Below is a collection of SAS® papers to help prepare for the SAS® Clinical Trials Programmer Certification Exam. See also SAS Certification Exam. Mind map. See sample test CDISC interview questions.
|
Time
(24 hours)
|
Certification Exam Topic |
SAS® Savvy Reference |
|---|---|---|
| 0.5 hour | I. Clinical Trials Process |
New Clinical SAS® Programmer Clinical Data Management Regularory and Project Management |
| 6 hours | II. Clinical Trials Data Structures |
CDISC Strategies |
| 1 hours |
III. Import and Export Clinical Trials Data |
Proc Import, Proc Export |
| 2 hours |
IV. Manage Clinical Trials Data |
|
| 0.5 hour | V. Transform Clinical Trials Data |
SAS Functions/Formats |
| 2 hours |
VI. Apply Statistical Procedures for Clinical Trials |
Proc Means/Summary Continuous - ANCOVA and Survival analysis Categorical - Chi Square, McNemar's Test and Rank Test |
| 2 hours | VII. Macro Programming for Clinical Trials |
SAS Macro Programming |
| 4 hours |
VIII. Report Clinical Trials Results |
|
| 4 hours |
IX. Validate Clinical Trial Data Reporting |
|
| 4 hours | X. Other Topics |
I. Clinical Trials Process | |
| Describe the clinical research process (phases, key roles, key organizations) | |
| Interpret a Statistical Analysis Plan | Training Statistical Programmers on SAP Review Skills, Sascha Ahrweiler |
| Derive programming requirements from an SAP and an annotated Case Report Form | |
| Describe regulatory requirements (principles of 21 CFR Part 11, International Conference on Harmonization, Good Clinical Practices) | |
II. Clinical Trials Data Structures | |
| Identify the classes of clinical trials data (demographic, lab, baseline, concomitant medication, etc.) | |
| Identify key CDISC principals and terms | |
| Describe the structure and purpose of the CDISC SDTM data model | Implementation of the CDISC SDTM at the Duke Clinical Research Institute, Jack Shostak |
| Describe the structure and purpose of the CDISC ADaM data model | From SAP to ADaM: The Nuts and Bolts, Nancy Brucken, Paul Slagle |
| Describe the contents and purpose of define.xml | Creating a define.xml file for ADaM and SDTM, John H. Adams |
III. Import and Export Clinical Trials Data | |
| Combine SAS data sets | |
| Efficiently import and subset SAS data sets | |
| Access data in an Excel workbook (LIBNAME and PROC IMPORT/EXPORT) | |
| Create temporary and permanent SAS data sets | |
| Apply regulatory requirements to exported SAS data sets (SAS V5 requirements) | |
IV. Manage Clinical Trials Data | |
| Investigate SAS data libraries using base SAS utility procedures (PRINT, CONTENTS, FREQ) | |
| Access DICTIONARY Tables using the SQL procedure | |
| Sort observations in a SAS data set | |
| Create and modify variable attributes using options and statements in the DATA step | |
| Examine and explore clinical trials input data (find outliers, missing vs. zero values, etc) | |
V. Transform Clinical Trials Data | |
| Process data using DO LOOPS | How to Use ARRAYs and DO Loops: Do I DO OVER or Do I DO i?, Jennifer L Waller |
| Process data using SAS arrays | Arrays by example, Diana Suhr |
| Retain variables across observations | |
| Use assignment statements in the DATA step | |
| Apply categorization and windowing techniques to clinical trials data | |
| Use SAS functions to convert character data to numeric and vice versa | |
| Use SAS functions to manipulate character data, numeric data, and SAS date values | |
| Transpose SAS data sets | |
| Apply 'observation carry forward' techniques to clinical trials data (LOCF, BOCF, WOCF) | Statisical Analysis (impute) |
| Calculate 'change from baseline' results | |
| Obtain counts of events in clinical trials | |
VI. Apply Statistical Procedures for Clinical Trials | |
| Use SAS procedures to obtain descriptive statistics for clinical trials data (FREQ, UNIVARIATE, MEANS, SUMMARY) | |
| Use PROC FREQ to obtain p-values for categorical data (2x2 and NxP test for association) | |
| Use PROC TTEST to obtain p-values for continuous data (one-sample, paired and two-sample t-tests) | |
| Create output data sets from statistical procedures | |
VII. Macro Programming for Clinical Trials | |
| Create and use user-defined and automatic macro variables | |
| Automate programs by defining and calling macros | |
| Use system options to debug macros and display values of macro variables in the SAS log (MPRINT, SYMBOLGEN, MLOGIC, MACROGEN) | The Macro Debugging Primer, Frank DiIorio |
VIII. Report Clinical Trials Results | |
| Use PROC REPORT to produce tables and listings for clinical trials reports | Seven Steps to Regulatory Publication Style With Proc Report. Dennis Gianneschi |
| Use ODS and global statements to produce and augment clinical trials reports | |
IX. Validate Clinical Trial Data Reporting | |
| Explain the principles of programming validation in the clinical trial industry | The 5 Most Important Clinical SAS Programming Validation Steps, Brian C. Shilling |
| Utilize the log file to validate clinical trial data reporting | Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs, Susan J. Slaughter, Lora D. Delwiche |
| Use programming techniques to validate clinical trial data reporting (PROC COMPARE, MSGLEVEL) | “How Does Your Data Compare?”, SAS’s COMPARE PROCEDURE, Jenna Heyen |
| Identify and Resolve data, syntax and logic errors |