the benefits of r programming in clinical trial data analysis – pubrica

An Academic presentation byDr. Nancy Agnes, Head, Technical Operations, Pubrica Group: www.pubrica.comEmail: sales@pubrica.com

THE BENEFITS OFR PROGRAMMING IN CLINICAL TRIAL DATA ANALYSIS

Outline

Today's Discussion

In-Brief IntroductionBenefits of R Programming in Clinical Trial Data Analysis Current Trends of R in PharmaReasons why R can be a Potentially Powerful Tool for Data Analysis R Packages for Clinical Trial Design, Monitoring, and AnalysisR Implementation in Pharma – Real-Time Examples Conclusion

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In-Brief

IntroductionDespite its recent development over the past several years, the use of R programming in medical writing solutions has not been the most widespread and apparent, its realistic use still seems to be impeded by multiple variables, often due to misunderstandings (e.g. validation) but also due to a lack of knowledge of its capabilities.

However, R is unquestionably building its own niche in the pharmaceutical industry (larger by the day) among these bottlenecks.

Benefits of R Programming in Clinical Trial Data Analysis

In recent years, data science has fueled powerful business decisions taken by industry leaders.

Data scientists are tellers of stories.

They often need to dig into data, clean, transform, create & validate models, understand patterns, generate insights and, most importantly, effectively communicate results in regulatory writing services.

Contd...

In addition to SAS, the most frequently spoken languages in statistics, analyticsand visualization are R and Python.

This article highlights R challenges observed, suggested approaches for risk assessment of R packages, Clinical Trial Data Analysis mitigation & implementation.

Current Trends of R in Pharma

Looking at current market trends, R utilization at thisjuncture is less than 10% in activities related to Medical Writing Companies and Pharma RegulatorySubmissions.

R is, however, commonly used in programs in public health, healthcare economics, and exploratory/scientific research, detection of patterns, Plots/Graphs generation, basic Stat analysis and machine learning.

For CDISC (SDTM, ADaM) datasets creation, R is not commonly used.

Contd...

"One of the programming community's common questions is, "Will we replace SAS with R or use both or other languages (Python)?".

Instead of deciding between SAS or R or Python, I believe that one can make most of these programming languages to solve acceptable data science issues (one size does not fit all).

Reasonswhy R can be a Potentially Powerful Tool for Data Analysis

R is a statistical computing and graphics language and environment. Under the terms of the GNU General Public License of the Free Software Foundation in source code form, it is available as Free Software.

As an open-source program, R enjoys tremendouscommunity support.

Availability of source code offers superior & detailed documentation.

Contd...

R compiles and operates on a wide range of UNIX, Windows and macOSarchitectures and related systems (including FreeBSD and Linux).

R is strongly extensible and offers a broad range of mathematical (linear and nonlinear simulation, classical statistical experiments, study of time series, grouping, clustering) and graphical techniques.

The ease, with which well-designed publication-quality plots can be generated, including mathematical symbols and formulae where appropriate, is one of R's strengths.

Contd...

R Packages for Clinical Trial Design, Monitoring, and Analysis

R has many packages for medical writing Clinical Trial data analysis.

Following are few examples: A table (Create Tables for Reporting Clinical Trials), compare OEM (Comparison of medical forms in CDISC ODM format), CRTSize (Sample size estimation in a cluster (group) randomized trials), Blockrand (creates randomizations for block random clinical trials), DoseFinding (Supports design & analysis of dose-finding experiments), Pact (Predictive Analysis of Clinical Trials), etc.

R Implementation in Pharma – Real- Time Examples

Although SAS was the primary tool at Amgen, R was regarded because of the lack of SAS graph macros (ggplot).

As the SAS Grid & R environment was housed at Amgen on various physical servers,integration was required and Microsoft DeployR was therefore selected.

DeployR is a technology for integrating into web, desktop, tablet, and dashboard systems for delivering R analytics.

Contd...

AMGEN INTEGRATES SAS & R USING MICROSOFT DEPLOYR:

SAS Procedure PROC Groovy allows Groovy code to be run on Java Virtual Machine via SAS Code (JVM). PROC GROOVY is used in this approach to invoke the Java code that is called DeployR Java Client Library.

CHALLENGES & VALIDATION OF R:

R is free but it's an investment.

The main challenge of using R is ensuring validation documentation.

R needs to be programmed (How do we develop software for Clinical science – that enables collaboration across the enterprise and the industry).

Contd...

R has too many Packages (Which packages are validated?).

R Packages may come from anywhere & be written by anyone or may not follow a typical SDLC (Software Development Life Cycle).

ConclusionThe roles and responsibilities of Statistical Programming Services are overlapping.

All disciplines have distinct focuses, however.

Clinical research, systematic reviews, and the working climate are complex and multidisciplinary in biostatistical activities.

Therefore, biostatistics Support Service is essential for fruitful, efficient, and high-quality collaborations to clearly define the responsibilities.

For which the ICH E6 guidance similarly formulates the tasks by concerning good clinical practice.

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