Researchers The Unit exists to develop and evaluate novel statistical methods of study design and data analysis relevant to pharmaceutical companies and medical research institutes.

We undertake methodological research, often in direct collaboration with companies, and provide professional development courses and consultancy service.

Our main areas of research and expertise are:

Statistical Methods for Dose-Finding Studies

Our work concerns first-in-man studies of new drugs, both in the setting of first-in-patients (e.g. oncology and TB) studies, and in the first of the healthy volunteers studies. Research is focused on Bayesian methods for Phase I/II dose-finding trials evaluating toxicity and activity (efficacy) simultaneously, and specifically, for advanced dose-finding trials studying combinations of multiple treatment and various dose-schedules 

Selected Publications:
  • Pavel Mozgunov, Thomas Jaki, ( 2019 ). A Flexible Design for Advanced Phase I/II Clinical Trials with Continuous Efficacy Endpoints . Biometrical Journal
  • Pavel Mozgunov, Thomas Jaki, ( 2019 ). An information-theoretic Phase I/II design for molecularly targeted agents that does not require an assumption of monotonicity . Journal of the Royal Statistical Society: Series C (Applied Statistics)
  • Winnie Yeung, John Whitehead, Ulrich Beyer, Bruno Reigner, Cheikh Diack, Thomas Jaki, ( 2015 ). Bayesian adaptive dose-escalation procedures for binary and continuous responses utilizing a gain function . Pharmaceutical Statistics

Analysing Pharmacokinetic and Pharmacodynamic Data

This is an area on which a new professional development course has been prepared. Most work to date has concerned non-compartmental estimation of pharmacokinetic parameters such as the area under the concentration versus time curve for sparse sampling schemes. 

Selected Publications:
  • Helen Barnett, Helena Geys, Tom Jacobs, Thomas Jaki, ( 2017 ). Comparing sampling methods for pharmacokinetic studies using model averaged derived parameters . Statistics in Medicine
  • Jaki, T., Pallmann, P., Wolfsegger, M. J., ( 2013 ). Estimation in AB/BA cross-over trials with application to bioequivalence studies with incomplete and complete data designs . Statistics in Medicine. 32(30):5469-5483
  • Helen Barnett, Tom Jacobs, Helena Geys, Thomas Jaki, ( 2018 ). Optimal Designs for Non-Compartmental Analysis of Pharmacokinetic Studies . Statistics in Biopharmaceutical Research

Proof-of-Concept Studies

We are interested in both Bayesian and frequentist approaches to the determination of sample size for phase II studies. Such studies may be conducted to make a go/no go decision for a single treatment, or to select one or more treatments or doses for further study. It is often desirable to include an interim analysis within the design, although less common to need more than one. Futility analyses are very sensible at this early stage of clinical evaluation. Often the decision to proceed has to be taken on the basis of an early endpoint that will not be suitable for later phase III studies: the sample size for such a trial should be related to the eventual treatment effect desired in terms of the definitive long-term outcome.

Selected Publications:
  • Faye Williamson, Peter Jacko, Sofia Villar, Thomas Jaki, ( 2017 ). A Bayesian adaptive design for clinical trials in rare diseases . Computational Statistics and Data Analysis
  • Wason, J. M. S., Jaki, T., ( 2014 ). A review of statistical designs for improving the efficiency of phase II studies in oncology. . Statistical Methods in Medical Research. Published online

Adaptive Designs for Clinical Trials

There is substantial experience within MPS of developing and implementing group sequential designs for clinical trials, and we are currently involved in the conduct of interim analyses for several trials that we have previously designed. We have also worked on methods for sample size reviews and their implementation. Current research includes designs with a single interim analysis conducted to detect a treatment by factor interaction. The factor might concern the presence of a biomarker, which might be genetic.  

Selected Publications:
  • Magirr, D., Jaki, T., Whitehead, J., ( 2012 ). A generalized Dunnett Test for Multi-arm Multi-stage Clinical Studies with Treatment Selection . Biometrika. 99(2), 494-501.
  • Wason, J. M. S., Jaki, T., ( 2012 ). Optimal design of multi-arm multi-stage trials . Statistics in Medicine. 31(30), 4269-4279.
  • Wason, J. M. S., Magirr, D., Law, M., Jaki, T., ( 2014 ). Some recommendations for multi-arm multi-stage trials . Statistical Methods in Medical Research

Survival Data

In the context of survival data, the score statistics will be logrank statistics or their counterparts from a Cox’s proportional hazards regression model adjusting for prognostic factors. The different score statistics will correspond to different waiting times, such as time to death and time to disease progression, or time to loss of sight in the right eye and time to loss of sight in the left eye. Correlations are needed to enable bivariate or multivariate analyses to be made or global tests to be performed. One possible approach is to use the Wei-Lin-Weissfeld method, but that is difficult to understand and does not reduce to a simple logrank analysis in the case of a single endpoint.

Selected Publications:
  • Matthias Brueckner, Andrew Titman, Thomas Jaki, ( 2017 ). Estimation in multi-arm two-stage trials with treatment selection and time-to-event endpoint . Statistics in Medicine
  • Matthias Brueckner, Andrew Titman, Thomas Jaki, ( 2019 ). Instrumental variable estimation in semi-parametric additive hazards models . Biometrics
  • Fang Wan, Andrew Titman, Thomas Jaki, ( 2019 ). Subgroup analysis of treatment effects for misclassified biomarkers with time-to-event data . Journal of the Royal Statistical Society: Series C (Applied Statistics)