Codesystem-statistic-type.xml

Definition for Code System StatisticStatisticType

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

<CodeSystem xmlns="http://hl7.org/fhir">
  <id value="statistic-type"/> 
  <meta> 
    <lastUpdated value="2019-12-31T21:03:40.621+11:00"/> 
  </meta> 
  <text> 
    <status value="generated"/> 
    <div xmlns="http://www.w3.org/1999/xhtml">
      <h2> StatisticStatisticType</h2> 
      <div> 
        <p> The type of a specific statistic.</p> 

      </div> 
      <p> This code system http://terminology.hl7.org/CodeSystem/statistic-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">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> 
  <extension url="http://hl7.org/fhir/StructureDefinition/structuredefinition-wg">
    <valueCode value="fhir"/> 
  </extension> 
  <extension url="http://hl7.org/fhir/StructureDefinition/structuredefinition-standards-status">
    <valueCode value="draft"/> 
  </extension> 
  <extension url="http://hl7.org/fhir/StructureDefinition/structuredefinition-fmm">
    <valueInteger value="5"/> 
  </extension> 
  <url value="http://terminology.hl7.org/CodeSystem/statistic-type"/> 
  <identifier> 
    <system value="urn:ietf:rfc:3986"/> 
    <value value="urn:oid:2.16.840.1.113883.4.642.1.1411"/> 
  </identifier> 
  <version value="4.2.0"/> 
  <name value="StatisticStatisticType"/> 
  <title value="StatisticStatisticType"/> 
  <status value="draft"/> 
  <experimental value="false"/> 
  <date value="2019-12-31T21:03:40+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 type of a specific statistic."/> 
  <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>