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How can we design trials in smaller populations? Join our Twitter Q&A

11 May 2017

On Tuesday 16 May 2017, we’ll be running a Twitter Q&A session around designing clinical trials for smaller populations, with the hashtag #smallpopulations.

There are situations when we know we will be unable to get enough patients to design the trial we would like; not through sloppiness or laziness, but because the patient population is relatively small. We then have three choices: throw our hands in the air and give up, do an underpowered trial, or make changes to the design to make the numbers feasible.

In a recent article for BMC Medicine, our director Max Parmar, Matt Sydes and Tim Morris addressed this problem. They recommended a simple, practical framework for making changes to the design.

First, they say we should always try to increase the number of patients we can enrol. Having done this as far as possible, they consider approaches to reducing the numbers required. These come under 'commonly considered' and 'less commonly considered' approaches. However, commonly considered does not mean 'better', just as less commonly considered approaches does not mean 'worse'.

We’re running a Twitter Q&A to give you a chance to ask questions about designing trials for smaller populations – and to hear your thoughts. Do you agree? Do you disagree? What’s your experience taught you?

Two of the authors, Max and Tim will be ready and waiting to answer your questions, and hear what you have to say.


Our panel

max_parmarMax Parmar
Statistician and Director, MRC CTU at UCL

Mahesh Parmar is a Professor of Medical Statistics and Epidemiology and Director of both the MRC Clinical Trials Unit and the Institute of Clinical Trials and Methodology at UCL.  He has been an Associate Director of the National Cancer Research Network since its inception in 2001, an organisation which has more than doubled the number of patients going into cancer studies in England. Max joined the MRC in 1987.  He has more than 200 publications in peer reviewed journals, many of which have had direct impact on policy, clinical practice and improving outcomes for patients. The Unit he directs is at the forefront of resolving internationally important questions, particularly in infectious diseases and cancer, and also aims to deliver swifter and more effective translation of scientific research into patient benefits. It does this by carrying out challenging and innovative studies and by developing and implementing methodological advances in study design, conduct and analysis.

Image of Tim MorrisTim Morris
Medical Statistician, MRC CTU at UCL

Tim Morris is a medical statistician based at the MRC Clinical Trials Unit at UCL. He works broadly on statistical methods for medical research, which aim to be anchored in statistical theory and be applicable to clinical trials. His main research interests are designing clinical trials in smaller populations, handling missing data and conducting sensitivity analyses, meta-analysis of individual participant data, the re-randomisation design for clinical trials, and anticipating and dealing with non-proportional hazards. Tim also has unhealthy obsessions with simulation studies and with statistical graphics, and is working on both.


How to participate

Our panellists will be sat by a computer and ready to answer questions from 12.30pm to 1.15pm on Tuesday 16 May (GMT). 

You can tweet within this 45 minute slot, or tweet your question before the session begins if you prefer.  If you would like to participate but are not on Twitter, you can also email us your questions in advance.

To ask a question, just tweet using the hashtag #smallpopulations.  One of our panel will then reply to you from the @MRCCTU account. 

As we have more than one panel member for our Q&A, the person who is answering your question will put their initials at the start of their tweet, so you know who is talking.

As well as answering your questions, we're also very interested to hear what you think - so if you want to contribute, tweet using the #smallpopulations hashtag.


Further information