The opportunities and challenges of basket studies

There has been a growing emergence in the utilisation of basket studies and it’s not difficult to see why. Progress in genomics, tumour biology and statistics has led to advances in “precision oncology”. Cancers that were once viewed as homogeneous in terms of location and treatment strategy are now better understood to be increasingly heterogeneous across biomarkers and genetically determined subgroups. No two cancers are the same; tumours differ from patient to patient and few patients may noticeably improve with treatment, whilst others experience no benefit at all. As a result, we have seen a shift towards targeted agents, and it has become more common for trials to focus on a specific mutation at a particular location.

When thinking about the timeframe from initial drug discovery to regulatory review, we need to embrace novel clinical trial designs that improve efficiency. With this in mind, there has been an observable trend towards investigating multiple target-treatment pairs in parallel, either within, or across tumour types. The term “master protocol” refers to a general framework whereby multiple parallel drug studies are operated under one overarching protocol. A basket trial is a type of master protocol that tests the effect of one drug on a single mutation in a variety of tumour types. That is, they include patients with a certain genetic mutation in common regardless of the site or origin of cancer in the body, so patients have cancer at a variety of sites such as lung, breast, prostate, etc.

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PHASTAR at SCDM 25 - conference summary

The theme of the 25th Society for Clinical Data Management (SCDM) annual conference in Baltimore was to raise awareness of the upcoming trends in the industry and reflect on how they will affect the clinical data management community.

The leadership forum convened a day ahead of the conference. It was an excellent opportunity for industry experts to come together, present their views and discuss how emerging study designs, regulations, and technology innovations are reshaping the role and profile of clinical data management. The core of our discussions was the first of 3 whitepapers from the SCDM released in June 2019, “The Evolution of Clinical Data Management to Clinical Data Science.” 

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PHASTAR's Head of Statistical Research to appear in BBC's Statistics and Big Data

PHASTAR's Jen Rogers will take part in the BBC's Statistics and Big Data conversation as part of the RSS's Belfast conference

As part of the build-up to the Royal Statistical Society’s 2019 Conference in Belfast, PHASTAR's Head of Statistical Research, Jennifer Rogers, will be taking part in an interactive conversation hosted by the BBC Statistics and Big Data. Jennifer will be joined by Presenter & Writer Timandra Harkness and Journalist Michael Blastland. The conversation will be hosted by BBC Northern Ireland's William Crawley.

The event will take place on Wednesday 4th September at 17:30 at BBC Northern Ireland Broadcasting House.

If you are interested in attending click here. The deadline for ticket applications is 22:00 (GMT+1) on 29th August.

More information can be found here.

Beyond the hype: AI and machine learning in clinical trials and healthcare

There is considerable hype surrounding Machine learning (ML) and Artificial Intelligence (AI) yet despite that, these technologies are real and powerful and this is starting to be realised in healthcare.  In this article we briefly discuss ML and AI alongside some key healthcare examples including how ML has added value in clinical trials with hands on examples performed by experts from PHASTAR’s newly established data science team. 

Although the terms AI and ML are frequently used interchangeably, they are not the same thing. AI is a broad concept that effectively describes how a machine can simulate natural human intelligence to solve a complex problem. AI is of course a moving target; based on those capabilities that a human possesses but a machine doesn’t. ML is one of the ways humans hope to achieve AI, where a machine can learn on its own without being programmed explicitly and without our constant supervision.

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Data integration for ISS/ISE

Integration of data from a number of clinical trials for an Integrated Summary of Safety (ISS) and Efficacy (ISE) requires careful planning and includes the following planning steps [see ICH M4]:

  • Assess the analysis and reporting requirement for the ISS/ISE
  • Consider these requirements against pre-existing study level analysis and reporting
  • Determine what data types need integrating across studies and at what level (SDTM/ADaM) the integration should occur at.

Assess the analysis and reporting requirement for the ISS/ISE

Before starting integrating data across studies it is key to have a clear understanding of what questions the ISS/ISE is trying to answer and how these requirements differ to those already answered by the individual studies.

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