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I am an MRC Investigator in the clinical trials methodology group, part of the Design and Analysis of Randomised Trials (DART) theme.
I have a Ph. D. in Business Administration and Quantitative Methods at Universidad Carlos III de Madrid in July 2012, with a focus on Stochastic Dynamic Optimization. In 2013, I joined the MRC Biostatistics Unit (BSU) in Cambridge as part of a project on the design of multi-arm multi-stage clinical trials. In 2014, I was awarded the first ever Biometrika post-doctoral fellowship. I am now leading a team of statisticians at the BSU and at Papworth Hospital trials Unit.
My research aims to improve clinical trial design through the development of innovative methods that lie in the intersection between optimisation, machine learning and statistics. These methods may result in efficiency gains (i.e. smaller or faster trials) but face several practical barriers (e.g. a high computational cost) to be widely adopted. These innovations include patient-centric trials - i.e. those having an explicit goal of assigning more trial participants to superior treatments (e.g. an efficacious vaccine). My proposed work includes four main objectives: 1) developing computationally feasible innovative trial designs, 2) improving analysis methods of optimal, patient-centric adaptive trials (estimation and testing); 3) designing innovative trial designs in response of specific emerging challenges (including using adaptive experiments to enhance and personalise m-health apps) and 4) promoting update and appropriate application of these novel designs in practice.
I am the senior statistician in two national trials that include an adaptive or innovative element. These are NIHR funded: The PIPAH trial (Positioning Imatinib for Pulmonary Arterial Hypertension) and The NOTACS trial (Nasal High-Flow Oxygen Therapy After Cardiac Surgery).
The PIPAH trial (Positioning Imatinib for Pulmonary Arterial Hypertension)
The goal of this study is to repurpose the anti-cancer drug Imatinib to treat a rare condition called idiopathic pulmonary arterial hypertension. This is an early stage open label single armed study that is composed of two parts, a dose selection stage and an efficacy stage. This will allow us to assess both the safety, tolerability and efficacy of Imatinib in the novel clinical setting. Both stages take advantage of adaptive designs to answer our hypotheses most efficiently with the expected number of patients. Our dose selection stage design is an example of the Bayesian continual reassessment method where we will combine prior information with observations of treatment toxicities at certain doses in a sequential manner to best identify the most tolerable dose. Our efficacy design follows a classical Simon’s two stage design that has a built-in interim analysis that will allow for early stopping due to futility. Such adaptive designs are essential in these settings where the rarity of the condition restricts our ability to conduct large scale controlled studies.
This trial is a collaboration between the MRC BSU Papworth Statistics Team (including Mikel Mckie and Martin Law) and Imperial College London (Prof. Martin Wilkins).
The NOTACS trial (Nasal High-Flow Oxygen Therapy After Cardiac Surgery)
The aim of the NOTACS trial is to compare the efficacy, cost-effectiveness and safety of two types of oxygen therapy in patients at high risk of post-operative pulmonary complication after cardiac surgery. The study uses a composite primary outcome measure of days alive and at home within 90 days of surgery, which captures several important patient-centred outcomes (such as complications, prolonged hospital stay, discharge to any post-acute care nursing facility, hospital readmission, and early death after surgery) in a single measure.
The NOTACS trial is an adaptive, multicentre, parallel group, randomised controlled trial. Traditionally, clinical trials use a fixed sample size which is estimated up-front, before the trial starts. This can be problematic as we often find that there is little pre-existing data to confidently base our sample size calculations on. The adaptive design element of the NOTACS trial is an interim sample size re-estimation. After a fixed number of patients have been recruited and completed follow-up, we will repeat our sample size calculation using information from the data accumulated so far. This will allow us to make better informed decisions about the required sample size for the NOTACS trial to ensure that it is not over- or under-powered.
This trial is a collaboration between the MRC BSU Papworth Statistics Team (including Sarah Dawson and Yi-Da Chiu as trial statisticians).
I am also the Senior Statistician leading a team of statisticians at Papworth Trials Unit Collaboration https://royalpapworth.nhs.uk/research-and-development/papworth-trials-unit-collaboration
Selected PapersSofia S. Villar and William F. Rosenberger (2017)Covariate-adjusted response-adaptive randomization for multi-arm clinical trials using a modified forward looking Gittins index rule
Biometrics : (To appear)
Adam Smith and Sofia S. Villar (2017)Bayesian adaptive bandit-based designs using the Gittins index for multi-armed trials with normally distributed endpoints
Journal of Applied Statistics : (To appear)
Williamson, Faye; Jacko, Peter; Villar, Sofia S.; Jaki, Thomas F. (2016)A Bayesian adaptive design for clinical trials in rare diseases
Computational Statistics and Data Analysis : (To appear)
Sofia S. Villar, Jack Bowden and James Wason (2015)Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges
Statistical Science : Vol. 30, No. 2, 199–215
Sofia S. Villar, James Wason and Jack Bowden (December 2015)Response-adaptive Randomization for Multi-arm Clinical Trials using the Forward Looking Gittins Index rule
Biometrics Volume 71, Issue 4: Pages 969–978
Sofia S. Villar (2017)Bandit strategies evaluated in the context of clinical trials in rare life-threatening diseases
Probability in the engineering and informational sciences Special Issue on Analytic Methods in Health Care and in Clinical Trials: (To appear)