Quanticate CRO Blog

The imminent application of Clinical Trial Regulation (EU) No. 536/2014, estimated to happen in 2020, will peak an emergent era of transparency in clinical development. The regulation applies to all interventional clinical trials (Phase 1 to 4) performed in the European Union (EU)/European Economic Area (EEA), plus additionally specified paediatric trials, and covers documents at the single clinical trial level irrespective of the drug’s marketing approval status. The regulation requires the disclosure of the study Protocol, Investigational Medicinal Product Dossier (IMPD; Sections S and E), Investigator’s Brochure (IB), Subject Information Sheet, Clinical Study Report (CSR; redacted) and a Plain Language Summary (PLS; also known as a Laypersons Summary) for each clinical trial within 12 months of the end of the trial (6 months for paediatric trials). Disclosure is readily welcomed by study participants who want to know what has been learned from their participation and is in accordance with the European Medicines Agency’s (EMA) goal of protecting and fostering public health by allowing better‑informed use of medicines. We will be able to learn from past successes as well as failures now that any biased under‑reporting of results will be stopped.

As the novel coronavirus pandemic sweeps across the globe, the media coverage of the COVID-19 disease is vast. There are articles being published continuously on all types of websites, and by journalists, across multiple disciplines. The aim of this blog is to pull together a list of trusted, valuable, statistically sound, and scientifically robust resources on COVID-19 for data scientists, statisticians, clinical programmers, medical writers and other individuals involved in drug development within the pharmaceutical and biotechnology industry or beyond.

With constantly evolving industry regulations, the increasing pressures to reduce the costs of drug development, and raising demand for new therapies, trends in clinical trial outsourcing strategies emerge.

Integrations of studies are important for setting up safety and efficacy profiles of the component of interest and are referred to as ISS and ISE. Without the integration of multiple studies some larger trends in the data may not be so easily visible, regardless of whether those are good or bad, so these integrations are vital to ensuring the safety and effectiveness of any intervention.

Substantial quantities of code can be difficult to navigate, debug and manage if poorly planned and laid out. Therefore, when starting a program anew or taking over previously established code it is imperative that extra effort is made to make the code straightforward and easy to understand for not only yourself but anyone who may inherit the code from you in the future. This blog will explore techniques and best practices to achieve this in your SAS® programs, whether starting with existing code or from scratch. Focus will be on larger SAS programs and how they can be accessible and reusable to developers of any level, including and going beyond the traditional standard good programming practices to delve into more advanced techniques and ideas for good program management. Techniques include using SAS functions and procedures to provide summaries and critical information for navigation and debugging of code.

What are Adaptive Clinical Trials?

There have been various definitions of adaptive clinical trial designs over the last 10 years but the one below, coming from the FDA draft guidance on the topic, covers it pretty well:

 “A clinical trial design that allows for prospectively planned modifications to one or more aspects of the design based on accumulating data from subjects in the trial.” ^[1]

In simple terms, it means that we anticipate that some features of the study (e.g. sample size, treatment arms, population of interest, etc) might change during the course of the study (i.e. after randomization), according to a pre-specified set of rules. Notably, the key term in the above definition is ‘prospectively planned’: not all planned modifications are legitimate and safeguard trial integrity and interpretability, however unplanned changes will never be considered acceptable and will result in the study results being discarded as not valid.

Welcome to our new FiresideSTATS Podcast Episode 1: An Introduction to Estimands. Today we are joined by our Statistical experts, Laura and Sonia, who answer our questions about Estimands and the ICH E9 (R1) addendum guidelines. 

In Rheumatoid Arthritis (RA) clinical trials, treatment response is often assessed via the American College of Rhematology (ACR) Criteria. This is a standard criterion to measure the effectiveness of various medications. ACR response is used to discriminate proven effective treatments from placebo treatments in a clinical trial setting. The ACR response criteria is indicated as ACR 20, ACR 50 or ACR 70.  

The primary focus of any Phase I oncology trial is to find the Recommended Phase II Dose (RP2D), by ascertaining the maximum tolerated dose (MTD), the maximal dose with the dose limiting toxicities (DLT) not exceeding a pre-set limit. However, before proceeding to Phase II we would want to confirm that the RP2D is appropriate, there is a suitable population to use in the Phase II study, that the dose is efficacious and if there could be lower, less toxic doses with good efficacy ^[1] – this is where the Phase I Expansion Study comes in. Other areas of interest could include pharmacokinetics, pharmacodynamics, toxicities and other safety endpoints. Once a dose (or multiple doses) of interest have been found, additional cohorts potentially with a large sample size and/or stratified by prognostic factors to help determine Phase 2 populations can be added ^[2].