Quanticate CRO Blog

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.

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. 

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.

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)

At the July 25^th 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.

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.

The BAYES 2018 Bayesian Biostatistics workshop took place from the 20th to the 22nd of June, and was an extraordinary chance to discuss how Bayesian methodology can be used within the pharma industry.

In this Statistical Knowledge Share Video our Principal Statisticians, Sonia, presents an example of Bayesian Methodology using simulated data and examines how to analyze Multiple End Points in Clinical Trials.