PHASTAR to host life science day at Alderley Park

PHASTAR to host life science day at Alderley Park

Fresh from the success of their Boston-Cambridge Life Science evening, the PHASTAR team is at it again! This time PHASTAR will be hosting a Life Science Day near it's Alderley Park offices in Cheshire, UK.

The day will explore the theme of 'how statistical analysis and data science can enhance drug and diagnostic device development' and will feature talks given by PHASTAR staff and external experts.

Featuring talks from:

'What is Health Data Science? One biostatistician’s answer' by Professor Peter Diggle, Distinguished University Professor at CHICAS, Lancaster University Medical School.

'What sample size? 101 other ways statisticians influence trial design' by Andrew Lloyd, Head of Statistics - PHASTAR.

'Extracting value from real-world data: an example with wearables' by Professor Jennifer Bradford, Head of Data Science - PHASTAR.

'You've developed your AI tool - now how do we test it?' by Professor Jennifer Rogers, Head of Statistical Research - PHASTAR.

'Ensuring sponsor oversight, and quality of data and interpretation for early phase trials' by Dr Susan Lovick, Statistical Technical Lead - PHASTAR.

The event will take place on Tuesday 26th November, 9:30am-4pm at the Alderley Park Conference CentreRegistration will start at 9:30am and the sessions will run from 10am-4pm, followed by a networking reception at 4pm-6pm.


If you are interested in attending, please contact our Business Development Team at This email address is being protected from spambots. You need JavaScript enabled to view it.!

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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Meta-analysis in Clinical Trials

In medical research, information on the outcomes of a treatment may be available from a number of clinical studies with similar treatment protocols.  When the studies are considered individually, they may be either too small or too limited in scope to come to a general conclusion about the effect of the treatment.  Meta-analysis, defined as the statistical analysis of a large collection of analytic results for the purpose of integrating the findings, attempts to combine the results of multiple studies in order to gain statistical power, strengthen the evidence about possible treatment effects and, in adequately-powered studies, learn more about subgroups and possible interactions.

In its simplest form, meta-analysis uses what are known as frequentist fixed-effect approaches, with weighted averages of fully aggregated data (obtained from publications), to compare two treatment groups.  For example, this may use information on the mean weight loss and standard deviation for an active and placebo drug from five trials.  More advanced methodologies (such as random-effects models, meta-regression, and Bayesian approaches) allow the researcher to incorporate complex data structures (such as subgroup data, or individual patient data (IPD)).  Note that higher model complexity carries both advantages and disadvantages.

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