Quanticate CRO Blog

R is the open source software environment and language used for data analysis and statistical computing. A great deal has already been said and written about R’s wide variety of graphical and statistical techniques. Even though R as a programming language is constantly growing in popularity in the pharmaceutical industry, it is still quite unpopular to use R in the preliminary stages of research like importing data from different sources, tidying it, calculating new variables in datasets and making other amendments available in SAS data steps.

This blog explores the ways R can come in useful as a tool for programming datasets and compares both tools in terms of performance and ease of use. It will also focus on the reliability of packages (from CRAN repository, Bioconductor and other sources) that one can use when creating and modifying datasets in R.

Patient safety has been the primary focus of clinical trial design since 1947 when the Nuremberg code outlined the ethical guidelines for clinical research. Trials must be designed to avoid injury or suffering and patients must give consent and are free to leave the trial at any point. The current world of drug development governed by ICH-GCP makes it mandatory to have all necessary steps taken by sponsors, CROs and investigators to keep patient safety as utmost priority.

The use of R programming in clinical trials has not been the most popular and obvious, despite its recent growth over the past few years, its practical use still seems to be hindered by several factors, sometimes due to misunderstandings, (e.g validation) but also because of a lack of knowledge of its capabilities. Despite these bottlenecks, though, R is doubtlessly creating its own (larger by the day) niche in the pharmaceutical industry.

In this blog we will see how R can be used to create TLFs much like the current combination of PROC REPORT/PROC TABULATE and the ODS currently does, thus showing its power and capability to play an important role in our industry in the years to come, not as a replacement for, but rather as an alternative option to SAS®.

Until 2007, the Food Drug & Administration (FDA) and the biopharmaceutical industry were grappled by the non-standardized sources of data. Gathering all the heterogenous data and mapping to the internal standards of the pharma companies proved to be the greatest challenge of the time. By the end of 2007, and towards the beginning of 2008, the Clinical Data Interchange Standards Consortium (CDISC) gained ground in its mission to develop a global set of standards. Eventually, CDISC began shaping the entire industry as FDA started requesting submission data in standardized formats, though CDISC compliant software options were limited.^1,2 The ‘study data standards’ provide a consistent general framework for organizing data, including templates for datasets, standard names for variables, identify appropriate controlled terminology and standard ways of doing calculations with common variables.³

The shift from paper to Electronic Data Capture (EDC) in the clinical trial world saw a shift in the way we look at the quality measurements of clinical data management (CDM) activities. The paper world had a clear understanding that the quality of the clinical data collected was simply the quality of the transcription work teams performed of transferring data from paper to a database. The Quality Control (QC) of paper versus database had a set standard for sampling of √N+1 or 20 subjects, whichever was smaller and a 100% QC of critical variables. Acceptable error rates were 0.5% which was widely agreed across the industry. These thresholds were no longer necessary when EDC enabled sites to enter the data directly and transcription was no longer needed. However, it is the role of the data management teams to be involved in many efforts to prepare data for appropriate analysis and submissions.

Welcome to our FiresideSTATS Podcast Episode 2. Today we are joined by our Statistical experts, Niccolo and Emily.

In 1992, the Food and Drug Administration (FDA) introduced four distinct and successful approaches known as Priority Review, Breakthrough Therapy, Accelerated Approval, and Fast Track approval of the investigational drug. Under the ‘accelerated approval’ regulation, surrogate endpoints for serious conditions that filled an unmet medical need are reviewed by FDA, allowing faster approval of drugs. A two-tiered system of review times – Standard Review and Priority Review were introduced by FDA under the Prescription Drug User Act (PDUFA). FDA reviews ‘Priority Review’ designated drugs within 6 months (compared to 10 months under standard review).^[i]

The leading cause of death in patients with coronavirus disease 2019 (COVID-19), caused by the virus ‘severe acute respiratory syndrome coronavirus 2’ (SARS‑CoV‑2), is respiratory failure from acute respiratory distress syndrome.^[1] Patients who required invasive mechanical ventilation had an 88% mortality rate in one study in New York, and a 53% mortality rate in one study in the UK.^[2][3] While an alarming proportion of patients with respiratory failure are dying, for the those who recover there is an urgent need to consider, and subsequently manage,  their longer-term care.^[4] Patients who have survived the COVID‑19 infection continue to experience feelings of fatigue, shortness of breath and reduced exercise tolerance. There is currently limited information regarding the best route to full recovery for post-coronavirus infection patients. Care will likely be multidisciplinary in nature and include a respiratory review along with physiotherapy, nutritional advice, psychiatric support, and potentially other disciplinary involvement.