Statistical analysis constitutes an essential part of every serious scientific research. Without data and a formal process of searching for evidences supporting or disproving stated hypotheses, there is nothing but mere opinion. Evidence-based medicine is no exception

1. 18 l November 2016
| Biostatistics
Statistical analysis constitutes an
essential part of all serious scientific
research. By conducting controlled
experiments, medical, genetics and
pharma companies can assess various
objectives, including safety, efficacy,
tolerability, pharmacokinetics and
pharmacodynamics of the
developed therapy.
Over recent years, huge developments
have been made in the field of biostatistics.
Not so long ago, multi-way ANOVA was
found to be a fairly sophisticated statistical
method, similarly to ridge regression
or factor analysis. Nowadays, however,
statistical methods are becoming more and
more advanced, as well as computationally
intensive. Complex trials involving the use
of advanced statistical methods – such
as nonlinear mixed-effects models, GEE,
simulation-based inference and multiple
data imputation – require appropriate tools.
SAS and GNU R
Two such tools, SAS® and GNU R, are two of
the leading packages in statistical analysis.
Employing both of them in cooperation
may result in tangible benefits for CROs.
SAS
Provided by the SAS Institute and well-
established in clinical research, SAS is
a reliable and powerful package and,
therefore, recognised as industry standard.
The set of implemented statistical method
covers most of the classic and modern
algorithms. SAS is perfectly suited to
process data sources consisting of millions
of records, and is a good solution –
Without data and a formal process of searching for proof to either
support or disprove stated hypotheses, they are nothing but mere
opinions. Evidence-based medicine is no exception – and a range
of statistical methods is available to evaluate these drugs
Adrian Olszewski
at KCR
SAS and R Team
in Clinical Research
Image:©Edelweiss–Fotolia.com

2. www.samedanltd.com l 19
especially for medium- and large-
sized teams of analysts in advanced,
corporate settings.
The software must be bought, and certain
functionalities are grouped and packed
in modules, which can be purchased
separately. Buying the license also grants
access to a professional helpdesk.
GNU R
Developed by the R Core Team and supported
by the R Consortium, GNU R (R) is one of
the most popular and best recognisable
computational environments. It is a direct
successor of the S programming language,
founded in 1976 at Bell Laboratories. In 1998,
S became the first statistical system that
received the top award from the Association
for Computing Machinery.
Today, R can be found in almost every area
of science, such as medicine, pharmacy,
genetics, epidemiology, banking, social
media, data mining and machine learning.
In particular, its capabilities in clinical research
and genomics are remarkable. Many of the
top companies – including pharmaceutical
ones – not only use R, but also contribute by
supplying specialised packages (1,2).
Likewise, R is also popular at universities
and in research departments, where new
algorithms are invented – for example,
at the CERN, NASA and the National Institute
of Standards and Technology.
R is well-known for its flexibility, full-featured
language, strong graphical and reporting
capabilities, and ability to access data in
multiple formats as well as its capacity of
containing a huge number of statistical
methods, stored in nearly 9,000 additional
packages (3). It is a free, general public licensed
software; however, companies like Microsoft,
Oracle and RStudio offer commercial, tweaked
versions as well.The support provided by the
broad and vibrant community of both users
and institutions is comprehensive.
R in Controlled Trials
It is sometimes claimed that R is not
validated and, as a result, cannot be used
in controlled trials and environments.
This requires a deeper explanation but, in
brief, it is a common misunderstanding.
In fact, every CRO develops processes
regarding widely understood verification of
both the software and written programmes.
Thus, validation is a constant part of creating
programmes, and should be carried out
regardless of any assurances made by
the software vendor.
Since the relevant guidelines released by the
FDA, ICH and the R Foundation for Statistical
Computing – as well as the entire source
code of R and all its packages – are publicly
available, the validation is easy to achieve
(4-7). Every R package has its author or
maintainer, who must follow rules in order to
publish the package into the Comprehensive
R Archive Network.
There is also an informal though significant
argument: R is created by professional
statisticians and used by over two million
individuals (8,9). As the code is open,
anyone can verify it. Archival messages in
mailing groups, forums and GitHub prove
both – that new procedures are constantly
checked and improved, while the older ones
are clean and stable. It is also important to
note that a local, version-frozen repository
of packages can be set up in order to ensure
reproducibility of results, regardless of
possible changes in the code (10).
Last but not least, R has been identified by
the FDA as suitable for both interpreting
data from clinical trials, as well as for
making submissions (11).
Why Combine SAS and R?
Every piece of software has its strengths and
weaknesses, and so does SAS. There are tasks
that can be completed easier or cheaper by
employing external programmes –
Figure 1
Required algorithm or functionality
Bidirectional data exchange
SAS IML module
Or different
method of
communication
SAS module 1
SAS base
Missing or
expensive
functionality
SAS module 2
1
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3. 20 l www.samedanltd.com
in other words, searching for process and
cost optimisations is nothing unusual.
Most of the top analytical software
currently available on the market offer a
way to communicate with R, and SAS is no
exception (12). Since 2009, SAS owns the
Interactive Matrix Language (IML) module,
which enables bidirectional communication
between both packages (13) – but this is
not the only way to establish such
a connection (14-16).
The following are some exemplary
scenarios where the cooperation between
SAS and R seems worthwhile, which
enhances the present toolkit and
significantly reduces costs.
Scenario 1: Accessing Data
SAS provides a wide set of modules for
data access, commonly named‘SAS/
ACCESS’. The modules perform very well,
although they must be purchased. They
are worth considering when dealing with
huge data volumes, but in clinical research,
it is common to process relatively small
datasets. It seems reasonable to have a
closer look at the data access capabilities
offered by R.
In R, data from SAS (native and transport
formats), as well as from many other
statistical packages, can be easily
obtained. Most relational databases are
supported directly or through the JDBC/
ODBC interface; Microsoft (MS) Office
spreadsheets may be read and modified
directly, and web services are consumed via
JSON data format. Full support of XML data
format is available, enabling information
exchange with electronic data capture
systems. With only a few lines of code,
data can be fetched from a given source,
processed locally or sent to SAS. Data can
be exported from SAS in the same way.
Scenario 2: Exporting Results
SAS owns an advanced Output Delivery
System (ODS), which is able to produce
professional-looking, rich text format (RTF)
documents. The RTF is acceptable and offers
a wealth of formatting options, but there
may be a requirement to prepare a native
MS Word document instead. This option is
currently missing in ODS. SAS, on the other
hand, provides an add-in for MS Office – but
this is a part of a separate module, the SAS
Platform for Business Analytics.
This is the place where R can be used due
to its advanced reporting capabilities.
ReporteRs is a package deserving special
attention, as it allows the user to produce
native MS Word and MS PowerPoint
documents. The package supports the
creation of tables of any complexity – like
clinical tables, for instance. Most aspects of
generated objects can be controlled: size,
font, colours, thickness and type of lines,
borders and shadows, to name a few.
Plots can be transformed into vector
graphics and become editable and scalable.
Produced this way, graphics may be resized
to any degree. Existing documents can
be turned into templates with additional
placeholders for tables and graphics.
It facilitates the creation of documents
conforming to corporate standards.
Scenario 3: Missing Methods
Although SAS is full of advanced statistical
methods, it is not unlikely to find one
missing – such as confidence intervals for
the difference of two proportions. It can
be programmed in SAS, but R owns the
ExactCIdiff package that implements this
method (17). The code calling required
functions will be just another statement
in an existing SAS programme.
The gsDesign package, which helps to
derive group sequential designs and
describe their advanced properties, makes
another example of a specialised R module
called from SAS (2). Another example is the
use of R as a connector between SAS and
the ADMB package applied for automatic
| Biostatistics
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4. www.samedanltd.com l 21
differentiation, used for advanced,
nonlinear statistical modelling (18).
Scenario 4: Validation
Regardless of the fact that there is no
requirement for validation of critical parts
of statistical programmes to be done in a
different statistical package, our experience
show that following this route may leverage
quality of the validation. Changing the
way of thinking, forced by using a different
programming language, may alter the
perspective and reception of validation
instructions, which helps to detect
and resolve issues.
Scenario 5: Advanced Graphing
There is an advanced graphing subsystem
in SAS called SAS/GRAPH, which can
produce high-quality plots. Opinions
vary, but some programmers describe
the process as slightly complex. In such a
situation, it is worth noting that R is capable
of producing advanced and professional-
looking graphs by using the famous
ggplot2 package, an implementation
of‘Grammar of Graphics’. There are also
other graphing subsystems available.
Scenario 6: Exposing Results
R may help to expose results of analyses
done in SAS in a network. There are three
packages that are able to simplify the
process: the first is knitr, a general-purpose
package for dynamic report generation
by following the reproducible research
paradigm. Reports are created by mixing
formatted content with chunks of R, SAS
and SQL codes. When processed, results
replace the commands or are coalesced.
Meanwhile, the OpenCPU and Shiny
packages help to constitute a full featured
web server that is able to host dynamic web
applications and reports.
Scenario 7: R-Based Tools
As a lightweight and fully portable
software, where installation is not required
and which works on various operating
systems and architectures (including
ARM-based minicomputers), R is a good
candidate for a framework used to create
advanced statistical solutions, such as:
• Automated processes searching
a database for potential frauds
• Local, handy windows-based
analytical tools
• Web-enabled reporting systems
and dashboards
Key Considerations
SAS and R are two different worlds, so
connecting them may result in issues
significantly affecting the results of the
analysis. Some fundamental discrepancies are:
• Origin of dates
• Representations of floating point numbers
• Used sum of squares
• Default contrasts
• Calculation of quantiles
• Generation of random numbers
• Implementation of advanced models
All of these must be taken into account
when integrating both systems or
validating the result of analyses.
As a summary, it can be said that the
integration of SAS with R packages, when
done properly, may bring noticeable benefits
in terms of enhanced functionality and
reduced costs. The scenarios shown above
do not exhaust the list, which is limited
mostly by one’s experience and invention.
References
1. Visit: http://blog.revolutionanalytics.
com/2014/05/companies-using-r-in-2014.html
2. Visit: www.cioreview.com/news/gsdesign-
explorer-to-optimize-merck-s-clinical-trial-
process-nid-1305-cid-36.html
3. Visit: www.r-clinical-research.com
4. Visit: www.fda.gov/ohrms/dockets/98fr/04d-
0440-gdl0002.pdf
5. Visit: www.fda.gov/ohrms/dockets/
98fr/5667fnl.pdf
6. Visit: www.fda.gov/regulatoryinformation/
guidances/ucm085281.htm
7. Visit: www.r-project.org/doc/R-FDA.pdf
8. Visit: www.r-project.org/foundation/board.html
9. Visit: www.oracle.com/technetwork/database/
options/advanced-analytics/r-enterprise/
bringing-r-to-the-enterprise-1956618.pdf
10. Visit: https://mran.microsoft.com/documents/
rro/reproducibility
11. Visit: http://blog.revolutionanalytics.
com/2012/06/fda-r-ok.html
12. Visit: https://support.sas.com/rnd/app/studio/
Rinterface2.html
13. Visit: http://support.sas.com/documentation/
cdl/en/imlug/63541/HTML/default/viewer.
htm#r_toc.htm
14. Visit: www.jstatsoft.org/article/view/
v046c02/v46c02.pdf
15. Visit: www.lexjansen.com/nesug/nesug12/
bb/bb10.pdf
16. Visit: www.phuse.eu/download.
aspx?type=cmsdocid=2847
17. Visit: https://journal.r-project.org/
archive/2013-2/wang-shan.pdf
18. Visit: www.admb-project.org
Adrian Olszewski is a
Biostatistician in the
Biometrics and Clinical Trial
Data Execution Systems
Department at KCR. He is
responsible for providing
comprehensive support for trials from early
design considerations, through the data
analysis – including interim evaluations – to
the final report. Adrian holds an MSc degree
in Computer Science.
Email: info@kcrcro.com
As a lightweight and fully portable
software, where installation is not required and
which works on various operating systems
and architectures, R is a good candidate for
a framework used to create advanced
statistical solutions