\ \ JOB ORIENTED COMPLETE CLINICAL SAS TRAINING \ \ Trainer: Kiran Website: http://learnsasonline.com Contact Details : +91 9542357321 (Phone and Whats A pp) Course Duration : 90 hours Base SAS : 30 hours Advanced SAS : 1 0 hours Clinical SAS : 30 hours Clinical Project : 2 0 hours Specialities Latest 9.4 version of SAS Software and all the related softwares will be installed in the students system. Daily tasks will be given after each session. 30 e - Books, SAS Certification guidance and sample questions, All the programming files, pdf’s, Datasets etc., will be provided. Resume preparation, Interview Preparation, Mock Interviews and J ob - Assistance will be provided. DETAILED SYLLABUS OF ALL THE MODULES SAS 9.4 Base Programming Create temporary and permanent SAS data sets. Use a DATA step to create a SAS data set from an existing SAS data set. Investigate SAS data libraries using base SAS utility procedures. Use a LIBNAME statement to assign a library reference name to a SAS library. Investigate a library programmatically using the CONTENTS procedure. Access data. Access SAS data sets with the SET statement. Use PROC IMPORT to access n on - SAS data sources. Read delimited and Microsoft Excel (.xlsx) files with PROC IMPORT. Use PROC IMPORT statement options (OUT=, DBMS=, REPLACE) Use the GUESSINGROWS statement Use the SAS/ACCESS XLSX engine to read a Microsoft Excel workbook.xlsx file. Combine SAS data sets. Concatenate data sets. Merge data sets one - to - one. Merge data sets one - to - many. Create and manipulate SAS date values. Explain how SAS stores date and time values. Use SAS informats to read common date and time expressions. Use SAS date and time formats to specify how the values are displayed. Control which observations and variables in a SAS data set are processed and output. Use the WHERE statement in the DATA step to select observations to be processed. Subset variables to be output by using the DROP and KEEP statements. Use the DROP= and KEEP= data set options to specify columns to be processed and/or output. Sort observations in a SAS data set. Use the SORT Procedure to re - order observations in place or output to a new dataset with the OUT= option. Remove duplicate observations with the SORT Procedure. Conditionally execute SAS statements. Use IF - THEN/ELSE statements to process data conditionally. Use DO and END statements to execute multiple statements conditionally. Use assignment statements in the DATA step. Create new variables and assign a value. Assign a new value to an existing variable. Assign the value of an expression to a variable. Assign a constant date value to a variable. Modify variable attributes using o ptions and statements in the DATA step. Change the names of variables by using the RENAME= data set option. Use LABEL and FORMAT statements to modify attributes in a DATA step. Define the length of a variable using the LENGTH statement. Accumulate sub - tota ls and totals using DATA step statements. Use the BY statement to aggregate by subgroups. Use first. and last. processing to identify where groups begin and end. Use the RETAIN and SUM statements. Use SAS functions to manipulate character data, numeri c data, and SAS date values. Use SAS functions such as SCAN, SUBSTR, TRIM, UPCASE, and LOWCASE to perform tasks such as the tasks shown below. Replace the contents of a character value. Trim trailing blanks from a character value. Search a character value and extract a portion of the value. Convert a character value to upper or lowercase. Use SAS numeric functions such as SUM, MEAN, RAND, SMALLEST, LARGEST, ROUND, and INT. Create SAS date values by using the functions MDY, TODAY, DATE, and TIME. Extract the month, year, and interval from a SAS date value by using the functions YEAR, QTR, MONTH, and DAY. Perform calculations with date and datetime values and time intervals by using the functions INTCK, INTNX, DATDIF and YRDIF. Use SAS functions to convert cha racter data to numeric and vice versa. Explain the automatic conversion that SAS uses to convert values between data types. Use the INPUT function to explicitly convert character data values to numeric values. Use the PUT function to explicitly convert num eric data values to character values. Process data using DO LOOPS. Explain how iterative DO loops function. Use DO loops to eliminate redundant code and to perform repetitive calculations. Use conditional DO loops. Use nested DO loops. Restructure SAS data sets with PROC TRANSPOSE. Select variables to transpose with the VAR statement. Rename transposed variables with the ID statement. Process data within groups using the BY statement. Use PROC TRANSPOSE options (OUT=, PREFIX= and NAME=). Use macro variables to simplify program maintenance. Create macro variables with the %LET statement Use macro variables within SAS programs. Identify and resolve programming logic errors. Use the PUTLOG Statement in the Data Step to help identify logic errors. Use PUTLOG to write the value of a variable, formatted values, or to write values of all variables. Use PUTLOG with Conditional logic. Use temporary variables N and ERROR to debug a DATA step. Recognize and correct syntax errors. Identify the characteristics of SAS stat ements. Define SAS syntax rules including the typical types of syntax errors such as misspelled keywords, unmatched quotation marks, missing semicolons, and invalid options. Use the log to help diagnose syntax errors in a given program. Generate list repor ts using the PRINT procedure. Modify the default behavior of PROC PRINT by adding statements and options such as use the VAR statement to select and order variables. calculate totals with a SUM statement. select observations with a WHERE statement. use the ID statement to identify observations. use the BY statement to process groups. Generate summary reports and frequency tables using base SAS procedures. Produce one - way and two - way frequency tables with the FREQ procedure. Enhance frequency tables with options (NLEVELS, ORDER=). Use PROC FREQ to validate data in a SAS data set. Calculate summary statistics and multilevel summaries using the MEANS procedure Enhance summary tables with options. Identify extreme and missing values with the UNIVARIATE p rocedure. Enhance reports system user - defined formats, titles, footnotes and SAS System reporting options. Use PROC FORMAT to define custom formats. VALUE statement CNTLIN= option Use the LABEL statement to define descriptive column headings. Control the use of column headings with the LABEL and SPLIT=options in PROC PRINT output. Generate reports using ODS statements. Identify the Output Delivery System destinations. Create HTML, PDF, RTF, and files with ODS statements. Use the STYLE=option to specify a s tyle template. Create files that can be viewed in Microsoft Excel. Export data Create a simple raw data file by using the EXPORT procedure as an alternative to the DATA step. Export data to Microsoft Excel using the SAS/ACCESS XLSX engine. SAS 9.4 Advance d Programming Generate detail reports by working with a single table, joining tables, or using set operators in SQL Use PROC SQL to perform SQL queries. Select columns in a table with a SELECT statement and FROM clause. Create a table from a query result set. Create new calculated columns. Assign an alias with the AS keyword. Use case logic to select values for a column. Retrieve rows that satisfy a condition with a WHERE clause. Subset data by calculated columns. Join tables - inner joins, full joins (coalesce function), right joins, left joins. Combine tables using set operators - union, outer union, except, intersect. Sort data with an ORDER BY clause. Assign labels and formats to columns. Generate summary reports by working with a single table, join ing tables, or using set operators in the SQL. Summarize data across and down columns using summary functions (AVG, COUNT, MAX, MIN, SUM). Group data using GROUP BY clause. Filter grouped data using HAVING clause. Eliminate duplicate values with the DISTIN CT keyword. Construct sub - queries and in - line views within an SQL procedure step. Subset data by using non - correlated subqueries. Reference an in - line view with other views or tables (multiple tables). Use SAS SQL procedure enhancements. Use SAS data set o ptions with PROC SQL (KEEP=, DROP=, RENAME=, OBS=). Use PROC SQL invocation options (INOBS=, OUTOBS=. NOPRINT, NUMBER) Use SAS functions (SCAN, SUBSTR, LENGTH). Access SAS system information by using DICTIONARY tables (members, tables, columns) Create and use user - defined and automatic macro variables within the SAS Macro Language. Define and use macro variables. Use macro variable name delimiter. (.) Use INTO clause of the SELECT statement in SQL to create a single variable or a list of variables. Use the SYMPUTX routine in a DATA Step to create a single variable or a list of variables. Control variable scope with: %GLOBAL statement %LOCAL statement SYMPUTX scope parameter Automate programs by defining and calling macros using the SAS Macro Language. Define a macro using the %MACRO and %MEND statements. Calling a macro with and without parameters. Document macro functionality with comments Generate SAS Code conditionally by using the %IF - %THEN - %ELSE macro statements or iterative %DO statements. Use the SAS AUTOCALL facility to permanently store and call macros. Use macro functions. Use macro functions. (%SCAN, %SUBSTR, %UPCASE) Use macro quoting functions. (%NRSTR, %STR) Use macro evaluation functions. (%SYSEVALF) Use %SYSFUNC to execute DATA step functions within the SAS Macro Language. Debug macros. Trace the flow of execution with the MLOGIC option. Examine the generated SAS statements with the MPRINT option. Examine macro variable resolution with the SYMBOLGEN option. Use the %PUT statement to print infor mation to the log. Create data - driven programs using SAS Macro Language. Create a series of macro variables. Use indirect reference to macro variables. (&&, etc.) Incorporate DICTONARY tables in data driven macros. Generate repetitive macro calls. Process data using 1 and 2 dimensional arrays. Define and use character arrays. Define and use numeric arrays. Create variables with arrays. Reference arrays within a DO loop. Specify the array dimension with the DIM function. Define arrays as temporary arrays. Load initial values for an array from a SAS data set. Clinical Trials Programming Clinical Trials Process Describe the clinical research process (phases, key roles, key organizations). Interpret a Statistical Analysis Plan. 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). Clinical Trials Data Structures Identify the classes of clinica l 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. Describe the structure and purpose of the CDISC ADaM data model. Describe th e contents and purpose of define.xml. Import and Export Clinical Trials Data Apply regulatory requirements to exported SAS data sets (SAS V5 requirements). Manage Clinical Trials Data Access DICTIONARY Tables using the SQL procedure. Examine and explore cl inical trials input data (find outliers, missing vs. zero values, etc). Transform Clinical Trials Data Apply categorization and windowing techniques to clinical trials data. Transpose SAS data sets. Apply 'observation carry forward' techniques to clinical trials data (LOCF, BOCF, WOCF). Calculate 'change from baseline' results. Obtain counts of events in clinical trials. 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 statistic al procedures. 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 (MPRIN T, SYMBOLGEN, MLOGIC, MACROGEN). Report Clinical Trials Results Use PROC REPORT to produce tables and listings for clinical trials reports. Use ODS and global statements to produce and augment clinical trials reports. Validate Clinical Trial Data Reporting Explain the principles of programming validation in the clinical trial industry. Utilize the log file to validate clinical trial data reporting. Use programming techniques to validate clinical trial data reporting (PROC COMPARE, MSGLEVEL). Identify and Re solve data, syntax and logic errors. Clinical Project Understanding the Industry Drug Development Process World Medical Association guidelines ICH - GCP guidelines E3,E6,E8 ,E9 Protocol Reviews Final clinical study report /ISS/ISE/Final clinical report/CSR Statistical analysis plan Interpretation 21 CFR Part 11 Statistical Programmer Work Process Drug Approval Process Clinical Trial Study Design s CDISC Standard Data Structures Important Documents Summary (ADRG, SDRG, eCTD etc.,) Getting Started from the Case Report Form eCRF Portal Electronic CRFs (eCRFs) Demographics Disposition Adverse Events Exposure Concomitant Medications Electrocardiogram Lab Annotating the eCRF Annotating Unique eCRF Pages Appearance of Annotations Annotated CRF Practices Study Data Tabulation Model (SDTM) Variable “Roles” SDTM Standard Domains SDTM Core Variables Clinical Trial Schedule of Assessments Model for SDTM Generation Demographics (DM) Disposition (DS) Adverse Events (AE) Exposure (EX) Concomitant Medications (CM) Electrocardiogram Test Results (EG) Laboratory Test Results (LB) Trial Design Domains Trial Summary Data Set (TS) Trial Arm Data Set (TA) Trial Element Data Set (TE) Trial Visit Data Set (TV) Trial Inclusion/Exclusion Data Set (TI) Trial Disease Assessment (TD) SDTM Metadata Creation Table of Contents of Metadata Define Header Metadata Variable - Level Metadata Value - Level Metadata Codelist Metadata Computational Method Metadata Analysis Data Model (ADaM) ADaM Standard Structures Variable “Roles” Standard ADaM Structure and Domains Standard ADaM Variables Subject - Level Analysis Data Set (ADSL) Structure of ADSL ADSL Specification ADSL Programming Basic Data Structure (BDS) ECG Test Results Analysis Data Sets (ADEG) Lab Test Results Analysis Data Sets (ADLB) Occurrence Data St ructure (OCCDS) Adverse Event Analysis Data Set (ADAE) Concomitant Medication Analysis Data Set (ADCM) ADaM Metadata Creation Define Header Metadata Table of Contents Metadata Variable - Level Metadata Parameter - Level Metadata Computational Method Metadata Codelist Metadata Analysis Results Metadata External Links Metadata Case Report Tabulation Data Definition (Define - XML) Structure of Define - XML The Process of Creating Define - XML Create Metadata Spreadsheet and Create Define - XML Components Create XPT Files Link for External Documents Construct Define.XML Programming Validation, Edit check Import and export – sql - pass through, accessing the data from an excel, access using libname statement, proc cport, proc cimport for creating xport transport files. Defining variables once Defining baseline obs. Imputation Methods - LOCF – Last observation c arried forward, BOCF, WOCF Windowing data T ransposing T ime to event data set Change from baseline dataset Critical variables datasets Categorizing Study day calculation CDISC Validation Using Pinnacle 21 Community Getting Started with Pinnacle 21 Community Running Pinnacle 21 Community (Graphical User Interface) Evaluating the Report Modifying the Configuration Files A Note about Controlled Terminology Running Pinnacle 21 Community (Command - Line Mode) ADaM Validation with Pinnacle 21 Community ADaM Traceability Checks with SAS Define.xml Validation with Pinnacle 21 Community Preparation of TLF's/TLG's viz., Demographic table Adverse event table Lab shift table Listings Graphs