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SAS in Pharmaceutical Industry

SAS in Pharmaceutical Industry. 30 July 2009 Arjun Roy & Madan Gopal Kundu Clinical Data Management & Biostatistics MACR. Contents. Statistical software. SAS – Advantage, History, Definition, Windows. Basic Programming. SAS Macro, Examples. Validation, Compliance .

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SAS in Pharmaceutical Industry

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

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

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

  4. Statistical Software

  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

  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 1976 • 98 of world’s top 100 company in Fortune 500 use SAS • CFR part 11 compliant

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

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

  9. SAS as a Data Warehouse

  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

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

  12. SAS for Statistical Analyses • Descriptive statistics • Contingency Tables • Correlation / Regression • t-test • Wilcoxon test • General Linear Model (ANOVA, ANCOVA) • Logistic regression • Chi-square/ Fisher’s exact test • Trend test • Dunnett Multiple comparison • Logrank test/ Kaplan Meier

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

  14. Learning SAS!!

  15. SAS Windows • Editor • Log • Output • Result • Explorer • Graph

  16. 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) EDITOR To write/ modify SAS program code

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

  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.)

  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 PRINT PROC REPORT PROC UNIVARIATE PROC MEANS PROC MIXED PROC LOGISTIC PROC TTEST PROC NLIN PROC GPLOT PROC FREQ PROC SQL PROC IML

  20. Example (Canada Guieline, 1992)

  21. Example (Canada Guideline, 1992)

  22. Procedures in SAS

  23. Procedures in SAS MACR

  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

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

  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

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

  28. Repeated Dose Tox Stat Analyses

  29. Parameters • 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 Analysis is done for both Main and Recovery part of the study For male and female separately

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

  31. Process flow Excel data %normtest %toxico %toxico_comb %toxico_rec SAS data Tables and graphs

  32. Pharmacovigilance - SDR generation

  33. Stat task… • N • Proportional Reporting Ratio • Relative Reporting Ration • Chi- square

  34. Process flow Excel data SAS programs SAS data Tables and graphs

  35. Validation • Program validation • Dataset validation • Output validation • Macro validation

  36. 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

  37. 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

  38. Program Validation • Cross verification with requirement/ algorithm • Documentation (History and Version)

  39. Output Validation • Matching of exact values • Layout • Format of the values • Consistency with the SOPs/ SAP/ Specification document

  40. 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

  41. Compliance SAS System Installation Mgt. - All installations are documented to the <SASROOT>\core\sasinst\hotfix directory - Testing of installation done by SAS Institute supplied installation test kit located in <SASROOT>\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.

  42. Compliance 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. 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 www.lexjansen.com/pharmasug/2002/proceed/fdacomp/fda05.pdf

  43. 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 PROC CDISC ODM SDTM SAS Dataset SAS XML LIBNAME ENGINE SDTM, ODM, LAB and ADaM can be effectively implemented in SAS Drug Development

  44. Data Flow in e-Submission

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