Definition for Code SystemAttributeEstimateType

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

<CodeSystem xmlns="http://hl7.org/fhir">
  <id value="attribute-estimate-type"/> 
  <meta> 
    <lastUpdated value="2022-09-10T04:52:37.223+10:00"/> 
    <profile value="http://hl7.org/fhir/StructureDefinition/shareablecodesystem"/> 
  </meta> 
  <text> 
    <status value="generated"/> 
    <div xmlns="http://www.w3.org/1999/xhtml">
      <p> This code system 
        <code> http://terminology.hl7.org/CodeSystem/attribute-estimate-type</code>  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> 
  </text> 
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    <valueInteger value="1"/> 
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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:2.16.840.1.113883.4.642.4.1942"/> 
  </identifier> 
  <version value="5.0.0-ballot"/> 
  <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>