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The Problems (and solutions) of Real World Data in the Pharma Industry

Clinical Programming Team

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.

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Topics: Clinical Programming, Outcomes Research, Real World Data, Observational longitudinal databases

Patient Reported Outcomes (PRO) to Support Medical Product Labeling Claims

Statistical Consultancy Team

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.

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Topics: FDA, European Medicines Agency, Biostatistics Consulting, Patient Reported Outcomes, medical labeling, PRO Instrument, Electronica Patient Reported Outcome (ePRO), Statistical Analysis Plan

ICH E2B(R3): Are You Ready For The New Update?

Pharmacovigilance Team


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).

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Topics: Good Clinical Practice (GCP), Adverse Events (AEs), Pharmacovigilance, Good Pharmacovigilance Practices, Standard Operating Procedures (SOP), Patient Safety, ICH E2B(R3), International Conference on Harmonisation (ICH), Safety Database

The Analysis of Direct and Indirect Pathways in Observational Studies

Statistical Consultancy Team

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:

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Topics: Biostatistics Consulting, Statisticians in the Pharmaceutical Industry (PSI), SAS Macros, Statistics, Observational Studies, Inflammatory Rheumatic Diseases, PROC GENMOD

Methods for assessing early-phase equivalence in Biosimilars

Statistical Consultancy Team

Rheumatoid Arthritis (RA) is a long-term inflammatory disease that causes pain, swelling, 
stiffness and loss of function in joints, with an approximately 0.5 to 1% (and increasing) prevalence in adults worldwide.  Alongside many treatment options, 
there’s recently been an increased focus on producing biosimilars, with many new drugs expected to come to the market in the coming years [1]. 

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Topics: Phase I Studies, Biostatistics Consulting, Bioequivalence, Biosimilars, Bioavailability, Rheumatoid Arthritis (RA)

Is a MSc in Statistics the ‘be-all and end-all’ to a career as a pharmaceutical statistician?

Statistical Consultancy Team


The vast majority of pharmaceutical companies and Clinical research organizations (CROs) ask for an MSc in Statistics (or Medical Statistics) when hiring statisticians, claiming these degrees in particular provide adequate preparation and the necessary hands-on experience to work in the industry. However – how strict is this rule across different companies, and should this be a requirement? Can similar qualifications such as an MMath or PGDip (which can have a lot of crossover with traditional statistics MScs) be equivalent and provide the required statistical training for a successful career in industry? Or can a BSc be sufficient? I aimed to answer these questions by asking senior statistical leaders and recruiters for their views through 4 questions.

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Topics: Statisticians in the Pharmaceutical Industry (PSI), Clinical Research Organization, MSc Statistics, Careers, Graduates

Top 3 Examples of Interactive Clinical Data Visualizations

Statistical Consultancy Team

Nowadays, vast amounts of data are collected during any clinical trial and it is essential for pharmaceutical sponsors to understand these data in great detail to make accurate decisions.

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Topics: Adverse Events (AEs), SDTM, Biostatistics Consulting, ADaM Datasets, Visualization, Technology Trends, Statistics

The Trend in Biosimilar Development and Recent FDA Guidance

Statistical Consultancy Team

At the present time, the regulatory and drug development communities are adapting to a rising trend in biosimilar development in a number of therapeutic areas.

The regulatory framework for biosimilars in the US is still evolving; the number of biosimilars approved by the FDA in 2015 was 1, this rose to 4 in 2016 and is set to increase in 2017. The FDA is developing and consulting on draft guidance documents that will shape future trials, and at this early stage there are a number of legal issues to be agreed around licencing conditions, such as the period of exclusivity and the applicability of the biosimilar to all approved indications of the reference product. To put this in context with the European landscape, biosimilars have been approved and used in the EU for over a decade without highlighting any major safety concerns. As of April 2017, there were 28 approved biosimilars in the EU on 11 different biologics. However, there are aspects of the emerging FDA guidance that will almost certainly be reflected in the evolution of trial designs in the future, for products aimed at the US market.

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Topics: Regulatory Requirements, Pharmacokinetics and Pharmacodynamic, FDA, Clinical Study Design, European Medicines Agency, Bioequivalence, PK Analysis, Demonstrating Biosimilarity, Biosimilars

The Logic of a Clinical Research Organization Programmer

Clinical Programming Team

As a statistical programmer at a leading data focused Clinical Research Organization (CRO), we are requested to become involved with many programming activities on a daily basis, centred around dataset or display generation and quality control (QC). Having the opportunity to develop a process/system which can be used by others is rare. To build any system, a lot of in depth thought is required before any programming begins. We take the requirements and build a robust system to address each potential scenario that may arise, including some which should not.

Within this blog we will explain how statistical programmers can work through a simple request to build a robust system using logic, SAS, UNIX and experience. It will provide a flavour of how robust systems are built and other considerations. 

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Topics: Clinical Programming, SAS Programming, Quality Control, SAS Macros, Quality Process Improvement, SAS

The INTO Statement in PROC SQL to Create Macro Variables

Clinical Programming Team

A member of the Quanticate Programming team writes about their opinions of the INTO statement in PROC SQL.

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Topics: Clinical Programming, SAS Programming, Large Datasets, SAS Macros, PROC SQL

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