Definition for Code SystemStatisticType

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<CodeSystem xmlns="http://hl7.org/fhir">
  <id value="statistic-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/statistic-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">absolute-MedianDiff
            <a name="statistic-type-absolute-MedianDiff"> </a> 
          </td> 
          <td> Absolute Median Difference</td> 
          <td> Computed by forming the difference between two medians.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">C25463
            <a name="statistic-type-C25463"> </a> 
          </td> 
          <td> Count</td> 
          <td> The number or amount of something</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">0000301
            <a name="statistic-type-0000301"> </a> 
          </td> 
          <td> Covariance</td> 
          <td> The strength of correlation between a set (2 or more) of random variables. The
             covariance is obtained by forming: cov(x,y)=e([x-e(x)][y-e(y)] where e(x), e(y)
             is the expected value (mean) of variable x and y respectively. Covariance is symmetric
             so cov(x,y)=cov(y,x). The covariance is usefull when looking at the variance of
             the sum of the 2 random variables since: var(x+y) = var(x) +var(y) +2cov(x,y) the
             covariance cov(x,y) is used to obtain the coefficient of correlation cor(x,y) by
             normalizing (dividing) cov(x,y) but the product of the standard deviations of x
             and y.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">predictedRisk
            <a name="statistic-type-predictedRisk"> </a> 
          </td> 
          <td> Predicted Risk</td> 
          <td> A special use case where the proportion is derived from a formula rather than derived
             from summary evidence.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">descriptive
            <a name="statistic-type-descriptive"> </a> 
          </td> 
          <td> Descriptive</td> 
          <td> Descriptive measure reported as narrative.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">C93150
            <a name="statistic-type-C93150"> </a> 
          </td> 
          <td> Hazard Ratio</td> 
          <td> A measure of how often a particular event happens in one group compared to how
             often it happens in another group, over time. In cancer research, hazard ratios
             are often used in clinical trials to measure survival at any point in time in a
             group of patients who have been given a specific treatment compared to a control
             group given another treatment or a placebo. A hazard ratio of one means that there
             is no difference in survival between the two groups. A hazard ratio of greater
             than one or less than one means that survival was better in one of the groups.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">C16726
            <a name="statistic-type-C16726"> </a> 
          </td> 
          <td> Incidence</td> 
          <td> The relative frequency of occurrence of something.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">rate-ratio
            <a name="statistic-type-rate-ratio"> </a> 
          </td> 
          <td> Incidence Rate Ratio</td> 
          <td> A type of relative effect estimate that compares rates over time (eg events per
             person-years).</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">C25564
            <a name="statistic-type-C25564"> </a> 
          </td> 
          <td> Maximum</td> 
          <td> The largest possible quantity or degree.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">C53319
            <a name="statistic-type-C53319"> </a> 
          </td> 
          <td> Mean</td> 
          <td> The sum of a set of values divided by the number of values in the set.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">0000457
            <a name="statistic-type-0000457"> </a> 
          </td> 
          <td> Mean Difference</td> 
          <td> The mean difference, or difference in means, measures the absolute difference between
             the mean value in two different groups.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">C28007
            <a name="statistic-type-C28007"> </a> 
          </td> 
          <td> Median</td> 
          <td> The value which has an equal number of values greater and less than it.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">C25570
            <a name="statistic-type-C25570"> </a> 
          </td> 
          <td> Minimum</td> 
          <td> The smallest possible quantity.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">C16932
            <a name="statistic-type-C16932"> </a> 
          </td> 
          <td> Odds Ratio</td> 
          <td> The ratio of the odds of an event occurring in one group to the odds of it occurring
             in another group, or to a sample-based estimate of that ratio.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">C65172
            <a name="statistic-type-C65172"> </a> 
          </td> 
          <td> Pearson Correlation Coefficient</td> 
          <td> A measure of the correlation of two variables X and Y measured on the same object
             or organism, that is, a measure of the tendency of the variables to increase or
             decrease together. It is defined as the sum of the products of the standard scores
             of the two measures divided by the degrees of freedom.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">C17010
            <a name="statistic-type-C17010"> </a> 
          </td> 
          <td> Prevalence</td> 
          <td> The ratio (for a given time period) of the number of occurrences of a disease or
             event to the number of units at risk in the population.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">C44256
            <a name="statistic-type-C44256"> </a> 
          </td> 
          <td> Proportion</td> 
          <td> Quotient of quantities of the same kind for different components within the same
             system. [Use for univariate outcomes within an individual.]</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">0000565
            <a name="statistic-type-0000565"> </a> 
          </td> 
          <td> Regression Coefficient</td> 
          <td> Generated by a type of data transformation called a regression, which aims to model
             a response variable by expression the predictor variables as part of a function
             where variable terms are modified by a number. A regression coefficient is one
             such number.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">C93152
            <a name="statistic-type-C93152"> </a> 
          </td> 
          <td> Relative Risk</td> 
          <td>  A measure of the risk of a certain event happening in one group compared to the
             risk of the same event happening in another group. In cancer research, risk ratios
             are used in prospective (forward looking) studies, such as cohort studies and clinical
             trials. A risk ratio of one means there is no difference between two groups in
             terms of their risk of cancer, based on whether or not they were exposed to a certain
             substance or factor, or how they responded to two treatments being compared. A
             risk ratio of greater than one or of less than one usually means that being exposed
             to a certain substance or factor either increases (risk ratio greater than one)
             or decreases (risk ratio less than one) the risk of cancer, or that the treatments
             being compared do not have the same effects.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">0000424
            <a name="statistic-type-0000424"> </a> 
          </td> 
          <td> Risk Difference</td> 
          <td> Difference between the observed risks (proportions of individuals with the outcome
             of interest) in the two groups. The risk difference is straightforward to interpret:
             it describes the actual difference in the observed risk of events between experimental
             and control interventions.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">C65171
            <a name="statistic-type-C65171"> </a> 
          </td> 
          <td> Spearman Rank-Order Correlation </td> 
          <td> A distribution-free analog of correlation analysis. Like regression, it can be
             applied to compare two independent random variables, each at several levels (which
             may be discrete or continuous). Unlike regression, Spearman's rank correlation
             works on ranked (relative) data, rather than directly on the data itself.</td> 
        </tr> 
        <tr> 
          <td style="white-space:nowrap">0000100
            <a name="statistic-type-0000100"> </a> 
          </td> 
          <td> Standardized Mean Difference</td> 
          <td> Computed by forming the difference between two means, divided by an estimate of
             the within-group standard deviation. It is used to provide an estimatation of the
             effect size between two treatments when the predictor (independent variable) is
             categorical and the response(dependent) variable is continuous</td> 
        </tr> 
      </table> 
    </div> 
  </text> 
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    <valueCode value="cds"/> 
  </extension> 
  <extension url="http://hl7.org/fhir/StructureDefinition/structuredefinition-standards-status">
    <valueCode value="trial-use"/> 
  </extension> 
  <extension url="http://hl7.org/fhir/StructureDefinition/structuredefinition-fmm">
    <valueInteger value="1"/> 
  </extension> 
  <url value="http://terminology.hl7.org/CodeSystem/statistic-type"/> 
  <identifier> 
    <system value="urn:ietf:rfc:3986"/> 
    <value value="urn:oid:2.16.840.1.113883.4.642.4.1937"/> 
  </identifier> 
  <version value="5.0.0-ballot"/> 
  <name value="StatisticType"/> 
  <title value="StatisticType"/> 
  <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="The role that the assertion variable plays."/> 
  <caseSensitive value="true"/> 
  <valueSet value="http://hl7.org/fhir/ValueSet/statistic-type"/> 
  <content value="complete"/> 
  <concept> 
    <code value="absolute-MedianDiff"/> 
    <display value="Absolute Median Difference"/> 
    <definition value="Computed by forming the difference between two medians."/> 
  </concept> 
  <concept> 
    <code value="C25463"/> 
    <display value="Count"/> 
    <definition value="The number or amount of something"/> 
  </concept> 
  <concept> 
    <code value="0000301"/> 
    <display value="Covariance"/> 
    <definition value="The strength of correlation between a set (2 or more) of random variables. The
     covariance is obtained by forming: cov(x,y)=e([x-e(x)][y-e(y)] where e(x), e(y)
     is the expected value (mean) of variable x and y respectively. Covariance is symmetric
     so cov(x,y)=cov(y,x). The covariance is usefull when looking at the variance of
     the sum of the 2 random variables since: var(x+y) = var(x) +var(y) +2cov(x,y) the
     covariance cov(x,y) is used to obtain the coefficient of correlation cor(x,y) by
     normalizing (dividing) cov(x,y) but the product of the standard deviations of x
     and y."/> 
  </concept> 
  <concept> 
    <code value="predictedRisk"/> 
    <display value="Predicted Risk"/> 
    <definition value="A special use case where the proportion is derived from a formula rather than derived
     from summary evidence."/> 
  </concept> 
  <concept> 
    <code value="descriptive"/> 
    <display value="Descriptive"/> 
    <definition value="Descriptive measure reported as narrative."/> 
  </concept> 
  <concept> 
    <code value="C93150"/> 
    <display value="Hazard Ratio"/> 
    <definition value="A measure of how often a particular event happens in one group compared to how
     often it happens in another group, over time. In cancer research, hazard ratios
     are often used in clinical trials to measure survival at any point in time in a
     group of patients who have been given a specific treatment compared to a control
     group given another treatment or a placebo. A hazard ratio of one means that there
     is no difference in survival between the two groups. A hazard ratio of greater
     than one or less than one means that survival was better in one of the groups."/> 
  </concept> 
  <concept> 
    <code value="C16726"/> 
    <display value="Incidence"/> 
    <definition value="The relative frequency of occurrence of something."/> 
  </concept> 
  <concept> 
    <code value="rate-ratio"/> 
    <display value="Incidence Rate Ratio"/> 
    <definition value="A type of relative effect estimate that compares rates over time (eg events per
     person-years)."/> 
  </concept> 
  <concept> 
    <code value="C25564"/> 
    <display value="Maximum"/> 
    <definition value="The largest possible quantity or degree."/> 
  </concept> 
  <concept> 
    <code value="C53319"/> 
    <display value="Mean"/> 
    <definition value="The sum of a set of values divided by the number of values in the set."/> 
  </concept> 
  <concept> 
    <code value="0000457"/> 
    <display value="Mean Difference"/> 
    <definition value="The mean difference, or difference in means, measures the absolute difference between
     the mean value in two different groups."/> 
  </concept> 
  <concept> 
    <code value="C28007"/> 
    <display value="Median"/> 
    <definition value="The value which has an equal number of values greater and less than it."/> 
  </concept> 
  <concept> 
    <code value="C25570"/> 
    <display value="Minimum"/> 
    <definition value="The smallest possible quantity."/> 
  </concept> 
  <concept> 
    <code value="C16932"/> 
    <display value="Odds Ratio"/> 
    <definition value="The ratio of the odds of an event occurring in one group to the odds of it occurring
     in another group, or to a sample-based estimate of that ratio."/> 
  </concept> 
  <concept> 
    <code value="C65172"/> 
    <display value="Pearson Correlation Coefficient"/> 
    <definition value="A measure of the correlation of two variables X and Y measured on the same object
     or organism, that is, a measure of the tendency of the variables to increase or
     decrease together. It is defined as the sum of the products of the standard scores
     of the two measures divided by the degrees of freedom."/> 
  </concept> 
  <concept> 
    <code value="C17010"/> 
    <display value="Prevalence"/> 
    <definition value="The ratio (for a given time period) of the number of occurrences of a disease or
     event to the number of units at risk in the population."/> 
  </concept> 
  <concept> 
    <code value="C44256"/> 
    <display value="Proportion"/> 
    <definition value="Quotient of quantities of the same kind for different components within the same
     system. [Use for univariate outcomes within an individual.]"/> 
  </concept> 
  <concept> 
    <code value="0000565"/> 
    <display value="Regression Coefficient"/> 
    <definition value="Generated by a type of data transformation called a regression, which aims to model
     a response variable by expression the predictor variables as part of a function
     where variable terms are modified by a number. A regression coefficient is one
     such number."/> 
  </concept> 
  <concept> 
    <code value="C93152"/> 
    <display value="Relative Risk"/> 
    <definition value=" A measure of the risk of a certain event happening in one group compared to the
     risk of the same event happening in another group. In cancer research, risk ratios
     are used in prospective (forward looking) studies, such as cohort studies and clinical
     trials. A risk ratio of one means there is no difference between two groups in
     terms of their risk of cancer, based on whether or not they were exposed to a certain
     substance or factor, or how they responded to two treatments being compared. A
     risk ratio of greater than one or of less than one usually means that being exposed
     to a certain substance or factor either increases (risk ratio greater than one)
     or decreases (risk ratio less than one) the risk of cancer, or that the treatments
     being compared do not have the same effects."/> 
  </concept> 
  <concept> 
    <code value="0000424"/> 
    <display value="Risk Difference"/> 
    <definition value="Difference between the observed risks (proportions of individuals with the outcome
     of interest) in the two groups. The risk difference is straightforward to interpret:
     it describes the actual difference in the observed risk of events between experimental
     and control interventions."/> 
  </concept> 
  <concept> 
    <code value="C65171"/> 
    <display value="Spearman Rank-Order Correlation "/> 
    <definition value="A distribution-free analog of correlation analysis. Like regression, it can be
     applied to compare two independent random variables, each at several levels (which
     may be discrete or continuous). Unlike regression, Spearman's rank correlation
     works on ranked (relative) data, rather than directly on the data itself."/> 
  </concept> 
  <concept> 
    <code value="0000100"/> 
    <display value="Standardized Mean Difference"/> 
    <definition value="Computed by forming the difference between two means, divided by an estimate of
     the within-group standard deviation. It is used to provide an estimatation of the
     effect size between two treatments when the predictor (independent variable) is
     categorical and the response(dependent) variable is continuous"/> 
  </concept> 
</CodeSystem>