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Table 4 Correspondence of TMF data quality indicators with the current data quality framework

From: Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R

TMFID TMF name Related in current framework to concept Description of element type/ implementation in current framework
TMF-1001 Agreement with previous values Disagreement of repeated measurements Domain
TMF-1003 Consistency Contradictions Domain
TMF-1004 Certain contradiction/error Certain contradictions Indicator
TMF-1005 Possible contradiction/warning Uncertain contradictions Indicator
TMF-1006
TMF-1009
TMF-1010
TMF-1011
TMF-1052
Distribution of values
Distribution of parameters recorded by the investigator
Distribution of parameters recorded by the device
Distribution of findings recorded by a medical reader
Distribution of parameters between study sites
Unexpected location parameter
Unexpected shape parameter
Unexpected scale parameter
Unexpected proportion
Indicator but TMF differentiates by the influencing factor while the current framework distinguishes by the statistical aspect.
TMF-1012 Missing modules Unexpected data elements An implementation that identifies missing modules within the indicator unexpected data elements
TMF-1013 Missing values in data elements Missing values Indicator
TMF-1014 Missing values in mandatory data elements Missing values An implementation that identifies mandatory data elements within the indicator missing values
TMF-1016 Data elements with value unknown etc. Missing due to specified reason Indicator (TMF targets a specific reason for missing value: unknown values)
TMF-1018 Outliers (continuous data elements) Univariate outliers Indicator
TMF-1019 Values that exceed the measurability limits Inadmissible numerical values Implementation within inadmissible numerical values
TMF-1021 Illegal values of qualitative data elements Inadmissible categorical values Indicator
TMF-1022 Illegal values of qualitative data elements used for the coding of missings Inadmissible categorical values An implementation that identifies inadmissible coding of missing modules within the indicator inadmissible categorical values
TMF-1023 Illegal values used for the coding of missing modules Inadmissible categorical values An implementation that identifies inadmissible coding of missing values within the indicator inadmissible categorical values
TMF-1024 Illegal values of qualitative data elements used for the coding of results exceeding measurability limits Inadmissible categorical values An implementation that identifies data elements with codes related to measurability limits within the indicator inadmissible categorical values
TMF-1029 Duplicates Duplicates Indicator
TMF-1030 Recruitment rate Nonresponse rate Indicator, the current framework uses the inverse. The link between both depends on the definition of recruitment and nonresponse rates
TMF-1031
TMF-1032
Refusal rate of investigations
Refusal rate of modules
Refusal rate Indicator with implementations at the level of examination modules or the entire study
TMF-1034 Drop-out-rate Drop-out rate Indicator
TMF-1042 Observational units with follow-up Non-response rate (inverse at unit level, depending on implementation form) Indicator
TMF-1043 Accuracy Accuracy Dimension
TMF-1046 Completeness Completeness Dimension
  1. 1) Included are TMF-indicators that have been classified as being at least important based on an empirical evaluation [29]. Two indicators with an important rating have not been included, “Compliance with procedural rule” (TMF-1047) and “Representativeness” (TMF-1048), as described in discussion