HL7 Terminology (THO)
6.0.0 - Publication International flag

This page is part of the HL7 Terminology (v6.0.0: Release) based on FHIR (HL7® FHIR® Standard) v5.0.0. This is the current published version in its permanent home (it will always be available at this URL). For a full list of available versions, see the Directory of published versions

CodeSystem: StatisticStatisticType

Official URL: http://terminology.hl7.org/CodeSystem/statistic-type Version: 1.0.1
Active as of 2024-04-24 Maturity Level: 5 Responsible: Health Level Seven International Computable Name: StatisticStatisticType
Other Identifiers: OID:2.16.840.1.113883.4.642.1.1411

Copyright/Legal: This material derives from the HL7 Terminology (THO). THO is copyright ©1989+ Health Level Seven International and is made available under the CC0 designation. For more licensing information see: https://terminology.hl7.org/license

The type of a specific statistic.

This Code system is referenced in the content logical definition of the following value sets:

Generated Narrative: CodeSystem statistic-type

Last updated: 2024-04-24 00:00:00+0000

This case-sensitive code system http://terminology.hl7.org/CodeSystem/statistic-type defines the following codes:

CodeDisplayDefinition
absolute-MedianDiff Absolute Median Difference Computed by forming the difference between two medians.
C25463 Count The number or amount of something.
0000301 Covariance 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.
predictedRisk Predicted Risk A special use case where the proportion is derived from a formula rather than derived from summary evidence.
descriptive Descriptive Descriptive measure reported as narrative.
C93150 Hazard Ratio 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.
C16726 Incidence The relative frequency of occurrence of something.
rate-ratio Incidence Rate Ratio A type of relative effect estimate that compares rates over time (eg events per person-years).
C25564 Maximum The largest possible quantity or degree.
C53319 Mean The sum of a set of values divided by the number of values in the set.
0000457 Mean Difference The mean difference, or difference in means, measures the absolute difference between the mean value in two different groups.
C28007 Median The value which has an equal number of values greater and less than it.
C25570 Minimum The smallest possible quantity.
C16932 Odds Ratio 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.
C65172 Pearson Correlation Coefficient 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.
C17010 Prevalence 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.
C44256 Proportion Quotient of quantities of the same kind for different components within the same system. [Use for univariate outcomes within an individual.].
0000565 Regression Coefficient 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.
C93152 Relative Risk 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.
0000424 Risk Difference 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.
C65171 Spearman Rank-Order Correlation 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.
0000100 Standardized Mean Difference 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.

History

DateActionAuthorCustodianComment
2023-11-14reviseMarc DuteauTSMGAdd standard copyright and contact to internal content; up-476
2020-10-14reviseGrahame GrieveVocabulary WGReset Version after migration to UTG
2020-05-06reviseTed KleinVocabulary WGMigrated to the UTG maintenance environment and publishing tooling.