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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

Statistical Methods in Risk Based Monitoring

Statistical Consultancy Team

This blog explores the statistical methods used in Risk Based Monitoring (RBM) and how the result of such statistical methods enables improved data integrity across a clinical trial.

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Topics: Statistics, Risk Based Monitoring, Centralized Monitoring, ICHGCP E6, Laboratory Datasets, Statistical Consultancy

Remote Monitoring During Clinical Trials, a Risk Based Approach

Medical Writing Team

Within the last few decades the number and complexity of clinical trials has increased considerably, not only across the industry but within individual companies.  With this increase comes the enhanced pressure of effectively monitoring these trials. 

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Topics: Regulatory Requirements, Clinical Trials, Good Clinical Practice (GCP), Clinical Data Storage, Case Report Form (CRF), Remote Monitoring, Adverse Events (AEs), Ethics, On-Site Monitoring, Risk Based Monitoring, Centralized Monitoring

[Published Journal] Using Observational Studies to Investigate the Relationship Between Fatigue and Work Disability

Statistical Consultancy Team

Our Statistical Consultancy Team recently collaborated on a study to investigate the long-term relationship between fatigue and work disability in patients initiating treatment with etanercept for rheumatoid arthritis (RA) or ankylosing spondylitis (AS); and this work has now been published. (https://arthritis-research.biomedcentral.com/articles/10.1186/s13075-018-1598-8)

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Topics: Rheumatoid Arthritis (RA), Observational Studies, Inflammatory Rheumatic Diseases, Observation Longitudinal Database, Statistical Consultancy

Why do a 3rd of Submissions fail the Technical Rejection Criteria?

Clinical Programming Team

At the July 25th PhUSE Webinar Wednesday, Ethan Chen (Director, Division of Data Management Services and Solutions Office of Business Informatics, FDA CDER) gave us a glimpse of the FDA’s view on Technical Rejection Criteria for Study Data. Those that were at the US Connect Conference earlier in the year may have seen this already.

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Topics: CDISC Guidelines, CDISC SDTM, SDTM Domains, CDISC, FDA, SAS Programming, SDTM, CDISC Standards, ADaM Datasets, Define.xml

Nutraceutical trials: demand, design and challenges

Statistical Consultancy Team

Food ingredient and nutraceutical manufacturers are increasingly looking to market their products by substantiating their unique health benefits.

There are many generic, widely used claims - in the EU, Article 13.1 of the European Food Safety Authority’s (EFSA) EC Regulation permits manufacturers to use some 4,6371 that pertain to vitamin and mineral content.

This proliferation is driving manufacturers to develop unique claims to use in marketing efforts, which in turn has led to a significant growth in demand for nutraceutical trials.

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Topics: Regulatory Requirements, Clinical Study Design, Biostatistics Consulting, Statistics, Nutraceutical Trials

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