Quiz - eSubmission Data for FIH Multiple Expansion Cohort Trials

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* 1. Select trial types that are often used in Multiple Expansion Cohort Trials? Select all that apply.

Post Marketing

Single Ascending Dose (SAD)

Drug-Drug Interaction (DDI)

Reactogenicity

Multiple Ascending Dose (MAD)

Open Label Extension (OLE)

Food Effects (FE)

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* 2. Which variable in the Demographics (DM) domain could be used to determine the planned intervention group? Select one.

EPOCH

ACTARM

ARM

COHORT

ARMNRS

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* 3. True or False? It is compliant to have a different set of SDTM for each cohort.

TRUE

FALSE

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* 4. What variable would you use to distinguish different trial parameter values for the different parts of a Multiple Expansion Cohort Trial?

EPOCH

TSVAL

TSPARM

TSGRPID

DOMAIN

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* 5. True or False? One protocol means one eSubmission data package.

True

False

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* 6. FIH Multiple Expansion Cohort Trials are complex. What are acceptable methods to provide clarity to data and analysis mapping. Select all that apply.

Provide multiple bookmarking sections to the acrf.pdf for each cohort to display how the data was collected in real-time.

Rename each SDTM dataset to append the cohort number. For example, DM1, DM2, and DM3.

Provide a diagram in the reviewer guide Protocol Design section that describes the expansion of the cohorts, the dose, and purpose of the trial part.

Utilize meaningful TA.ARM values and TE.Element values that makes the intervention dose and trial part clear.

Add variables PART and PARTN to all ADaM datasets.

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* 7. In your opinion, rate the importance of each topic below. There is no right answer.

	Not Important at All	Least Important	Somewhat Important	Very Important	Most Important
Data traceability	Data traceability Not Important at All	Data traceability Least Important	Data traceability Somewhat Important	Data traceability Very Important	Data traceability Most Important
Data validation issues	Data validation issues Not Important at All	Data validation issues Least Important	Data validation issues Somewhat Important	Data validation issues Very Important	Data validation issues Most Important
Technical rejection criteria	Technical rejection criteria Not Important at All	Technical rejection criteria Least Important	Technical rejection criteria Somewhat Important	Technical rejection criteria Very Important	Technical rejection criteria Most Important
Favorable analysis results	Favorable analysis results Not Important at All	Favorable analysis results Least Important	Favorable analysis results Somewhat Important	Favorable analysis results Very Important	Favorable analysis results Most Important
Patient safety	Patient safety Not Important at All	Patient safety Least Important	Patient safety Somewhat Important	Patient safety Very Important	Patient safety Most Important
Transparency in analysis methodology	Transparency in analysis methodology Not Important at All	Transparency in analysis methodology Least Important	Transparency in analysis methodology Somewhat Important	Transparency in analysis methodology Very Important	Transparency in analysis methodology Most Important
Consistently reporting data standards used	Consistently reporting data standards used Not Important at All	Consistently reporting data standards used Least Important	Consistently reporting data standards used Somewhat Important	Consistently reporting data standards used Very Important	Consistently reporting data standards used Most Important
Reading the most recent version of the Study Data Technical Conformance Guide	Reading the most recent version of the Study Data Technical Conformance Guide Not Important at All	Reading the most recent version of the Study Data Technical Conformance Guide Least Important	Reading the most recent version of the Study Data Technical Conformance Guide Somewhat Important	Reading the most recent version of the Study Data Technical Conformance Guide Very Important	Reading the most recent version of the Study Data Technical Conformance Guide Most Important