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A Guide to CDISC SDTM Standards, Theory and Application

Clinical Programming Team

The Clinical Data Interchange Standards Consortium (CDISC) creates standards that is now mandatory for the regulatory submission to the FDA and PMDA. Study Data Tabulation Model (SDTM) is one of the standards which provides a standard for streamlined data in collection, management, analysis and reporting. If your data is not in SDTM standards then a pharmaceutical company or Clinical Research Organizations (CRO) will regard SDTM mapping to the latest version of SDTM standards for regulatory submission.

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Topics: Statistical Programming, CDISC Guidelines, CDISC SDTM, SDTM Domains, CDISC, Standardization, Clinical Programming, SAS Programming, CDISC Interchange

A Guide to Adaptive Randomization based on a Patient's Characteristics

Holly Jackson


Clinical Trial Design

The most common design used for Phase II and Phase III clinical trials is the randomized controlled trial design. In randomized controlled trials, the probability of being assigned the study treatment or the control treatment is fixed throughout the trial and is normally 50%, so that each treatment is given to the same number of patients. This approach leads to a high‑chance of identifying if one treatment is significantly better than the other. The fact that 50% of the trial population is assigned to the control treatment is not a particular issue in common diseases such as cardiovascular disease. For example, of the 7 million people in the United Kingdom (UK) with cardiovascular disease, if 200 of them are involved a clinical trial, then 99.9% of the overall cardiovascular disease population will benefit from the results of the clinical trial[1].

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Topics: Clinical Study Design, Adaptive Trial Design, Statistical Consultancy, Randomized Controlled Trials, Personalized Medicine, Patient Characteristics

Get ready for Plain Language Summaries in Medical Writing

Medical Writing Team

The imminent application of Clinical Trial Regulation (EU) No. 536/2014, estimated to happen in 2020, will peak an emergent era of transparency in clinical development. The regulation applies to all interventional clinical trials (Phase 1 to 4) performed in the European Union (EU)/European Economic Area (EEA), plus additionally specified paediatric trials, and covers documents at the single clinical trial level irrespective of the drug’s marketing approval status. The regulation requires the disclosure of the study Protocol, Investigational Medicinal Product Dossier (IMPD; Sections S and E), Investigator’s Brochure (IB), Subject Information Sheet, Clinical Study Report (CSR; redacted) and a Plain Language Summary (PLS; also known as a Laypersons Summary) for each clinical trial within 12 months of the end of the trial (6 months for paediatric trials). Disclosure is readily welcomed by study participants who want to know what has been learned from their participation and is in accordance with the European Medicines Agency’s (EMA) goal of protecting and fostering public health by allowing better‑informed use of medicines. We will be able to learn from past successes as well as failures now that any biased under‑reporting of results will be stopped.

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Topics: Regulatory Requirements, Medical Writing, Regulatory Writing, Clinical Trial Documentation, Clinical Study Report, Plain Language Summaries

Novel Coronavirus Disease-2019 (Covid-19) Statistics and Scientific Resources

Commercial Team

As the novel coronavirus pandemic sweeps across the globe, the media coverage of the COVID-19 disease is vast. There are articles being published continuously on all types of websites, and by journalists, across multiple disciplines. The aim of this blog is to pull together a list of trusted, valuable, statistically sound, and scientifically robust resources on COVID-19 for data scientists, statisticians, clinical programmers, medical writers and other individuals involved in drug development within the pharmaceutical and biotechnology industry or beyond.

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Topics: Regulatory Requirements, Good Clinical Practice (GCP), Bayesian Methods, FDA, European Medicines Agency, Artificial Intelligence, Machine Learning, Covid-19, MHRA

Clinical Trial Outsourcing Trends and Research in 2020

Thomas Underwood

With constantly evolving industry regulations, the increasing pressures to reduce the costs of drug development, and raising demand for new therapies, trends in clinical trial outsourcing strategies emerge.

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Topics: Regulatory Requirements, Clinical Study Design, Outsourcing Solutions, Strategic Resourcing, Adaptive Trial Design, Additional Monitoring, Oncology, Functional Outsourcing, FSP, Real World Data, Risk Based Monitoring, ICHGCP E6, Observational Studies, rare diseases, Artificial Intelligence, Machine Learning, Personalized Medicine, Regulatory Submission, AI

Integration of CDISC ADaM Datasets for ISS/ISE Submissions

Clinical Programming Team

Integrations of studies are important for setting up safety and efficacy profiles of the component of interest and are referred to as ISS and ISE. Without the integration of multiple studies some larger trends in the data may not be so easily visible, regardless of whether those are good or bad, so these integrations are vital to ensuring the safety and effectiveness of any intervention.

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Topics: Statistical Programming, CDISC Guidelines, CDISC, ISS/ISE, Integrated Summaries, Integrated Summary of Safety, Integrated Summary of Efficacy, Large Datasets, ADaM Datasets, Analysis Data Model, Statistical Analysis Plan

Why Consider Functional Outsourcing by a Functional Service Provider?

Commercial Team

One of the key components of a Strategic Resourcing model is the provision of staff through functional outsourcing. This approach has enabled Clinical Research Organizations (CRO) to deliver genuine value and great customer care to many of the leading pharmaceutical companies around the world as they operate as Functional Service Providers (FSP). FSP relationships are becoming more commonplace within the pharmaceutical industry.

Recent clinical trial outsourcing trends demonstrate that  larger sponsors split their FSP outsourcing to full service outsourcing by 48% vs 52%, whereas smaller service providers come in at 36% FSP vs 64% full service. Providers indicate that a larger proportion of their revenue comes from FSP than from full-service arrangements (2018: FSP, 57%; full service, 43%). And, like sponsors, they anticipate remaining around this level and stability through 2021 (FSP, 53%; full service,47%).

Historically ISR reported that in terms of model allocation, the Preferred Provider model grew —34% of work is outsourced in this manner compared to 27% in 2015 and 21% in 2013. The Preferred Provider model is used for nearly as much work as in-house resources (36%)[1].

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Topics: CRO Selection, Specialist Biometrics CRO, Outsourcing Solutions, Strategic Resourcing, Quality Process Improvement, CRO Efficiency, Functional Outsourcing, FSP

Big SAS Code: How to Handle Large Programs

Clinical Programming Team

Substantial quantities of code can be difficult to navigate, debug and manage if poorly planned and laid out. Therefore, when starting a program anew or taking over previously established code it is imperative that extra effort is made to make the code straightforward and easy to understand for not only yourself but anyone who may inherit the code from you in the future. This blog will explore techniques and best practices to achieve this in your SAS® programs, whether starting with existing code or from scratch. Focus will be on larger SAS programs and how they can be accessible and reusable to developers of any level, including and going beyond the traditional standard good programming practices to delve into more advanced techniques and ideas for good program management. Techniques include using SAS functions and procedures to provide summaries and critical information for navigation and debugging of code.

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Topics: CDISC, SAS Programming, SDTM, SAS Datasets, Big Data, PROC SQL, SAS

The What, Why and How of Adaptive Clinical Trials

Statistical Consultancy Team

What are Adaptive Clinical Trials?

There have been various definitions of adaptive clinical trial designs over the last 10 years but the one below, coming from the FDA draft guidance on the topic, covers it pretty well:

 “A clinical trial design that allows for prospectively planned modifications to one or more aspects of the design based on accumulating data from subjects in the trial.” [1]

In simple terms, it means that we anticipate that some features of the study (e.g. sample size, treatment arms, population of interest, etc) might change during the course of the study (i.e. after randomization), according to a pre-specified set of rules. Notably, the key term in the above definition is ‘prospectively planned’: not all planned modifications are legitimate and safeguard trial integrity and interpretability, however unplanned changes will never be considered acceptable and will result in the study results being discarded as not valid.

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Topics: Clinical Trials, FDA, Phase I Studies, Clinical Study Design, Randomization, Biostatistics Consulting, Phase I Study Design, Drug Development, Adaptive Trial Design, Oncology, Statistical Consultancy, Randomized Controlled Trials

The Global Statistical Test for Multiple Endpoints Analysis

Statistical Consultancy Team

Multiple endpoints in clinical trials are a very common occurrence, one which is often linked to the complexity of the treatment effect that a study aims at estimating. In Parkinson’s Disease, for instance, whilst the endpoint favored by the regulators is often the Unified Parkinson’s Disease Rating Scale (UPDRS) Motor Score, there are other measures of drug activity that have a paramount importance both to the clinician and the patient such as, for instance, the amount of Good Quality ON Time (where ON time refers to whether the patient has received a symptomatic treatment such as Levodopa). A clinical trial might then want to investigate the treatment effect on both these endpoints in order to further support efficacy claims for the drug being studied.

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Topics: Clinical Trials, SAS Macros, Statistics, Multiple Endpoints, Statistical Consultancy, Estimands, R, Global Statistical Test

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