Quanticate CRO Blog

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.

The International Conference on Harmonisation (ICH) has defined E2B as the international standard for transmitting medicine adverse event reports. The ICH E2B document includes message standards required for effective transmission of individual case safety reports (ICSR). Eventually, the need for the exchange of high volume of safety information world-wide efficiently and automatically has led to periodic revisions of the E2B document. Since 2001, when E2B(R2) was implemented, there have been many developments in regulatory reporting requirements and pharmacovigilance practices. Recently, the ICH E2B(R3) and M2 Expert Working Groups (EWGs) jointly developed an implementation guide on the standards adopted for electronic transmission of ICSRs. It includes new requirements that will require compliance by product manufacturers (and the organizations that assist them with reporting safety information).

The blog was orginally presented by one of our statistical consultants at the Statisticians in the Pharmaceutical Industry (PSI) 2017 conference.

Inflammatory rheumatic diseases, such as ankylosing spondylitis (AS), are a major cause of work disability. Despite clinical progress in inflammation control and associated improvements in outcomes, work disability remains an issue for AS patients, and other underlying causes, such as fatigue, have been postulated. We have used data from an observational study, which followed a large cohort of AS patients in routine clinical practice for 12 months, to investigate the longitudinal relationship (data at baseline, 6 and 12 months) between fatigue and work disability in the presence of other recognised confounders. Initial results suggested possible inter-relationships between the effects of fatigue and anxiety/depression, leading to a post-hoc hypothesis that: