Quanticate CRO Blog

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various definitions of adaptive clinical trial designs over the last 10 years but the one below, coming from the FDA draft guidance on the topic, covers it pretty well: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

Over the last few years, Estimands have become an increasingly hot topic for statisticians within our industry. In August 2017, the first draft of The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) E9 (R1) addendum was released for consultation. The addendum was entitled ʽEstimands and sensitivity analysis in clinical trialsʼ and is expected to be finalised in the coming months. Pharmaceutical companies, regulatory agencies and clinical research organisation (CROs) are working in implementing this addendum into their working practices. It is a time of learning and change for all.

The approach to missing data in clinical trials has evolved over the past twenty years, particularly regarding the view to incorporate missing data in our understanding of results. The problem of missing data is of particular importance due to it introducing bias and leading to a loss of power, inefficiencies and false positive findings (Type I Error). It is often the last visit at which clinical benefit is measured and an incomplete picture of the safety and efficacy profile is painted if subjects drop out prior to this visit, leading to inaccurate conclusions for the investigative treatment.

More experienced programmers – especially if they are fluent in multiple programming languages – will face the dilemma of choosing the best method to achieve a given goal. This blog discusses automated delivery of company emails. There are many situations which require sending tens or hundreds of messages to individuals and people tend to use creative methods to avoid the manual distribution. Some of the possible approaches involve using SAS^®, Microsoft Visual Basic for Applications (VBA) or even a combination of both. This blog presents a solution based on EMAIL (SMTP) access method within SAS FILENAME statement and compares this methodology with the technique using Microsoft Visual Basic, as well as providing an introduction to the object oriented type of programming. Further discussion will be on merits and limitations of both routines and finally the blog will consider integration of both processes.