CRF listings.
Discrepancy management: CRF listings.
Introduction:
Clinical data management: The ultimate goal of clinical data management is to complete every study with a dataset that accurately represents data captured in the study. Here the discrepancy management enters in this path and it is known as the process of identifying and managing potential problems with data collected during a study.
DISCREPANCY:
Discrepancies are inconsistence found in clinical trial data which need to be corrected as per study protocol.A single discrepant data or open query can lead to database unlock can change the fate of the product.
CRFs need to be carefully prepared to collect data completely and accurately. There are a number of factors which have an underlying impact on the overall quality of the data collected.
The CRF should allow collection of the data as requested in the protocol and the format should follow the protocol’s treatment procedure.
If the CRF does not allow data capture as requested by the protocol then errors are built into the study instead of quality, which would inevitably result in a high number of queries being generated as the CRF is processed.
Discrepancy management also known as query management is the process of cleaning subject data in CDM system that includes manual queries and programmed procedures (edit checks).
Data Manager Generate and Issue/Re-Issue queries if any during data entry by the site personnel and a data point fails the automatic edit checks (as specified in the DVS) a query will be generated in the clinical database. These are called auto queries.
In some circumstances CDM need to raise the manual queries. Eg: In case report forms if any invalid data is entered (in form of duplicates or inconsistence data) auto queries are not useful. Manually need to raise the queries.
In discrepancy management in the form of listing or reports they review the data.
Listings:
Listings or reports can be defined as reviewing the data and output the invalid data by giving the logic, reviewer instructions and query text to in the process of raising manual queries. By the listings only they can output the data.
Eg: crf listings.
Crf listings:
To clean the crf data steps to be followed.
1. output the invalid data through listings.
A. give the logic
B. give the reviewer instruction.
C. give the query text
By following these steps DM can output the data in an excel sheet.
2. Raising the manual query.
By giving the logic it can only output the data. Again they need to raise the query MANUALLY.
Output data is two types.
1st type of output data:
When there is invalid data as duplicate or overlapping in crf forms it is directly pushed as dirty data in excel sheet.
Eg: AE vs AE (adverse event crf vs adverse event crf)
MH vs MH (medical history crf vs medical history crf)
CM vs CM (concomitant medication crf vs concomitant medication form)
2nd type of output:
In this type data will be output as dump. Gain they need to review the data manually. here it is a very challenging task. We need to review one crf against 100 no. of crf forms.
Eg: MH vs CM
AE vs CM
VS vs AE
ECG vs AE
LAB vs AE
DM vs PREG
DM vs PREG vs AE vs DISP
BY seeing AE vs AE listing we can understand the 1st type of output data, overlapping or duplicate data and how it will be reported.
AE vs AE:
For one subject Id Ae terms are matching in two Ae forms but Ae start date and AE end date are overlapping then they list the dirty data as output. Again they need to raise query manually.
We can understand these listings by seeing examples of items in AE forms for same subject id(eg:10100):
1st AE FORM for sub ID<10100>:
Sub ID: 10100
AE ID: 15
AE TERM: FEVER
AE START DATE: 5th April 2020
AE STOP DATE: 5th May 2020
2nd AE FORM for sub ID<10100>:
Sub ID: 10100
AE ID: 18
AE TERM: FEVER
AE START DATE: 15th April 2020
AE STOP DATE: 25th May 2020
In the above AE forms for same sub ID <10100> and for same AE TERM (fever) star date and stop dates are overlapping. This is invalid data and by giving listing we can output the invalid data.
HOW TO GIVE THE LOGIC AND OUTPUT THE DUPLICATE DATA in listings?
By this listing directly overlapping data will be appear as output in excel sheet.
In output data following items will be there.
Study ID; sub ID; AE ID; AE term; AE start date; AE end date.
We can understand by seeing the following example:
OUTPUT DATA:
By seeing MH vs CM listings we can understand the 2nd type of output data
MH vs CM:
Example:
For sub id <10100> medical history has been treated and started the medication.
Sub ID: 10100
MH ID: 12
MH TERM: FEVER
MH START DATE: 25TH SEPTEMBER 1996
MH END DATE: 25 TH SEPTEMBER 2019
IS MH BEEN TREATED: YES /NO
Above MH form is showing that for sub id <10100> MH has been treated.so for that concomitant medication form should be there.
In this type of listings we will get the 100 no. of CM forms in excel sheet as dump. Here DM staff need to manually review or check one MH form againstv100 no. of CM forms.
Listings will give the way how to manually check one form against 100 no. of forms.
HOW TO CHECK 1 MH form AGAINST 100 NO. OF CM FORMS?
1st step: check the start and end dates
2nd step: in matching date forms check for indication: MH should be there. If indication is not MH then need to raise the query.
By giving the listing DM can easily do that task:
LISTING:
Output:
MH ID; MH TERM: MH START DATE; MH ONGOING; IS MH TREATED; CM ID; CM TERM; CM START DATE; CM ONOING; INDICTATION.
In this way through listings of different crf forms DM can out put the invalid data and raise the manual queries according to that.
CONCLUSION:
Discrepancy management is a vital vehicle in clinical trials to ensure the integrity, quality of data and to ensure the collected CRF data is complete and accurate so that results are correct. In this process CRF listing is very challenging task for DM staff and through this recognizing the invalid data and further raising the manual queries will be done.
Pravallika Racherla
Comments