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The Evolution of Patient Centricity in Clinical Trials and Data Collection

Clinical Data Management Team



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.

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Topics: Clinical Trials, FDA, Phase I Studies, Electronic Data Capture, Drug Development, Patient Safety, Real World Data, Clinical Data Management

Creating your own interactive dashboards in SAS

Clinical Programming Team



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.

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Topics: Statistical Programming, SAS Programming, SAS Datasets, SAS, Interactive Dashboards

WARNING: Transposing Data using PROC SQL

Clinical Programming Team



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.

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Topics: Statistical Programming, SAS Programming, SAS Datasets, SAS Macros, SAS Proc Transpose, PROC SQL, SAS, Tree Traversal, Do Loops

Machine Learning in the Pharmaceutical Industry

Clinical Programming Team

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.

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A Guide to Adaptive Randomization based on a Patient's Characteristics

Holly Jackson


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.

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Topics: Clinical Study Design, Adaptive Trial Design, Statistical Consultancy, Randomized Controlled Trials, Personalized Medicine, Patient Characteristics

Examples of Do Loops in SAS with PROC DS2

Clinical Programming Team



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. 

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Topics: Statistical Programming, SAS Programming, SAS Datasets, SAS, Multithreading, PROC DS2, Object Oriented Programming

The Advantages of Parallel Processing Clinical Data in SAS/Connect

Clinical Programming Team


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.

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Topics: Clinical Trials, Phase I Studies, Clinical Study Design, SAS Programming, Phase 3 Studies, Phase 4 Studies, Multiple Imputation, Phase 2 Studies, SAS, Missing Data, Safety Dataset, Parallel Processing

Re-Randomization Tests in Equivalence Trials: Can We Still Use Them?

Statistical Consultancy Team

Randomization is widely acknowledged to be one of the more, if not the most, important parts of a properly planned and designed clinical trial. Flaws at a randomization level might lead to systematic imbalances in the allocation of patients to treatment groups, ultimately resulting in a lack of control of the overall type I error (i.e.: the pre-specified α level used as a reference for hypothesis testing). Whilst generation of randomization lists based on ‘static’ algorithms (e.g. stratified algorithms) is a relatively easy and standard process that can be done using standard software, a new type of method, that we’ll refer to as ‘dynamic’ randomization, is also increasingly used. This requires more complex algorithms to be embedded in the Interactive Web Response System (IWRS) integrated with the study database. The reason for this is that whilst with common algorithms the list of treatment allocations is fully determined a priori, i.e.: before we know the characteristic of the subjects that will be randomized, dynamic methods generate the randomization list case-wise, that is only when a new patients come in, using minimization algorithms to make sure that the groups are balanced with respect to specific characteristics not only when all subjects have been recruited, but during the whole recruitment process.

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Topics: Phase I Studies, Randomization, Phase 3 Studies, Phase 4 Studies, Phase 2 Studies, Equivalence Trial, Re-Randomization, Superiority Trial, Re-Randomization Test

FDA Guidance for Human Gene Therapy for Hemophilia A & B

Statistical Consultancy Team

Having seen an increasing number of gene therapy approvals, the FDA has issued draft guidance1 to help the developers of human gene therapy (GT) products for the treatment of hemophilia A & B.  In this article we will be focusing our attention on what guidance has been provided about the design of human gene therapy clinical trials for hemophilia A & B, including what is needed to support an accelerated approval approach. 

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Topics: Regulatory Requirements, Clinical Trials, FDA, Phase I Studies, Clinical Study Design, Phase 3 Studies, Phase 4 Studies, Additional Monitoring, Phase 2 Studies, Statistical Consultancy, rare diseases, hemophilia

A Guide to Phase 1 Clinical Trial Designs

Statistical Consultancy Team

The primary aims of Phase 1 Clinical Trials are to determine the safety, tolerability and pharmacokinetics (PK) of a compound. Trials have historically been conducted in the logical sequence of single ascending dose, multiple ascending dose, examination of preliminary effect of food on exposure, and potential drug-drug interaction, with assessments to determine the effect of gender, age, bioavailability and bioequivalence performed as necessary.

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Topics: Pharmacokinetics and Pharmacodynamic, Phase I Studies, Phase I Study Design, Clinical Trial Phases, Phase 3 Studies, Peadiatric Assessments, Bioequivalence, Drug-drug Interaction, Bioavailability

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