data management

Question 1

what are CRF’s

Answer 1

case report forms

• collect data consistently across sites
• collect data as speciified in the protocol
• help sites in complying with protocol procesdures
  collect data in analysable format

Question 2

what needs to happen before the CRF is signed off

Answer 2

cross functional review and approval process

includes
- drafting it
reviewing it by the relevant teams
- approval

whole process repeated when amendments are made

Question 3

what do we use to form the data that is to be collected

Answer 3

the protocol

data nees to be collected as is specified in the protocol, for all specified time points

thats IT! dont collect additional data incase it might be interesting

Question 4

why do we need the CRF to be easy to follow for patients and sites filling it out

Answer 4

this aids protocol compliance and reduces number of queries

Question 5

lets say we know we want to collect questions on the asberg depression scale, how might the collection of data differ

Answer 5

how they will actually fill this out: checkbox, coded dropdown list

Question 6

what is a major source of queries in CRF

Answer 6

ambiguous data - make it very clear what is being requested

Question 7

how can we avoid collecting ambiguous data

Answer 7

include units of measurment and specify the accuracy required (e.g., 6 spaces for DOB)

avoid blank responses - include option to say the measurment was not done or nt applicable e.g., N/A

use headers to say whether certain questions are applicable e.g., do you have any of the following: diabetes - IF YES –>when were you diagnosed etc. if no ignore

clear questions e.g., death instead of death of baby (n might think death of who?)

clear instructions along side of questions to help reduce number of protocol deviations

Question 8

is one identifier enough for a patient e.g., initials

Answer 8

no, second identifier needed

• patient initials
• month/year of birth

Question 9

what are CDISC standards and why do we need them?

Answer 9

Clinical Data Interchange Standards Consortium

standards that ensure consistent and reliable data collection and analysis in clinical trials

facilitates collaboration andd sharing of data

Question 10

what are some key CDISM standards?

Answer 10

• Standards for data collection – cDASH
• study data tabulation model – SDTM
• data analysis – ADaM

Question 11

what standards are required to be met for a submission of a trial to FDA

Answer 11

CDISC STANDARDS

Question 12

why is it good to use CDISC standards alongside a template CRF

Answer 12

speeds up process of building a database

Question 13

how does using CDISC standards alongside a template CRF speed up the process of building a database?

Answer 13

because in the database design we need to define the variables and the data to be collacted in the study

CDISC standards provide a pre-defined and standardised set of vairables and data

e.g., the SDTM provides set of standardised variable names, formats and valuees that can be used to organise collected data

means we dont need to spend time defining nad creating new vairiable names /data formats since they are already provided by the standard

Question 14

what are the different data capture methods

Answer 14

• traditional paper CRF’s
• electronic CRF’s
• patient completed questionairs (paper or electronic)
• laboratory data
• other external data e.g., blood pressure monitor

Question 15

what ways are there to colect data that are not the CRF

Answer 15

lab data

data collected from devices or apps

Question 16

what do we need to make sure about non-CRF collected data

Answer 16

structure - needs to have consistency

format - way that allows for analysis

data manager needs to make sure that transport of the data is not changed accidentaly. needs to have integrity

Question 17

what are clinical datamanagment systems

Answer 17

software applcications that help manage data collection, cleaning and analysis of clinical trial data

Question 18

when do we do data cleaning

Answer 18

from when data is first captured all the way to submitting the data for final analysis

Question 19

what is data cleaning

Identify missing, inconsistent and out of range

Answer 19

checking the data collected in the clinical trial to ensure that there are no missing data points or values that are inconsistent or outside the expected range. This is important for ensuring the accuracy and completeness of the data.

• Data coding queries - review data that is transfered to the standard format for analysis and make sure the data has been coded correctly.

Question 20

data cleaning

Reconciliation with external data sources:

Answer 20

This involves comparing the data collected in the clinical trial to external sources of data, such as medical records or laboratory results, to ensure that the data is accurate and complete.

Question 21

data cleaning

review and identify protocol deviations

Answer 21

eviewing the data to identify any deviations from the study protocol, such as missed visits or medication doses, which can affect the accuracy and reliability of the data.

Question 22

where would we find the data cleaning plan

Answer 22

data managment plan

Question 23

what kind of things constitute data discrepencies

Answer 23

• Incorrect source data used
• Transcription errors from source to CRF
• Data entry errors
• Incomplete data
• Study identification number errors

Question 24

how can we identify data discrepencies

Answer 24

• Programmed database checks
• Batch checks on entered data
• Dataset review
• Reconciliation with external datasets
• Safety data review (e.g., checking AEs) and medical coding (translating medical terms into standardised codes)
• Source data verification

Question 25

what is database lock?

Answer 25

marks the end of data collection and start of analysis

Question 26

When cleaning data what do you need to look for?

Answer 26

• Incorrect data - look up the min and max values from the questionnaires - any out of this range?
• protocol deviations
• inconsistencies
• missing data