Skip to main content

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