Biography
Background
I am a statistician with interests in methods for improving the design and analysis of clinical trials. As well as working in methodology development I have a great interest in collaborating on real trials to apply new methods in practice.
I have been at Newcastle University since July 2017.
Areas of expertise
- Biostatistics
- Clinical trials
- Novel trial design
Research
My research focuses on statistical methods for improved design and analysis of clinical trials. I am particularly interested in:
- Adaptive designs for clinical trials;
- Methods for incorporating biomarkers into clinical trials to stratify patients by treatment response;
- Improved methods for analysing composite endpoints, in particular ones with continuous components.
Scopus: Click here
Contact
- Email: james.wason@ncl.ac.uk
- Telephone: +44 (0) 191 208 3699
- Address: Population Health Sciences Institute, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne NE2 4AX, UK
Publications
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(2021).
Treatment allocation strategies for umbrella trials in the presence of multiple biomarkers: A comparison of methods.
Pharm Stat 2021; doi:10.1002/pst.2119.
(2021).
Statistical consideration when adding new arms to ongoing clinical trials: The potentials and the caveats.
Trials 2021; 22:203.
(2020).
Mentalization for Offending Adult Males (MOAM): Study protocol for a randomized controlled trial to evaluate mentalization-based treatment for antisocial personality disorder in male offenders on community probation.
Trials 2020; 21:1001.
(2020).
PSU4 Value of information for adaptive trials: Proof of concept study in multi-arm multi-stage trials of interventions for the prevention of surgical site infections.
Value Health 2020; 23:S738.
(2020).
Ensuring that COVID-19 research is inclusive: Guidance from the NIHR INCLUDE project.
BMJ Open 2020; 10:e043634.
(2020).
Innovative trial approaches in immune-mediated inflammatory diseases: Current use and future potential.
DOI: 10.21203/rs.3.rs-63613/v1.
(2020).
Efficient analysis of time-to-event endpoints when the event involves a continuous variable crossing a threshold.
J Stat Plan Infer 2020; 208:119-29.
(2020).
Controlling type I error rates in multi-arm clinical trials: A case for the false discovery rate.
Pharm Stat 2020.
(2020).
Increasing power in the analysis of responder endpoints in rheumatology: a software tutorial.
medRxiv 2020.
(2020).
Developing and testing high-efficacy patient subgroups within a clinical trial using risk scores.
Stat Med 2020; 39:3285-98.
(2020).
Developing a predictive signature for two trial endpoints using the cross-validated risk scores method.
arXiv 2020; arXiv:2007.01680.
(2020).
Efficient adaptive designs for clinical trials of interventions for COVID-19.
Stat Biopharm Res 2020; 12:483-97.
(2020).
A review of Bayesian perspectives on sample size derivation for confirmatory trials.
arXiv 2020; arXiv:2006.15715.
(2020).
Including non-concurrent control patients in the analysis of platform trials: Is it worth it?.
BMC Med Res Methodol 2020; 20:165.
(2020).
Graphical approaches for the control of generalised error rates.
Stat Med 2020.
(2020).
The Adaptive designs CONSORT Extension (ACE) statement: A checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design.
BMJ 2020; 369:m115.
(2020).
Analysis of responder-based endpoints: improving power through utilising continuous components.
Trials 2020; 21:427.
(2020).
Borrowing of information across patient subgroups in a basket trial based on distributional discrepancy.
Biostatistics 2020.
(2020).
Multisystemic therapy versus management as usual in the treatment of adolescent antisocial behaviour (START): 5-year follow-up of a pragmatic, randomised, controlled, superiority trial.
Lancet Psychiat 2020; 7:P420-30.
(2020).
Two-stage sparse regression screening to detect biomarker-treatment interactions in randomized clinical trials.
arXiv 2020; arXiv:2004.12028.