Quanticate CRO Blog

Clinical Trial Design

The most common design used for Phase II and Phase III clinical trials is the randomized controlled trial design. In randomized controlled trials, the probability of being assigned the study treatment or the control treatment is fixed throughout the trial and is normally 50%, so that each treatment is given to the same number of patients. This approach leads to a high‑chance of identifying if one treatment is significantly better than the other. The fact that 50% of the trial population is assigned to the control treatment is not a particular issue in common diseases such as cardiovascular disease. For example, of the 7 million people in the United Kingdom (UK) with cardiovascular disease, if 200 of them are involved a clinical trial, then 99.9% of the overall cardiovascular disease population will benefit from the results of the clinical trial^[1].

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.