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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Validate Clinical Trial Data Reporting | 20% | - Use PROC COMPARE and log review - Programming validation techniques - Validation principles |
| Topic 2: Transform or Summarize Clinical Trials Data | 15% | - Derive variables and study days - Aggregation and transposition - Data step manipulation |
| Topic 3: Clinical Trials Process | 5% | - Clinical trial lifecycle and protocol - Derive programming requirements from protocol and CRF |
| Topic 4: Clinical Trials Data Structures | 10% | - Clinical domain structures - CDISC standards: SDTM, ADaM - Define.xml and dataset specifications |
| Topic 5: Regulatory Submissions | 5% | - ICH and FDA guidelines - Compliance for clinical data reporting |
| Topic 6: Apply Statistical Procedures for Clinical Trials | 15% | - PROC MEANS, FREQ, TABULATE - Basic statistical analysis for clinical data |
| Topic 7: Macro Programming for Clinical Trials | 15% | - Debugging macros - Macro variables and parameters - Create and call macros |
| Topic 8: Report Clinical Trials Results | 10% | - Produce tables, listings, and figures - PROC REPORT and PROC PRINT |
| Topic 9: Manage Clinical Trials Data | 5% | - Clean, merge, and subset data - Use SQL and dictionary tables - Access and explore clinical datasets |
Question 1
What is the main focus of 21 CFR Part 11?
A. trial safety requirements
B. electronic submission requirements
C. trial protocol requirements
D. statistical calculation requirements
Question 2
Given the following entry in the SAS log:
Which alternate method could you use to?
- Transpose all data
- Not produce a warning message in the log
A. Use PROC TRANSPOSE with a WHERE clause to omit records with missing VISIT values
B. Use PROC DATASETS with SELECT and MODIFY statements
C. Use PROC COPY with SELECT statements
D. Use a DATA step with conditional OUTPUT statements
Question 3
This question will ask you to provide a section of missing code.
Given the input SAS data set LABRAW:
Which DO LOOP will create the output SAS data set WORK.LAB_NEW?
A. do i=1 to 2; visit=i; date=dat{i}; result=num{i}; output; end;
B. do i=1 to 2; visit=i; date=dat{i}; result=num{i}; end; output;
C. do i=1 to 2; do j=1 to 2; visit=i; date=dat{j}; result=num{j}; end; output; end;
D. do i=1 to 2; do j=1 to 2; visit=i; date=dat{j}; result=num{j}; output; end;
Question 4
The following SAS program is submitted:
How many data sets are created and how many observations are in the data set(s)?
A. 1 data set named PVALUES with 1 observation.
B. 2 data sets named PVALUES and PVALUES1 each with 1 observation.
C. 1 data set named PVALUES with 2 observations.
D. 2 data sets named PVALUES1 and PVALUES2 each with 2 observations
Question 5
You are working on validating the ADSL data set which contains the variables subject, sex, age, birth date, baseline height, and baseline weight. These variables are brought in from SDTM, and you derive new variables baseline BMI (from baseline height and weight) and age categorization (from age).
You get the following in the SAS log:
To reconcile this difference, which variable values will be most helpful to provide to the production programmer?
A. BLBMI
B. SUBJID BLHT BLWT BLBMI
C. AGECAT1 BLBMI
D. SUBJID BLBMI
Solutions:
| Question 1 Answer: B | Question 2 Answer: D | Question 3 Answer: A | Question 4 Answer: B | Question 5 Answer: B |
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