Venkatesan
Balu, Associate Director, Global Data Sciences, Navitas Life Sciences
The use of R programming in clinical trials has
not been the most popular and obvious. Despite experiencing significant growth
in recent years, the adoption of R programming in clinical trials is not as
widespread and evident as anticipated. Practical implementation faces obstacles
due to various factors, including occasional misunderstandings, particularly in
the context of validation, and a notable lack of awareness regarding its
capabilities. However, despite these challenges, R is steadily establishing a
growing niche within the pharmaceutical industry.
Opportunities for R Programming in
Clinical Trials
Although R is versatile and applicable in various
settings, it is commonly associated with scientific computing and statistics.
In the context of clinical trials, where researchers aim to understand and
enhance drug development and testing processes, R has become a prominent tool
for analyzing the collected data. While SAS has been a longstanding programming
language for clinical trials, its high cost prompts the industry to explore
better alternatives. Therefore, there is a quest for sustainable technology and
tools that can effectively address industry challenges.
To drive innovation, there is a need to move away
from traditional, inefficient processes and tools toward solutions that are
efficient, simple, easy to implement, reliable, and cost-effective.
Collaboration among industry stakeholders is crucial to develop a robust
technology ecosystem and establish consensus on validation and regulatory
benchmarks. Equally vital is preparing the workforce with the necessary
skillsets to meet future demands.
Current Usage Trends of R
Analyzing the current trends of R in the
pharmaceutical industry reveals that its usage is currently below par in
activities related to Pharma Regulatory Submissions. However, R finds extensive
use in public health projects, healthcare economics, exploratory and scientific
analysis, trend identification, generating plots/graphs, specific statistical
analysis, and machine learning. R continues to advance steadily in clinical
trials, however lacks widespread usage within the clinical space. The notable
difference between SAS and R is that SAS is proprietary software, whereas R is
an open-source programming language.
SAS or R Programming- Which is
Better?
The ongoing
debate in the programming community revolves around whether to replace SAS with
R, use both, or explore
other alternatives like Python. Instead of adopting an either-or scenario,
leveraging the strengths of each programming language for specific Data Science
problems is recommended, recognizing that one size does not fit all. Early adopters
of R have faced challenges, with regulatory compliance for R packages being a
common issue. For R to be considered for tasks related to regulatory submission,
a rigorous risk assessment of R packages, feasibility analysis, and the
establishment of processes for R usage through pilot projects with necessary
documentation become imperative.
Benefits of Using R Programming
R, as a language and environment for statistical
computing and graphics, possesses characteristics that make it a potentially
powerful tool for Data Analysis. With approximately 2 million users worldwide
and three decades of legacy, R stands out as open-source software receiving
substantial support from the community. Its availability under the GNU General
Public License and extensive documentation contribute to its strength. R is
versatile, running on various platforms, offering a wide array of statistical
and graphical techniques, and its ease of producing publication-quality plots
enhances its appeal.
The pharmaceutical industry has witnessed the
emergence of various R packages tailored for Clinical Trial Design, Monitoring,
and Analysis. Examples include Atable for creating tables for reporting
clinical trials, compareODM for comparing medical forms in CDISC ODM format,
CRTSize for sample size estimation in cluster randomized trials, and others.
These packages cater to different aspects of clinical trial data analysis,
showcasing the versatility of R in this domain.
This article talks more about use of r in
clinical trials and how this will be used by taking advantages of open source
of R. The FDA emphasizes the need for
fully documenting software packages used for statistical analysis in
submissions. The use of R poses specific challenges related to validation,
given its free and open-source nature. To address this, the R Foundation has released
guidance documents focusing on regulatory compliance, validation issues, and
the software development life cycle. However, this will not have problems in
implementing validation of the deliverables or any integration with the tools for
the statistical analysis.
Implementing Dual Programming
As a procedural measure, we can implement dual
programming, where primary programmers focus on the SAS system for deliverables
while validators utilize an alternative program, such as R programming. As part
of demographic table validation, we generated the table using both SAS and R
programming.
Table 01
--
Benefits of Using R Programming
Given that the cost of the R-package is
non-chargeable, it can also serve as a potential tool for API integration. For
instance, in signal detection, R packages can prove to be valuable tools due to
the intricate derivation process for EBGM in the Bayesian approach, which aims
to mitigate false positive signals resulting from multiple comparisons. The
computation adjusts the observed-to-expected reporting ratio for temporal
trends and confounding variables such as age and sex. While both methods can
estimate this, the accessibility of R as free software enables easy integration
into any system as an API or for macro estimation purposes without any copyrights
issue.
Identifying the Limitations in Using
R- Programming
It is crucial to note that software cost is
essential to any company, including pharma and biotech ones. While R and
RStudio® are free and SAS® requires an annual license, using R instead of SAS
may not always lower costs. The cost of software is only one part of the
equation. To be used in a highly regulated industry such as pharmaceuticals,
software validation, maintenance, and support are critical, and their costs
need to be considered. Although R is free and open source, it comes with a
steep learning curve, lacks direct support from the company, and faces a
shortage of R programmers compared to those familiar with SAS®.
Leveraging the Right Tools to
Capture Value
Capturing the value of R programming starts with
a clear vision for its use and a systematic approach to identifying and
prioritizing the needs in the industry. Clinical Data Science is evolving
rapidly, and the industry actively seeks alternative solutions to unlock
valuable insights from diverse datasets. Recognizing the need for innovation,
collaboration, and efficient tools is crucial. Rather than viewing SAS, R, and
Python as mutually exclusive, leveraging the strengths of each for appropriate
Data Science problems provides a nuanced and effective approach.
Ensuring data quality, scientific integrity, and
regulatory compliance through risk assessment frameworks, validation, and
documentation are imperative in this dynamic landscape. The pharmaceutical
industry's journey toward embracing R reflects the broader trend of industries
recognizing the value and potential of open-source tools in addressing complex
challenges.
Venkatesan
Balu is the Associate Director, Global Data
Sciences, Navitas Life Sciences with 15+ years of experience in the
Biostatistics domain, and in Phase I to Phase IV Clinical Trials across various
therapeutic areas, BABE and PK studies. He has invaluable expertise in
providing inputs to study design, sample size, SAP, outlier evaluation, interim
analysis, complex statistical evaluation & model selection, and regulatory
requirement. He is a technical leader in drug development strategy, adaptive
design, portfolio optimization, and decision-making in clinical trials.
Blog Post
<html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns:m="http://schemas.microsoft.com/office/2004/12/omml" xmlns="http://www.w3.org/TR/REC-html40">
De-Mystifying R Programming in Clinical Trials
Venkatesan Balu, Associate Director, Global Data Sciences, Navitas Life Sciences
The use of R programming in clinical trials has not been the most popular and obvious. Despite experiencing significant growth in recent years, the adoption of R programming in clinical trials is not as widespread and evident as anticipated. Practical implementation faces obstacles due to various factors, including occasional misunderstandings, particularly in the context of validation, and a notable lack of awareness regarding its capabilities. However, despite these challenges, R is steadily establishing a growing niche within the pharmaceutical industry.
Opportunities for R Programming in Clinical Trials
Although R is versatile and applicable in various settings, it is commonly associated with scientific computing and statistics. In the context of clinical trials, where researchers aim to understand and enhance drug development and testing processes, R has become a prominent tool for analyzing the collected data. While SAS has been a longstanding programming language for clinical trials, its high cost prompts the industry to explore better alternatives. Therefore, there is a quest for sustainable technology and tools that can effectively address industry challenges.
To drive innovation, there is a need to move away from traditional, inefficient processes and tools toward solutions that are efficient, simple, easy to implement, reliable, and cost-effective. Collaboration among industry stakeholders is crucial to develop a robust technology ecosystem and establish consensus on validation and regulatory benchmarks. Equally vital is preparing the workforce with the necessary skillsets to meet future demands.
Current Usage Trends of R
Analyzing the current trends of R in the pharmaceutical industry reveals that its usage is currently below par in activities related to Pharma Regulatory Submissions. However, R finds extensive use in public health projects, healthcare economics, exploratory and scientific analysis, trend identification, generating plots/graphs, specific statistical analysis, and machine learning. R continues to advance steadily in clinical trials, however lacks widespread usage within the clinical space. The notable difference between SAS and R is that SAS is proprietary software, whereas R is an open-source programming language.
SAS or R Programming- Which is Better?
The ongoing debate in the programming community revolves around whether to replace SAS with R, use both, or explore other alternatives like Python. Instead of adopting an either-or scenario, leveraging the strengths of each programming language for specific Data Science problems is recommended, recognizing that one size does not fit all. Early adopters of R have faced challenges, with regulatory compliance for R packages being a common issue. For R to be considered for tasks related to regulatory submission, a rigorous risk assessment of R packages, feasibility analysis, and the establishment of processes for R usage through pilot projects with necessary documentation become imperative.
Benefits of Using R Programming
R, as a language and environment for statistical computing and graphics, possesses characteristics that make it a potentially powerful tool for Data Analysis. With approximately 2 million users worldwide and three decades of legacy, R stands out as open-source software receiving substantial support from the community. Its availability under the GNU General Public License and extensive documentation contribute to its strength. R is versatile, running on various platforms, offering a wide array of statistical and graphical techniques, and its ease of producing publication-quality plots enhances its appeal.
The pharmaceutical industry has witnessed the emergence of various R packages tailored for Clinical Trial Design, Monitoring, and Analysis. Examples include Atable for creating tables for reporting clinical trials, compareODM for comparing medical forms in CDISC ODM format, CRTSize for sample size estimation in cluster randomized trials, and others. These packages cater to different aspects of clinical trial data analysis, showcasing the versatility of R in this domain.
This article talks more about use of r in clinical trials and how this will be used by taking advantages of open source of R. The FDA emphasizes the need for fully documenting software packages used for statistical analysis in submissions. The use of R poses specific challenges related to validation, given its free and open-source nature. To address this, the R Foundation has released guidance documents focusing on regulatory compliance, validation issues, and the software development life cycle. However, this will not have problems in implementing validation of the deliverables or any integration with the tools for the statistical analysis.
Implementing Dual Programming
As a procedural measure, we can implement dual programming, where primary programmers focus on the SAS system for deliverables while validators utilize an alternative program, such as R programming. As part of demographic table validation, we generated the table using both SAS and R programming.
Table 01 --Benefits of Using R Programming
Given that the cost of the R-package is non-chargeable, it can also serve as a potential tool for API integration. For instance, in signal detection, R packages can prove to be valuable tools due to the intricate derivation process for EBGM in the Bayesian approach, which aims to mitigate false positive signals resulting from multiple comparisons. The computation adjusts the observed-to-expected reporting ratio for temporal trends and confounding variables such as age and sex. While both methods can estimate this, the accessibility of R as free software enables easy integration into any system as an API or for macro estimation purposes without any copyrights issue.
Identifying the Limitations in Using R- Programming
It is crucial to note that software cost is essential to any company, including pharma and biotech ones. While R and RStudio® are free and SAS® requires an annual license, using R instead of SAS may not always lower costs. The cost of software is only one part of the equation. To be used in a highly regulated industry such as pharmaceuticals, software validation, maintenance, and support are critical, and their costs need to be considered. Although R is free and open source, it comes with a steep learning curve, lacks direct support from the company, and faces a shortage of R programmers compared to those familiar with SAS®.
Leveraging the Right Tools to Capture Value
Capturing the value of R programming starts with a clear vision for its use and a systematic approach to identifying and prioritizing the needs in the industry. Clinical Data Science is evolving rapidly, and the industry actively seeks alternative solutions to unlock valuable insights from diverse datasets. Recognizing the need for innovation, collaboration, and efficient tools is crucial. Rather than viewing SAS, R, and Python as mutually exclusive, leveraging the strengths of each for appropriate Data Science problems provides a nuanced and effective approach.
Ensuring data quality, scientific integrity, and regulatory compliance through risk assessment frameworks, validation, and documentation are imperative in this dynamic landscape. The pharmaceutical industry's journey toward embracing R reflects the broader trend of industries recognizing the value and potential of open-source tools in addressing complex challenges.
Venkatesan Balu is the Associate Director, Global Data Sciences, Navitas Life Sciences with 15+ years of experience in the Biostatistics domain, and in Phase I to Phase IV Clinical Trials across various therapeutic areas, BABE and PK studies. He has invaluable expertise in providing inputs to study design, sample size, SAP, outlier evaluation, interim analysis, complex statistical evaluation & model selection, and regulatory requirement. He is a technical leader in drug development strategy, adaptive design, portfolio optimization, and decision-making in clinical trials.