing the sample standard deviation (i.e., the sample-based estimate of the standard
             deviation of the population) by the square root of n , the size (number of observations)
             of the sample.</td> 
        
        
        </tr> 



        
        <tr> 
          
          
          <td style="white-space:nowrap">0000421
            
            
            <a name="attribute-estimate-type-0000421"> </a> 
          
          
          </td> 
          
          
          <td> Tau squared</td> 
          
          
          <td> An estimate of the between-study variance in a random-effects meta-analysis. The square
             root of this number (i.e. Tau) is the estimated standard deviation of underlying effects
             across studies.</td> 
        
        
        </tr> 



        
        <tr> 
          
          
          <td style="white-space:nowrap">C48918
            
            
            <a name="attribute-estimate-type-C48918"> </a> 
          
          
          </td> 
          
          
          <td> Variance</td> 
          
          
          <td> A measure of the variability in a sample or population. It is calculated as the mean squared
             deviation (MSD) of the individual values from their common mean. In calculating the MSD,
             the divisor n is commonly used for a population variance and the divisor n-1 for a sample
             variance.</td> 
        
        
        </tr> 
      
      
      </table> 
    
    
    </div> 
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  <url value="http://terminology.hl7.org/CodeSystem/attribute-estimate-type"/> 
  <identifier> 
    <system value="urn:ietf:rfc:3986"/> 
    <value value="urn:oid:2.16.840.1.113883.4.642.1.1541"/> 
  </identifier> 
  <version value="4.3.0-snapshot1"/> 
  <name value="AttributeEstimateType"/> 
  <title value="AttributeEstimateType"/> 
  <status value="draft"/> 
  <experimental value="false"/> 
  <date value="2021-08-05T12:00:00+11:00"/> 
  <publisher value="HL7 (FHIR Project)"/> 
  <contact> 
    <telecom> 
      <system value="url"/> 
      <value value="http://hl7.org/fhir"/> 
    </telecom> 
    <telecom> 
      <system value="email"/> 
      <value value="fhir@lists.hl7.org"/> 
    </telecom> 
  </contact> 
  <description value="A statistic about a statistic, e.g.  Confidence interval or p-value"/> 
  <caseSensitive value="true"/> 
  <valueSet value="http://hl7.org/fhir/ValueSet/attribute-estimate-type"/> 
  <content value="complete"/> 
  <concept> 
    <code value="0000419"/> 
    <display value="Cochran's Q statistic"/> 
    <definition value="A measure of heterogeneity across study computed by summing the squared deviations of
     each study's estimate from the overall meta-analytic estimate, weighting each study's
     contribution in the same manner as in the meta-analysis."/> 
  </concept> 
  <concept> 
    <code value="C53324"/> 
    <display value="Confidence interval"/> 
    <definition value="A range of values considered compatible with the observed data at the specified confidence
     level"/> 
  </concept> 
  <concept> 
    <code value="0000455"/> 
    <display value="Credible interval"/> 
    <definition value="An interval of a posterior distribution which is such that the density at any point inside
     the interval is greater than the density at any point outside and that the area under
     the curve for that interval is equal to a prespecified probability level. For any probability
     level there is generally only one such interval, which is also often known as the highest
     posterior density region. Unlike the usual confidence interval associated with frequentist
     inference, here the intervals specify the range within which parameters lie with a certain
     probability. The bayesian counterparts of the confidence interval used in frequentists
     statistics."/> 
  </concept> 
  <concept> 
    <code value="0000420"/> 
    <display value="I-squared"/> 
    <definition value="The percentage of total variation across studies that is due to heterogeneity rather than
     chance. I2 can be readily calculated from basic results obtained from a typical meta-analysis
     as i2 = 100%×(q - df)/q, where q is cochran's heterogeneity statistic and df the degrees
     of freedom. Negative values of i2 are put equal to zero so that i2 lies between 0% and
     100%. A value of 0% indicates no observed heterogeneity, and larger values show increasing
     heterogeneity. Unlike cochran's q, it does not inherently depend upon the number of studies
     considered. A confidence interval for i² is constructed using either i) the iterative
     non-central chi-squared distribution method of hedges and piggott (2001); or ii) the test-based
     method of higgins and thompson (2002). The non-central chi-square method is currently
     the method of choice (higgins, personal communication, 2006) – it is computed if the 'exact'
     option is selected."/> 
  </concept> 
  <concept> 
    <code value="C53245"/> 
    <display value="Interquartile range"/> 
    <definition value="The difference between the 3d and 1st quartiles is called the interquartile range and
     it is used as a measure of variability (dispersion)."/> 
  </concept> 
  <concept> 
    <code value="C44185"/> 
    <display value="P-value"/> 
    <definition value="The probability of obtaining the results obtained, or more extreme results, if the hypothesis
     being tested and all other model assumptions are true"/> 
  </concept> 
  <concept> 
    <code value="C38013"/> 
    <display value="Range"/> 
    <definition value="The difference between the lowest and highest numerical values; the limits or scale of
     variation."/> 
  </concept> 
  <concept> 
    <code value="C53322"/> 
    <display value="Standard deviation"/> 
    <definition value="A measure of the range of values in a set of numbers. Standard deviation is a statistic
     used as a measure of the dispersion or variation in a distribution, equal to the square
     root of the arithmetic mean of the squares of the deviations from the arithmetic mean."/> 
  </concept> 
  <concept> 
    <code value="0000037"/> 
    <display value="Standard error of the mean"/> 
    <definition value="The standard deviation of the sample-mean's estimate of a population mean. It is calculated
     by dividing the sample standard deviation (i.e., the sample-based estimate of the standard
     deviation of the population) by the square root of n , the size (number of observations)
     of the sample."/> 
  </concept> 
  <concept> 
    <code value="0000421"/> 
    <display value="Tau squared"/> 
    <definition value="An estimate of the between-study variance in a random-effects meta-analysis. The square
     root of this number (i.e. Tau) is the estimated standard deviation of underlying effects
     across studies."/> 
  </concept> 
  <concept> 
    <code value="C48918"/> 
    <display value="Variance"/> 
    <definition value="A measure of the variability in a sample or population. It is calculated as the mean squared
     deviation (MSD) of the individual values from their common mean. In calculating the MSD,
     the divisor n is commonly used for a population variance and the divisor n-1 for a sample
     variance."/> 
  </concept> 
</CodeSystem>