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].
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.
Multiple imputation is a statistical procedure for handling missing data in a study with the aim of reducing the bias, and complications, that missing data can cause. Multiple imputation involves creation of multiple datasets where the missing data are imputed with more realistic values as compared to the non-missing data, allowing for the uncertainty around what the real value might be by imputing data randomly from a distribution. Rubin (1987) developed a method for multiple imputation whereby each of the imputed datasets are analysed, using standard statistical methods, and the results are combined to give an overall result. Analyses based on multiple imputation should then give a result that reflects the true answer while adjusting for the uncertainty of the missing data.
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.
