van der Schaar Lab

Paper on COVID-19 clinical trials published in Statistics in Biopharmaceutical Research

AstraZeneca R&D image

A paper authored jointly by the van der Schaar Lab’s researchers, alongside colleagues from AstraZeneca, Novartis, and academics within the broader machine learning community, has been published in Statistics in Biopharmaceutical Research (editor: Toshimitsu Hamasaki).

The paper, entitled “Machine learning for clinical trials in the era of COVID-19,” was accepted for publication on July 3 and made available online on July 20.

The authors introduce a number of machine learning methods that could substantially assist in the identification, approval and distribution of COVID-19 treatments and vaccines, with a particular emphasis on three particular challenges:

  • ongoing clinical trials for non-COVID-19 drugs;
  • clinical trials for repurposing drugs to treat COVID-19; and
  • clinical trials for new drugs to treat COVID-19.

While the paper in question focuses exclusively on COVID-19, its authors believe that their proposed approaches have broad applicability and substantial potential for impact within the domain of pharmaceutical discovery. They conclude that timely implementation of machine learning “will yield benefits that affect the entire future course of drug development and change the lives of patients across the world.”

In addition to its wide-reaching impact, the paper is notable as an example of fruitful and open collaboration between members of the academic machine learning community and researchers within the pharmaceutical industry. As the paper’s conclusion notes, “Diverse quantitative communities are coming together to address the challenges of this pandemic; our hope is that they will stay together – not just for this pandemic but in the long run, which will greatly improve the conduct of clinical trials in the future.”

You can find more work by the van der Schaar Lab’s researchers on adaptive clinical trials here, and on causal inference/estimating treatment effects here.

Machine learning for clinical trials in the era of COVID-19

William R. Zame, Ioana Bica, Cong Shen, Alicia Curth, Hyun-Suk Lee, Stuart Bailey, James Weatherall, David Wright, Frank Bretz & Mihaela van der Schaar


The world is in the midst of a pandemic. We still know little about the disease COVID-19 or about the virus (SARS-CoV-2) that causes it. We do not have a vaccine or a treatment (aside from managing symptoms). We do not know if recovery from COVID-19 produces immunity, and if so for how long, hence we do not know if “herd immunity” will eventually reduce the risk or if a successful vaccine can be developed – and this knowledge may be a long time coming.

In the meantime, the COVID-19 pandemic is presenting enormous challenges to medical research, and to clinical trials in particular.

This paper identifies some of those challenges and suggests ways in which machine learning can help in response to those challenges. We identify three areas of challenge: ongoing clinical trials for non-COVID-19 drugs; clinical trials for repurposing drugs to treat COVID-19, and clinical trials for new drugs to treat COVID-19. Within each of these areas, we identify aspects for which we believe machine learning can provide invaluable assistance.

For a full list of the van der Schaar Lab’s publications, click here.

To find out more about the van der Schaar Lab’s work related to the COVID-19 pandemic, visit our dedicated page here.

Ioana Bica

Ioana Bica

Ioana Bica is a second year PhD student at the University of Oxford and at the Alan Turing Institute. She has previously completed a BA and MPhil in Computer Science at the University of Cambridge where she has specialised in machine learning and its applications to biomedicine.

Ioana’s PhD research focuses on building machine learning methods for causal inference and individualised treatment effect estimation from observational data. In particular, she has developed methods capable of estimating the heterogeneous effects of time-dependent treatments, thus enabling us to determine when to give treatments to patients and how to select among multiple treatments over time.

Recently, Ioana has started working on methods for understanding and modelling clinical decision making through causality, inverse reinforcement learning and imitation learning.

Mihaela van der Schaar

Mihaela van der Schaar

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

Mihaela has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.

Nick Maxfield

Nick Maxfield

Nick oversees the van der Schaar Lab’s communications, including media relations, content creation, and maintenance of the lab’s online presence.

Nick studied Japanese (BA Hons.) at the University of Oxford, graduating in 2012. Nick previously worked in HQ communications roles at Toyota (2013-2016) and Nissan (2016-2020).

Given his humanities/languages background and experience in communications, Nick is well-positioned to highlight and explain the real-world impact of research that can often be quite esoteric. Thankfully, he is comfortable asking almost endless questions in order to understand a topic.