Quanticate CRO Blog

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.

The laboratory dataset is one of the core safety datasets and, at first glance, it could appear intimidating, with multiple tests and visits per patient. This article will illustrate checks that are worth applying at the very beginning of programming work – these could be in addition to the standardized process of domain validation.

Clinical Trial Management Systems (CTMS) are an important part of every clinical trial. Selecting the right CTMS helps address inefficiencies on the operational side of research, such as clinical trial planning, preparation, performance and reporting. As more and more pharma and biopharma sponsors start to recognize the potential opportunities that exist with EDC-CTMS integration, there is a growing need to address the complex process of electronic data capture (EDC) implementation.

2012 saw the introduction of good pharmacovigilance practices (GVPs) in the European Union (EU) and since then, the industry has experienced a huge amount of change, including the introduction of the new EudraVigilance system in 2017. The new system, due to be fully implemented in 2019, is a European data processing network and management system designed to allow companies to report and evaluate suspected adverse reactions both during drug development and on an ongoing basis.

The ICH E6 on Good Clinical Practice was updated on 9 November 2016, the first addendum for 20 years. Sections 5.0 on Risk and 5.18.3 on Extent and Nature of Monitoring in particular created an increased need for Risk-Based Monitoring (RBM) and Centralized Monitoring (CM). The details of how this may be covered and the increasing evolution of a risk in the industry have been noted. The required changes involved create a number of different team roles. These include opportunities for biostatisticians and statistical programmers - players who may not have been so directly involved in onsite monitoring, a more traditionally clinical domain.

Our job as Real World Data programmers is to put the whole patient data, usually spread across multiple tables, into one coherent history. This might lead to some surprising discoveries such as: patients with records before birth/after death, patients changing sexes multiple times, and finding non-valid codes. Such discrepancies are usually pretty easy to classify as data issues and are easy to handle. On the other hand we might encounter real world data events that cannot be as easily classified and need special approaches such as; patient receiving multiple Rx’s for the same drug on one day, or receiving a new Rx’s before the previous runs out, patients visiting the Doctor’s office / Emergency Room during their hospital stays, patients changing wards/level of care multiple times during one hospital visit, and finding valid but non-billable codes in insurance claims data. In this post we share some commonly encountered problems (and our solutions) related to Real World Data analysis.

With encouragement from the U.S. Food and Drug Administration (FDA), using Patient Reported Outcomes (PRO) data to claim labeling became more and more popular. Well-defined and reliable PRO can be used to support a claim in medical product labeling. ^[1] It is found that there are an increasing number of regulatory submissions for new drugs to provide PRO data to support claims. DeMuro et al. (2013) ^[2] have reviewed drug approvals by both FDA and EMA for the years 2006–2010. They found that out of 75 drugs approved by the EMA, 35 (47%) had at least one PRO related claim approved by the EMA compared to 14 (19%) for the FDA.