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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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 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

15. 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

16. 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

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

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