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CDM 1 SAS in Pharmaceutical Industry 30 July 2009 Arjun Roy & Madan Gopal Kundu Clinical Data Management & Biostatistics MACR

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CDM 2 Statistical software SAS – Advantage, History, Definition, Windows Basic Programming SAS Macro, Examples Validation, Compliance Contents

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CDM 3 Statistical Software – why?? Solution is Statistical software!! Manual computation is error prone and time consuming Clinical trials produces huge volume of data

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CDM 4 Statistical Software

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CDM 5 SAS – why?? Advanced statistical analysis is much more accessible Non standard analyses can be programmed Comparatively faster when working with very large dataset. Better reporting tool Also offers data warehousing capability Popularity

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CDM 6 History Statistical Analysis System Developed by Jim Goodnight and John Shall in1970 at N.C. University Initially Developed for Agricultural Research SAS Institute founded in of worlds top 100 company in Fortune 500 use SAS CFR part 11 compliant

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CDM 7 SAS solutions for life sciences SAS for Clinical Data Integration SAS for Life Sci. Sales & Marketing SAS Drug Development SAS Patient Safety

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CDM 8 What is SAS ?? Tool for Stat. Analyses Reporting tool Programming Language Data Warehouse Database

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CDM 9 SAS as a Data Warehouse

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CDM 10 SAS as a Database Import/ Export facilities – Can read or export data from a variety of formats Performing query, merging or data manipulation is possible Data transformation, derivation of new variables

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CDM 11 SAS as a Programming Language Macro facility Matrix manipulation Possible to write routines for new methods

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CDM 12 SAS for Statistical Analyses Descriptive statistics Contingency Tables Correlation / Regression t-test Wilcoxon test General Linear Model (ANOVA, ANCOVA) Logistic regression Chi-square/ Fishers exact test Trend test Dunnett Multiple comparison Logrank test/ Kaplan Meier

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CDM 13 SAS as a Reporting Tool Almost any kind of tables for CSR can be programmed that meets the Clinicians and Regulatory requirement. Reporting procedures in SAS PROC REPORT PROC PRINT PROC TABULATE DATA STEP

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CDM Learning SAS!!

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CDM 15 Editor Log Output Result Explorer Graph SAS Windows

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CDM 16 EDITOR To write/ modify SAS program code LOG To check execution of the program. Helps in identify the error in SAS code Tells about details such as amount of time it taken to execute the code EXPLORER It displays the list of libraries (containing dataset, formats, compiled macros and graphs)

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CDM 17 OUTPUT It displays the output generated upon execution of SAS code RESULTS It displays index of the output

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CDM 18 Libraries & Datasets SAS stores Datasets in Libraries. Libraries are just a referred location in Hard-drive. (e.g., F:\MADANKU\Ragacin\) Datasets in Libraries can be generated using Data steps. Can be imported from other formats (e.g., Excel, Oracle Clinical etc.)

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CDM 19 Procedures in SAS SAS procedures analyze data in SAS data sets to produce summary statistics to produce tables, listings & graphs to perform SQL queries to perform Statistical analyses to manage and print SAS files. SAS Procedures come in modules (e.g., SAS/BASE, SAS/STAT, SAS/SQL, SAS/IML, SAS/GRAPH) Commonly used procedures: PROC PRINTPROC REPORTPROC UNIVARIATE PROC MEANSPROC MIXEDPROC LOGISTIC PROC TTESTPROC NLINPROC GPLOT PROC FREQPROC SQLPROC IML

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CDM 20 Example (Canada Guieline, 1992) SubjectSequenceTreatmentPeriodAuCtln(AUCt) ATRT BRTT CRTT ETRT FRTT GTRT HRTT ITRT KRTT LTRT MTRT NRTT ORTT PTRT QRTT RTRT

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CDM 21 SubjectSequenceTreatmentPeriodAuCtln(AUCt) ATRR BRTR CRTR ETRR FRTR GTRR HRTR ITRR KRTR LTRR MTRR NRTR ORTR PTRR QRTR RTRR Example (Canada Guideline, 1992)

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CDM 22 Procedures in SAS

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CDM 23 Type 3 Tests of Fixed Effects EffectNum DFDen DFF ValuePr > F Sequence Period Treatment Estimates LabelEstimateStandard ErrorDFt ValuePr > |t|AlphaLowerUpper T VS R Label AUCt Ratio (T/R) Lower Limit of AUCt Ratio Upper Limit of AUCt Ratio T VS R87.68%74.09%103.77% MACR Procedures in SAS

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CDM 24 Macros in SAS Collection of SAS statements which can be used repeatedly Why macro? -Same program can be used repetitively -Makes program simpler -Data driven programs can be made, letting SAS decide what to do based on actual data values Macros are complicated, but makes the work lot easier

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CDM 25 Macros in SAS Defining of a macro Calling of a macro Specifying the analysis, algorithm etc. Name of the macro Key-parameter

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CDM 26 SAS in CDM Clinical Trial of all phases -Sample size estimation -Randomization schedule -Tables, Listings & Figures (TLFs) Pre-clinical Data Analyses Pharmacovigilance signal generation Pharmacokinetic (PK) analyses Pharmacodynamic (PD) analyses Non-standard -Repeated Measure -Nonlinear Mixed Model -Bayesian

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CDM 27 In-house Developed Macro Pre-clinical Data Analyses Pharmacovigilance Sample Size Randomization Schedule

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CDM 28

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CDM 29 Body weight – Change and % change Clinical Chemistry parameters (n=20) Hematology parameters (n=21) Urine parameters (n=4) Organ weights (n=8-9) -Absolute -Relative to body weight -Relative to brain weight Parameters Analysis is done for both Main and Recovery part of the study For male and female separately

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CDM 30 Flow of Stat Analyses Verifying Normality Assumption ANCOVA Dunnett pair-wise comparison K-W test Wilcoxon pair-wise comparison Log transform Inverse transform Square root Normal Non Normal

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CDM 31

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CDM 32 Process flow Excel data SAS data Tables and graphs %normtest %toxico %toxico_comb %toxico_rec

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CDM 33

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CDM 34

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CDM 35

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CDM 36 N Proportional Reporting Ratio Relative Reporting Ration Chi- square Stat task…

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CDM 37 Process flow Excel data SAS data Tables and graphs SAS programs

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CDM 38

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CDM 39

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CDM 40

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CDM 41 Validation Program validation Dataset validation Output validation Macro validation

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CDM 42 Program Validation Documented evidence that program performs as expected Log inspection Log enhancement Intermediate results checking Style Simplicity Readability (use of comments) Re-usability Syntax checking Logical Dead ends Infinite loops Code never executed

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CDM 43 Program Validation Documented evidence that program performs as expected Log inspection Log enhancement Intermediate results checking Style Simplicity Readability (use of comments) Re-usability Syntax checking Logical Dead ends Infinite loops Code never executed Act in haste and repent in leisure, Code too soon and debug forever - Raymond Kennington

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CDM 44 Program Validation Cross verification with requirement/ algorithm Documentation (History and Version)

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CDM 45 Output Validation Matching of exact values Layout Format of the values Consistency with the SOPs/ SAP/ Specification document

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CDM 46 SAS Macro Validation White Box testing - Takes account internal mechanism of macro - Testing with known, provided data and known results - Check for the correct results - Only legal parameters should be specified for its arguments Black Box testing - Ignores the internal mechanism of macro - Testing with unknown data and unknown results - Check for plausible results - Any kind of parameters should be specified for its arguments - Focuses only on the output

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CDM 47 Compliance SAS System Installation Mgt. - All installations are documented to the \core\sasinst\hotfix directory - Testing of installation done by SAS Institute supplied installation test kit located in \core\sastest. Version Control - Important for a regulated environment to track changes in program file, log file and output file. - SAS does not provide these feature. - It can be attained through use of version control packages such as Microsoft Visual SourceSafe.

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CDM 48 Compliance Security of SAS Datasets - Controlled access to the contents of SAS datasets can be administered through password protection of the dataset Retrieval of Electronic Records - Compliance is straightforward - Printing audit trails can be done by setting the TYPE option to TYPE=Audit in PROC PRINT SAS Coexistence with FDA 21 CFR Part 11, How Far Can We Get? – Available at Audit Trails for SAS Datasets - With PROC DATASETS, it is possible to initiate SAS dataset specific audit trails, that log dataset updates, modification and deletions.

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CDM 49 SAS for CDISC Data standards are critical component in quest to improve global public health. Varying data standards CDISC attempts to define an industry standard for clinical data formatting SDTM, ODM, LAB and ADaM can be effectively implemented in SAS Drug Development ODM SDTM PROC CDISC SAS XML LIBNAME ENGINE SAS Dataset

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CDM 50 Data Flow in e-Submission

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CDM 51 Any Questions

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CDM 52 Thank You…!

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