A Roadmap for SAS Programmers to Clinical Statistical Programming

A Roadmap for SAS Programmers to
Clinical Statistical Programming
1

Introduction
2
The purpose of this presentation is to describe step by step the transition of a SAS
Programmer into a Clinical Statistical Programmer. It can be used as guidelines
for SAS Programmers who wants to put their programming and technical expertise
into industries.
A SAS Programmer is someone who uses SAS software for different scenarios.
The person who uses it for different purposes is known as a SAS Programmer.
On the other hand, a Clinical Statistical Programmer performs all the procedures
to generate future outputs and makes advanced and real-world developments to
face further challenges. A primary role of Clinical Statistical Programmers is to use
their technical and programming skills in order to enable clinical trial statisticians
to perform their statistical analysis duties more efficiently.
This presentation will briefly discuss about the smooth transition that a SAS
Programmer needs to go through in order to become a Clinical Statistical
Programmer.

Clinical Statistical Programmer
3
A primary role of Clinical Statistical Programmers is to use
their technical and programming skills in order to enable
clinical trial statisticians to perform their statistical analysis
duties more efficiently.

Types of Clinical Trials
4
Classification by the way the researches behave:
1. Observational study (do not test drugs or treatments.
Researchers observe participants by monitoring their health over a period of
time.);
2. Interventional study (test the safety and effectiveness of a
candidate drug, therapy or experimental treatment).
Classification of The U.S. NIH (by purpose):
1. Prevention trials;
2. Screening trials;
3. Diagnostic trials;
4. Treatment trials;
5. Quality of life trials;
6. Compassionate use trials or expanded access.

The Drug/Device Development Process
5
Like many industries, the pharmaceutical industry has a vocabulary
and language all its own.
The clinical trial industry is primarily concerned with bringing new
drugs, biologics, devices, or therapies to the general population and
marketplace. In the United States, most clinical trials are funded by
pharmaceutical companies that want to bring a new treatment to
market or by the National Institutes of Health (NIH), which funds
research to improve the health of all Americans.

The Drug/Device Development Process
6

Drug Approval Process
7
Because the majority of clinical trials are conducted with the idea to bring a
new drug or device to market, we will briefly look at the U.S. Food and Drug
Administration (FDA) approval process.

Device Approval Process
8
Based on the degree of risk inherent in the device:
Class 1 devices carry little risk for the patient; they include devices
such as elastic bandages and surgical instruments.
Class 2 devices carry slightly higher risk for the patient; they include
such devices as infusion pumps and motorized wheelchairs.
Class 3 devices are high-risk devices and thus require the most
regulatory scrutiny. Class 3 devices include replacement heart valves
and implantable defibrillators.

Clinical Trial Study Designs
9
There are many types of clinical trials, and there are some general
trial design concepts that you need to understand.
One key concept is the randomization of study therapy. When you
randomly assign patients to study therapy, you reduce potential
treatment bias.
Another key concept is treatment blinding. Blinding a patient to
treatment means that the patient does not know what treatment is
being administered.

Regulations and Standards
10
The rules and regulations that govern the pharmaceutical industry
have many layers. A SAS programmer you will have to follow many
of those regulations, which can be broken down into three major
categories:
1. Federal Laws (The Code of Federal Regulations, HIPPA 1996);
2. Federal Guidelines (eCTDs);
3. Industry Regulations & Standards (ICH, CDISC etc.).

Federal Laws
11
Any work that you perform that contributes to a submission to the FDA is
covered by these federal regulations. There are a number of specific
regulations and guidance that you should know.
Code Description
“21 CFR – Part 11
Electronic Records;
Electronic
Signatures”
*CFR= Code of
federal regulations
A federal law that regulates the submission of
electronic records and electronic signatures to the
FDA. 21 CFR – Part 11 means that you must be
qualified to do your work, your programming must be
validated, you must have system security in place,
and you must have change control procedures for
your SAS programming.

Federal Laws
12
Code Description
Health Insurance
Portability and
Accountability Act
1996
HIPAA serves to protect the information about a
subject’s identifying information. It is the core reason
that the most specific identifying information about
each subject in every clinical trial conducted in the
United States is limited to the subject’s initials and
date of birth. Any identifying information that is more
specific is carefully protected by the investigating site.
When validating data that may come to you as a
programmer, it is important to understand that
personal information should not be included—and if it
is, it is your responsibility to point it out to have it
removed.

Federal Guidelines
13
“ICH E3 Structure and
Content of Clinical
Study Reports”
The “E3” describes in detail what reporting goes
into a clinical study report for an FDA submission.
“ICH E9 Statistical
Principles for Clinical
Trials”
The “E9” discusses the statistical issues in the
design and conduct of a clinical trial.
“ICH E6 Good Clinical
Practice: Consolidated
Guidance”
The “E6” (or GCPs) discusses the overall
standards for implementing a clinical trial.
Electronic Common
Technical Document
(eCTD) Specifications
and Guidance
It is the vision for future electronic submissions to
the FDA. This specification was developed by the
ICH as an open-standards solution for electronic
submissions to worldwide regulatory authorities.

Federal Guidelines
14
"Study Data
Standards“
It has details on how the FDA expects to get your
SDTM and ADaM data sets.
"CDER Common
Data Standards
Issues Document”
It is an extension of the Study Data Standards
document. It contains practical submission guidance
on how to send CDISC formatted data to the FDA and
various things that you need to watch out for when
submitting your data sets to the FDA.
CDER Data
Standards Program
The CDER Data Standards Program was founded in
2010 to help improve and refine the data standards
processes at CDER in order to make electronic
submissions more efficient.

Industry Regulations and Standards
15
International
Conference on
Harmonization
(ICH)
While the US FDA is the world’s leading drug approval
agency, other countries also develop drugs and have
agencies that regulate their approval. In a global setting, it
is important for all parties involved in drug development to
have a standard set of definitions for similar concepts and a
common understanding for how drugs should be
developed. This way, companies that develop drugs in one
country under one set of rules can apply to have the same
drug approved in other countries without having to
redevelop it. If all countries have the same understanding of
the rules, data developed elsewhere will follow a consistent
set of rules. ICH is a global organization that provides these
common definitions and guidelines and is often a source for
standard values for certain data.

Industry Regulations and Standards
16
Clinical Data
Interchange
Standards
Consortium
(CDISC)
A non-profit group that defines clinical data standards for
the pharmaceutical industry. CDISC has developed
numerous data models that you should familiarize yourself
with. Two key sets of standards that affect the majority of
clinical trial programmers are the Study Data Tabulation
Model (SDTM) used for submitting data tabulations and the
Analysis Data Set Model (ADaM) used for submitting
analysis data sets. While these two sets of standards
overlap in many areas, both have many distinct
components that can effect how data is stored.

Scenarios for Implementing CDISC
Standards
17

Information Flow Using CDISC Standards
19

Departments
20
Your Clinical Trial Colleagues:
1. Biostatistics
2. Site Management
3. Data Management
4. Information Technology

Project Management
21
Most contract research organizations and pharmaceutical companies
are organized in a matrix management structure. This structure is
called a matrix because there are project teams that span various
functional departments. It may help to visualize the relationship like
this:

Guiding Principles for the Clinical
Statistical Programmers
22
1. Understand the Clinical Study.
✓ A good Statistical Programmer takes his/her time to understand the
subject matter. Just because you are an expert SAS Programmer
doesn’t mean you know everything about a particular drug or
device or the disease state that it aims to cure.
✓ At first, you should focus on reading the clinical protocol. The
protocol describes the device or medication to be used, the patient
populations under study, the statistical plan of the clinical trial, and
the details of the disease state.

Guiding Principles for A Clinical
Statistical Programmer
23
✓ Second step would be studying and understanding the Statistical
Analysis Plan (SAP) which is a detailed document and it describes
how the clinical trial data will be analyzed.
✓ Finally, there is another very important document which is called
annotated CRF. It shows you where the variables in the clinical
database come from on the CRF. The following is an example of
an annotated medical history CRF page:

Guiding Principles for the Statistical
Programmer
24
2. Program a Task Once and Reuse Your Code Everywhere.
✓ An example can be using of a studyauto file with an %include
statement which will load all the required libraries and formats.
3. Use good defensive programming techniques.
4. Use SAS Macros Judiciously.
5. A Good Programmer Is a Good Student.
6. Strive to Make Your Programming Readable.

Guiding Principles for the Statistical
Programmer
25
7. A list of common reporting tasks that you need to be familiar with:
1. Categorical data analysis;
2. Continuous data analysis;
3. Data transposition;
4. Header N calculations;
5. Output formatting, pagination.

Trial Overview & Journey Sample
26
1. SCREENING VISIT
At first when you first appear the screening visit for phases I-III, the
study is explained in detail and you are free to ask any questions. If
you agree to participate, the study nurse will review the informed
consent form with you and ask for your signature and authorization to
proceed with the screening evaluations. It usually lasts 1-3 hours.

27
2. BASELINE VISIT
If the screening tests results are positive then you are fit to participate,
the study coordinator will schedule an entry — or baseline — visit.
When you attend this visit, the first step in your trial, you’ll receive
your study medication and the dosing will be studied with you. There
will be some lab tests done to get baseline values before the start of
your study treatment.

28
3. CLINICAL TRIAL VISITS
The number of your clinical visits will depend on the specifics and
length of the study. There could be follow-up visits that contain a brief
physical exam (not usually in phase IV observational), an assessment
of your study medications and your symptoms, and lab tests.

29
4. LAST CLINICAL TRIAL VISIT
This visit doesn’t always mean that the journey is end — the trial may
require follow-up visits. Ask when the results will be available, and
how you will be informed. While your part may be over, the clinical trial
itself can last longer.

30
5. WHAT HAPPENS WHEN THE TRIAL IS OVER?
When your participation in the trial is done, in some cases the
research coordinator could arrange telephone follow-ups with you.
Other patients may still be participating, so when the trial is formally
over, the results will be available on the site as soon as they have
been made public.

Classifying Clinical Trial Data
32
The data are broadly classified as efficacy data and safety data. CDISC
have categorized data into interventions class, events class, findings class,
and other special-purpose “domains” such as demographics.

33
The following sample CRF forms have been made to align with the CDISC
CDASH standard:
1. Demographics and Trial-Specific Baseline Data
2. Concomitant or Prior Medication Data
3. Medical History Data
4. Laboratory Data
5. Adverse Event Data
6. Endpoint/Event Assessment Data
7. Clinical Endpoint Committee (CEC) Data
8. Study Termination Data
9. Treatment Randomization Data
10. Quality-of-Life Data

34
1. Demographics and Trial-Specific Baseline Data
Trial-specific patient characteristics may be included with the
demographics data as well. Height, weight, smoking status, and
sometimes vital signs are common additions. These measures are
collected because they may be relevant to the therapeutic intervention
and could be used to stratify the statistical analysis. Here is a typical
demographics CRF:

35
2. Concomitant or Prior Medication Data
Concomitant medications and prior medications are collected in one
of two forms: a list-type free-text format where the medications get
coded later by data management, or a pre-categorized data format.
Here is the free-text CRF format:

36
Here is the pre-categorized per protocol CRF format:
The free-text CRF format is useful in that it allows for an explicit
description of the medication taken, whereas the pre-categorized
format omits that detail. However, the free-text list format necessitates
additional coding with a coding dictionary such as WHOdrug in order
to be useful for analyses.

37
3. Medical History Data
Like concomitant medication data, patient medical history data are
collected in one of two forms: a list-type free-text format where the
histories get coded, or a pre-categorized data format. Here is the free-
text CRF format:

38
Here is the pre-categorized medical history CRF format:
The free-text CRF format is useful in that it allows for explicit
description of the historical condition, whereas the pre-categorized
CRF format omits that detail. However, the free-text list format
necessitates coding with a coding dictionary such as MedDRA in
order to be useful for analyses.

39
4. Laboratory Data
It may consist of many different collections of tests, such as ECG
laboratory tests, microbiologic laboratory tests, and other therapeutic-
indication-specific clinical lab tests. It traditionally consists of results
from urinalysis, hematology, and blood chemistry tests.

40
5. Adverse Event Data
In the FDA’s “Guidance for Industry E6 Good Clinical Practice:
Consolidated Guidance,” an adverse event is defined as follows:
“Any untoward medical occurrence in a patient or clinical investigation subject
administered a pharmaceutical product and that does not necessarily have a causal
relationship with this treatment. An AE can therefore be any unfavorable and
unintended sign (including an abnormal laboratory finding), symptom, or disease
temporally associated with the use of a medicinal (investigational) product, whether or
not related to the medicinal (investigational) product.”
There are several data issues for the statistical programmer to be
concerned about here.

41
In just about any clinical trial, an adverse event form similar to the
following sample will be found.

42
There are several data issues for the statistical programmer to be
concerned about here.
✓ Treatment-Emergent Signs and Symptoms
✓ Serious Adverse Event Reconciliation (to be reported to FDA)
✓ Concomitant Medication Reconciliation
✓ Laboratory Data Reconciliation

43
6. Endpoint/Event Assessment Data
Endpoint or event assessments typically capture what the clinical trial
was designed to study. For example, if a clinical trial were studying an
anti-epilepsy medication, then the event form would likely collect
seizure information. The endpoint or event assessment form is
designed to collect data after the investigational drug or device
intervention so that these data can be statistically compared to data
from the patient’s state before the drug or device intervention.

44
7. Clinical Endpoint Committee (CEC) Data
It is often the case that the endpoint/event form captures data that are
not entirely objective because they contain some level of clinical
judgment. A sample CEC form follows:

45
8. Study Termination Data
The study termination form collects patient exit information from the
clinical trial. Here is a sample study termination form:

46
9. Treatment Randomization Data
They are often found within some form of Interactive Voice Response
System (IVRS), but they may also be found in an electronic file that
contains the treatment assignments or on the CRF itself. If
randomization data are found on the CRF, they usually consist only of
the date of randomization for treatment-blinded trials.
IVRS data are often found outside the confines of the clinical data
management system and usually consist of the following three types
of data tables.

47
10. Quality-of-Life Data
Quality-of life data are collected to measure the overall physical and
mental well-being of a patient. These data are usually collected with a
multiple-question patient questionnaire and may be summed up in an
aggregate patient score for analysis. Some commonly used quality-of-
life questionnaires are the SF-36 and SF-12 Health Survey, but there
are quite a few disease-specific QOL questionnaires available to
clinical researchers.

Validation
48
The validation of a SAS programmer's work is essential because the industry
is governed by federal laws, SAS programmers are bound by a very strict set
of rules and regulations.
Validation techniques:
• Using procedures such as PROC COMPARE, PROC PRINT, PROC
MEANS, PROC FREQ.
• Using SAS options to your advantage: MPRINT, SYMBOLGEN, and
MLOGIC.
• It is good practice to review your own code after it is written to make sure
that the comments make sense and are sufficient to explain what is being
done.
• By keeping only the variables that you need.

Validation
49
Validation techniques:
• Log review is another important step in the validation process. Searching
the following keywords could be an effective technique:
ERROR
WARNING
INFO: Character
INFO: The variable
NOTE: Character
NOTE: Division
NOTE: Merge
NOTE: Missing
NOTE: Numeric

The Future of SAS Programming in
Clinical Trials: Changes in Technology
50
There was a time when the regulatory submissions to the authorities
were entirely paper-based, but now, the drug applications are being
sent to the authorities in electronic formats, in PDF documents and
SAS transport format files. But in future, it’s expected that in future the
drug application and approval process are going to be completely
electronic.
Apart from SAS, Python and R language are also competing for
attention. As a SAS programmer, you need to be well-informed about
those changes so that you can continue to be a prolific information
technology professional.

Clinical Trials: Changes in Regulations
51
In 1997, the regulation for electronic signatures and electronic
records, 21 CFR–Part 11, went into effect. It necessitates system
validation and security of all clinical data software used in the conduct
of clinical trials. In 2003, new HIPAA regulations from the United
States Department of Health and Human Services placed privacy
restrictions on how patient data can be shared. Most recently are the
changes in the Prescription Drug User Fee Act (PDUFA) V that grant
the FDA the authority to require electronic submissions and the ability
to mandate standards. As a SAS programmer need you should
always try to be informed about these regulations so that you can
keep your work and organization compliant.

Clinical Trials: Changes in Standards
52
Lack of standards led to inefficiency as companies had to negotiate
how to exchange their clinical trial information. CDISC worked for
solving this problem by publishing open data models for the worldwide
clinical trial industry to use. As the adoption of the CDISC data models
grows, you will see software systems grow in their ability to use them.
This will mean more standardization of data exchange tools and
clinical reporting tools. As a SAS programmer, you need to be working
toward standardizing your data analysis processes around the use of
the data standards provided by CDISC. You should also be vigilant to
watch for software that will leverage industry standards.

Reference
53
[1] Jack, Shostak, Date of Publication: March 2014, SAS Programming in Pharmaceuticals
Industries, Second Edition, United States, ISBN: 978-1-61290-805-2. [Accessed: 27JAN18].
[2] Chris, Holland and Jack, Shostak, Date of Publication: March 2014, Implementing CDISC Using
SAS® An End-to-End Guide, United States, ISBN: 978-1-60764-888-8. [Accessed: 02FEB18].
[3] Brian, Shilling, 2015. The 5 Most Important Clinical SAS Programming Validation Steps, [Internet]
<https://www.pharmasug.org/proceedings/2015/IB/PharmaSUG-2015-IB01.pdf> [Accessed:
16APR18].
[4] CDISC, Introducing the CDISC Standards: New Efficiencies for Medical Research, A CDISC
Publication. [Accessed: 05FEB18].
[5] Investor's Guide To Clinical Trials: Phase Success Rates For Introductory Pipeline Analysis,
[Internet] <https://seekingalpha.com/article/4051661-investors-guide-clinical-trials-phase-success-
rates-introductory-pipeline-analysis> [Accessed: 06MAR18].
[6] Sunil, Boreddy, Drug Development and Clinical Trials phases, [Internet]
<https://www.slideshare.net/boreddysunilkumarreddy/drug-development-and-clinical-trial-phases>
[Accessed: 06MAR18].

Questions?
54
Mohammad Majharul Alam,
Statistical Programmer,
SAS Certified Advanced & Clinical Trials Programmer,
Shafi Consultancy Bangladesh.
majharul@shaficonsultancy.com
www.shaficonsultancy.com

A Roadmap for SAS Programmers to Clinical Statistical Programming

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