Developing, Managing, and Evaluating a Standard Macro System Please use my personal email address, because I do not know how long my company address will be there. Time: 19 mins Albert Mo Biometrics XOMA, LLC Berkeley, CA

Agenda Introduction Definition of SMS Creation & Evolution of SMS Developing SMS Managing SMS Evaluating SMS Challenges & Opportunities Conclusion Q & A Here are the items that I will be talking about. At the end, I will leave a few minutes for Q & A. Time: 2 mins

Introduction & Definition What is a Standard Macro System? Globally developed Fully integrated Centrally managed What is NOT a Standard Macro System? Standard Macro Libraries Project (TA) level macros SMS has been a very popular, effective, and critical tool in many pharma companies. They are a set of SAS macros designed to automate the generation of TLG for clinical studies. I use some of the attributes to define the SMS. Time: 2:50

Creation & Evolution of SMS Company xxx Table XX Protocol xxx Title of Table (Population: xxx) Treatment A (N=XX) Treatment B (N=XX) P-value Treatment A vs. Treatment B Variable 1 XX X.XXX n(%) XX (XX.X%) Variable 2 N MEAN XX.X STD XX.XX MEDIAN MIN-MAX (XX-XX) 95% CI (XX, XX) Footnotes of Table: (Page x of y) Table 1. Sample Summary Table Shell Here is a typical Table Shell. It will take some effort, even for an experienced stat programmer, to develop and validate the program. More variations: Adding a new column of Treatment C Adding new rows for Variable 3 Adding another column of “Treatment B vs. C” Etc. General practices: “Just copy the program and make a new TLG…” We seem to be “Reinventing wheels” all the time. Many nightmares: Stat programmers: speed and accuracy Reviewers: consistency and confidence Managers: all parties are satisfied. Creating SMS to make lives easier… Time: 5 mins

Figure 1. SMS System Flowchart Structure of SMS Figure 1. SMS System Flowchart TLG (t_demog.pdf) High-level Utility Macros: %setup(colvar=treat, …); %contsum(var=age, …); %catsum(var=sex, …); %catsum(var=race, …); %print(…); End-user Macros: %demog_table(…); Application Program (t_demog.sas) %demog_table (…); Analysis Data Sets (demog) Low-level Utility Macros: %printset(…); %pageset(…); %nobs( …); %wordcnt(…); %chkparm(…); Application programs: t_demog(a list of parameters) End-user macros: %demog_table %ae_table High-level macros: %catsum %contsum %print Low-level macros: %pageset %wordcnt %chkparm Some SMSs do not have end-user macros and the application programmers have to assemble a set of high-level macros. Time: 6 mins

Benefits of SMS 1. Improved productivity Fewer Customized Programs More Standardized Programs + Premises: Customized vs. Standardized To avoid “re-inventing the wheels”. Time: 6:30 mins

Benefits of SMS 2. Savings from Centralized effort Centralized team effort in: Developing Supporting Training Savings: Developed once! Tested once! Validated once! Re-used again and again!!! Pool talents together Time: 7 mins

Benefits of SMS 3. Company-wide standardization Figure 2. SMS Stakeholders and their Roles with SMS Input Analysis Data Sets Clinical Data Management (CDM) SMS Output TLGs Statistical Programming Biostatistics & Other Clinical Operations CRFs Clinical Database Data sets Site Investigators (External) CSRs Clinical data is complex. Its environment is complex Here is a grand schema of its environment. To reap the benefits of SMS, we need to have standardization all around it. Push forward Push backward Streamline the operations From: standard CRF modules to To: standard way display p-values in the tables… Time: 8:30 mins

Developing SMS Assemble a SMS team Establish coding standard Think globally, yet work locally Guarantee backward-compatibility Respect users’ working environment Apply Software Development Life Cycle (SDLC) 12 recommendations I will mention 6 points. More in my SUGI paper. %local vs. %global  even more critical. Time: 9:30 mins

Software Development Life Cycle (SDLC) Figure 3. Software Development Life Cycle (SDLC) User Needs Analysis Design Unit Testing User Acceptance Test Community Requirements Implementation Coding SDLC has been around for many years. It maybe a novelty for some in Stat Programming environment (under the gun…) A circle of processes to help making a quality software products Each process has Deliverables, documentations Two-way Communication in each process All processes are centered around the User Community More stringent  Better software product Time: 10:30 mins

Managing SMS Secure upper management’s support and commitment Provide proper leadership to the SMS team Establish a steering committee Organize a SMS User Group (SMSUG) Establish 24/7 technical support Conduct regular training & education sessions (eHandbook) 12 recommendations I will mention 6 points. More in my SUGI paper. Customer-orientation. Time: 11:30 mins

Evaluating SMS - Critical Success Factors (CSFs) Management support Error-free confidence and reliability Robustness and flexibility Quick response Continuous improvement Continuous education & training Continuous promotion (raising awareness) 12 recommendations I will mention 6 points. More in my SUGI paper. Success statement: VP: “Whoever wants to deviate from the standard set of TLGs needs my approval…: Doomed statement: “My manager said I would deviate from the standard any time I wanted to…” Time: 11:30 mins

Challenges - Complexity stemmed from having many stakeholders All input data (empty data sets, existence of data sets/variables, missing values, zero divisions, large sizes, etc.) All customers (statistical programmers, biostatisticians, clinicians, clinical data managers, drug safety, medical writing, and other clinical operations personnel, etc.) All statistical methods (PROC FREQ, PROC NPAR1WAY, PROC GLM, PROC LIFETEST, PROC LIFEREG, etc.) All tables (missing columns, missing cells, empty columns/cells, etc.) All output (styles, margins, formats, titles/footnotes, “No Report Generated”, etc.) SMS can be very complex With proper planning, it can be manageable. Time: 11: 45 mins

Challenges - Concerns about job security Fears: “Now that we have SMS, I will no longer be needed, or have a job…” Focus on: Creation of analysis data sets Validation of application programs Project planning/management Participation of SMS development and testing Development of other selective customized TLG programs: There is an 80-20 rule: 80% of TLGs can be generated by SMS, while 20% of TLGs still need to be created with customized programs. Human factors cannot be ignored. We must calm their fears. Never run out of things to do… Time: 12:30 mins

Opportunities Emergent trends of company-wide standardization: Standard CRF modules Standard Analysis Dataset (ADS) Specification Standard Statistical Analysis Plan (SAP) templates Standard CSR templates SOPs and other Operational Guides Emergent trend of industry-wide standardization: CDISC (Clinical Data Interchange Standards Consortium) standard Trends make SMS’s operational environment more smooth. Time: 14 mins

Conclusion SMS is probably the biggest investment in Biostatistics operation. But it will pay off. Our ultimate goal of “A new TLG is just another standard macro call away!” is closer to becoming a reality. Many companies have started their journey. Some are very successful; some are struggling along the way. Hope I provided some tips to help your journeys easier. Time: 15 mins

Q & A Thank you all for your attention. Now, I would like to open the floor for questions. Time: 16 miins

Contact information Albert Mo mo@xoma.com Please use my personal email address, because I do not know how long my company address will be there. Time: 19 mins