Quanticate CRO Blog

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.

It’s with great pride that we can say we are a part of an industry which is making every virtuous effort in changing lives of millions of patients on this planet by developing new medicines and devices. Being in the Biometric world, I believe that we are the privileged ones among others in the clinical development world. We have the privilege to engage with the maximum number of patients on a daily basis, even though it’s virtual. Clinical Research Organizations see, process and analyze hundreds of patients’ data daily across portfolios of clinical trials that we work on.

Easily finding the right information is key to smart and timely business decisions. In Clinical Trials, it can even mean the difference between life and death! There are huge numbers of visualization tools available, but are they best? When you understand your data and user needs fully, are you better off building something from scratch within SAS? Using dummy Clinical Trial data, we'll compare our in-house web-based server-less solution, which uses PROC JSON and the Open Source D3 library for graphing and drill-down. We will also review SAS techniques helpful in building in-house platform: PROC GKPI, PROC STREAM and custom html+JavaScript codes run through SAS. As an alternative we look at paid-for solutions like Microsoft Power BI Desktop. Finally, we‟ll consider if the breadth of options offered by tools like Power BI help or hinder, compared to targeted reports defined by experts.

If you search online for “how to transpose using PROC SQL” you will be told – repeatedly – that it cannot be done. Out of curiosity I decided to ignore the advice, and my solution came to 345 lines, achieving what PROC TRANSPOSE can do in 6; proving that you can do it – you just shouldn’t. However, tackling the challenge requires us to look deeper at tree traversal, used for finding combinations of variables for the transposed columns. Within SAS, basic tree traversal methods include nested and macro do loops, and as we get more advanced we can create efficient algorithms dealing with file searching, data-structuring and much more. This blog will give an overview of my method for transposing and use it to show how tree traversal can be understood and implemented in SAS.

This blog explores what Machine Learning (ML) is and it’s difference variations. We will cover the three types of ML and present real-life examples from the pharmaceutical industry of all three types. We will also cover the SAS Data Mapper Tool which is one of the ML algorithms. In addition to this, we will also touch base upon challenges of data science and the regulatory processes for approvals of AI/ML Products.

Randomized controlled trials (RCTs) are the approach most often used in phase II and phase III clinical trials. In RCTs the probability of being assigned the experimental drug and the control is fixed throughout the trial and normally 50%, so that each drug is given to a similar number of patients. This leads to a high chance of identifying if one treatment is significantly better.

PROC DS2 is a new SAS® proprietary programming language with full release in version 9.4. It has many features but this blog’s focus will be on Object Oriented Programming (OOP) and multithreading. Multithreading and greater efficiency in the use of your system can be an exciting prospect, but the daunting task of learning OOP can slow down or block attempts to fully learn and utilise this exciting new procedure. 

Increasing amounts of data to be processed and further use of computationally intensive statistical techniques such as Bayesian Analysis and Multiple Imputation (MI) in clinical trials has resulted in a large increase in computer processing times which presents challenges when analyzing and reporting clinical trial data. This increase in processing time can cause delays in timelines if this has not been fully accounted for. The execution of the quality control (QC) programs of such tasks may also have to be coordinated and performed in parallel to limit the total time spent processing on production and QC which can be difficult to coordinate. It may even be the case that results from the production program are unable to be fully produced when double programmed due to time constraints (e.g. obtaining fewer samples in a Bayesian analysis or performing fewer imputations) which can reduce the quality of the QC process.