·
Medical writing
services include medicinal and regulatory Writing, scientific correspondence,
materials for instruction and medical
writing consulting services.
Introduction:
Despite 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.
Current Trends of R in Pharma:
Looking at current market
trends, R utilization at this juncture is less than 10% in activities related
to Medical
Writing Companies and Pharma Regulatory Submissions. 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.
Reasons why 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 tremendous community support. Availability of source code
offers superior & detailed documentation.
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), SASxport (Read and Write
'SAS' 'XPORT' Files), ADCT (Adaptive
Design in Clinical Trials), ClinPK, cpk (Clinical
Pharmacokinetics Toolkit), randomizeR (Randomization
for Clinical Trials), Base R (lot of
functionality useful for design and analysis of clinical trials), Greport (Graphical Reporting for Clinical
Trials), Coronavirus (Provides a daily
summary of the Coronavirus (COVID-19) cases by state/province) etc.
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). 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).
Conclusion:
In pharmaceutical firms, medical
writing agencies, and contract research organizations (CROs), R's
acceptance as the program of choice is
something that many doubted to see over their lifetime. Still, things are
progressing quickly, even in the pharmaceutical industry.
Reference : https://pubrica.com/services/physician-writing-services/
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