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James Wason

Professor of Biostatistics

Population Health Sciences Institute

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. I currently retain a part-time position in the MRC Biostatistics Unit, University of Cambridge.

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:

  1. Adaptive designs for clinical trials;
  2. Methods for incorporating biomarkers into clinical trials to stratify patients by treatment response;
  3. Improved methods for analysing composite endpoints, in particular ones with continuous components.

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Publications

You can quickly discover relevant content by members of the PHSI-BRG by filtering publications here.

Innovative trial approaches in immune-mediated inflammatory diseases: Current use and future potential

DOI: 10.21203/rs.3.rs-63613/v1
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Efficient analysis of time-to-event endpoints when the event involves a continuous variable crossing a threshold

Journal of Statistical Planning and Inference 2020; 208:119-29
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Controlling type I error rates in multi-arm clinical trials: A case for the false discovery rate

Pharmaceutical Statistics 2020
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Increasing power in the analysis of responder endpoints in rheumatology: a software tutorial

medRxiv 2020
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Developing and testing high-efficacy patient subgroups within a clinical trial using risk scores

Statistics in Medicine 2020; 39(24):3285-3298
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Developing a predictive signature for two trial endpoints using the cross-validated risk scores method

arXiv 2020; arXiv:2007.01680

Efficient adaptive designs for clinical trials of interventions for COVID-19

Statistics in Biopharmaceutical Research 2020; 12(4):483-497
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A review of Bayesian perspectives on sample size derivation for confirmatory trials

arXiv 2020; arXiv:2006.15715

Including non-concurrent control patients in the analysis of platform trials: Is it worth it?

BMC Medical Research Methodology 2020; 20:165
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Graphical approaches for the control of generalised error rates

Statistics in Medicine 2020
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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
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Analysis of responder-based endpoints: improving power through utilising continuous components

Trials 2020; 21:427
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Borrowing of information across patient subgroups in a basket trial based on distributional discrepancy

Biostatistics 2020
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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 Psychiatry 2020; 7(5):P420-P430
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Two-stage sparse regression screening to detect biomarker-treatment interactions in randomized clinical trials

arXiv 2020; arXiv:2004.12028

Multiple Interventions for Diabetic Foot Ulcer Treatment Trial (MIDFUT): Study protocol for a randomised controlled trial

BMJ Open 2020; 10(4):e035947
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Prevalence of multiplicity and appropriate adjustments among cardiovascular randomized clinical trials published in major medical journals

JAMA Network Open 2020; 3(4):e203082
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Developing a composite outcome measure for frailty prevention trials - Rationale, derivation and sample size comparison with other candidate measures

BMC Geriatrics 2020; 20:113

Simple MRI score aids prediction of dementia in cerebral small vessel disease

Neurology 2020; 94(12):e1294-e1302
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A web application for the design of multi-arm clinical trials

BMC Cancer 2020; 20:80
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Imaging breast cancer using hyperpolarized carbon-13 MRI

Proceedings of the National Academy of Sciences of the United States of America 2020; 117(4):2092-2098
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Sample size estimation using a latent variable model for mixed outcome co-primary, multiple primary and composite endpoints

arXiv 2019; arXiv:1912.05258

Overestimated treatment effects in randomised phase II trials: What's up doctor?

European Journal of Cancer 2019; 123:116-127
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Two-stage adaptive designs for three-treatment bioequivalence studies

Statistics in Biopharmaceutical Research 2019; 11(4):360-374
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Bayesian design and analysis of external pilot trials for complex interventions

arXiv 2019; arXiv:1908.05955

To add or not to add a new treatment arm to a multiarm study: A decision-theoretic framework

Statistics in Medicine 2019; 38(18):3305-3321
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The impact of an epilepsy nurse competency framework on the costs of supporting adults with epilepsy and intellectual disability: Findings from the EpAID study

Journal of Intellectual Disability Research 2019; 63(12):1391-1400
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Evaluation of PR3-ANCA status after rituximab for ANCA-associated vasculitis

Journal of Clinical Rheumatology 2019; 25(5):217-223
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Effect of sleep deprivation and exercise on reaction threshold in peanut-allergic adults: A randomised controlled study

Journal of Allergy and Clinical Immunology 2019; 144(6):1584-1594.e2
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Exact group sequential designs for two-arm experiments with Poisson distributed outcome variables

Communications in Statistics - Theory and Methods 2019
DOI .bib .enw .ris

Anti-VEGF intervention in neovascular AMD: Benefits and risks restated as natural frequencies

BMJ Open Ophthalmology 2019; 4(1):e000257
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Design of Multi-Arm, Multi-Stage Trials in Oncology

In Textbook of Clinical Trials in Oncology: A Statistical Perspective

Admissible multiarm stepped‐wedge cluster randomized trial designs

Statistics in Medicine 2019; 38(7):1103-1119
DOI .bib .enw .ris

Design of experiments for a confirmatory trial of precision medicine

Journal of Statistical Planning and Inference 2019; 199:179-187
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A latent variable model for improving inference in trials assessing the effect of dose on toxicity and composite efficacy endpoints

Statistical Methods in Medical Research 2020; 29(1):230-242
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Employing latent variable models to improve efficiency in composite endpoint analysis

arXiv 2019; arXiv:1902.07037

Familywise error control in multi-armed response-adaptive trials

Biometrics 2019; 75(3):885-894
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Biomarker-guided trials: Challenges in practice

Contemporary Clinical Trials Communications 2019; 16:100493
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When to keep it simple - Adaptive designs are not always useful

BMC Medicine 2019; 17:152
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Multi-arm multi-stage trials can improve the efficiency of finding effective treatments for stroke: A case study

BMC Cardiovascular Disorders 2018; 18(1):215
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Development process of a consensus-driven CONSORT extension for randomised trials using an adaptive design

BMC Medicine 2018; 16(1):210
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Group sequential crossover trial designs with strong control of the familywise error rate

Sequential Analysis 2018; 37(2):174-203
DOI .bib .enw .ris

A novel nano-iron supplement to safely combat iron deficiency and anaemia in young children: The IHAT-GUT double-blind, randomised, placebo-controlled trial protocol

Gates Open Research 2018; 2:48
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Blinded and unblinded sample size reestimation in crossover trials balanced for period

Biometrical Journal 2018; 60(5):917-933
DOI .bib .enw .ris

Blinded and unblinded sample size reestimation procedures for stepped-wedge cluster randomized trials

Biometrical Journal 2018; 60(5):903-916
DOI .bib .enw .ris

Healthy Campus Trial: A multiphase optimization strategy (MOST) fully factorial trial to optimize the smartphone cognitive behavioral therapy (CBT) app for mental health promotion among university students: Study protocol for a randomized controlled trial

Trials 2018; 19:353
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Group sequential clinical trial designs for normally distributed outcome variables

The Stata Journal 2018; 18(2):416-431
DOI .bib .enw .ris

Improving the analysis of composite endpoints in rare disease trials

Orphanet Journal of Rare Diseases 2018; 13:81
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An optimised multi-arm multi-stage clinical trial design for unknown variance

Contemporary Clinical Trials 2018; 67:116-120
DOI .bib .enw .ris

Response‐adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Pharmaceutical Statistics 2018; 17(2):182-197
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Adaptive designs in clinical trials: Why use them, and how to run and report them

BMC Medicine 2018; 16:29
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Multisystemic therapy versus management as usual in the treatment of adolescent antisocial behaviour (START): A pragmatic, randomised controlled, superiority trial

Lancet Psychiatry 2018; 5(2):119-133
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Training nurses in a competency framework to support adults with epilepsy and intellectual disability: The EpAID cluster RCT

Health Technology Assessment 2018; 22(10):1-104
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Improving phase II oncology trials using best observed RECIST response as an endpoint by modelling continuous tumour measurements

Statistics in Medicine 2017; 36(29):4616-4626
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Group sequential designs for stepped-wedge cluster randomised trials

Clinical Trials 2017; 14(5):501-517
DOI .bib .enw .ris

The longitudinal effect of ejaculation on seminal vesicle fluid volume and whole-prostate ADC as measured on prostate MRI

European Radiology 2017; 27(12):5236-5243
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Imaging biomarker roadmap for cancer studies

Nature Reviews Clinical Oncology 2017; 14(3):169-186
O’Connor JP, Aboagye EO, Adams JE, Aerts HJ, Barrington SF, Beer AJ, Boellaard R, Bohndiek SE, Brady M, Brown G, Buckley DL, Chenevert TL, Clarke LP, Collette S, Cook GJ, deSouza NM, Dickson JC, Dive C, Evelhoch JL, Faivre-Finn C, Gallagher FA, Gilbert FJ, Gillies RJ, Goh V, Griffiths JR, Groves AM, Halligan S, Harris AL, Hawkes DJ, Hoekstra OS, Huang EP, Hutton BF, Jackson EF, Jayson GC, Jones A, Koh DM, Lacombe D, Lambin P, Lassau N, Leach MO, Lee TY, Leen EL, Lewis JS, Liu Y, Lythgoe MF, Manoharan P, Maxwell RJ, Miles KA, Morgan B, Morris S, Ng T, Padhani AR, Parker GJ, Partridge M, Pathak AP, Peet AC, Punwani S, Reynolds AR, Robinson SP, Shankar LK, Sharma RA, Soloviev D, Stroobants S, Sullivan DC, Taylor SA, Tofts PS, Tozer GM, van Herk M, Walker-Samuel S, Wason J, Williams KJ, Workman P, Yankeelov TE, Brindle KM, McShane LM, Jackson A, Waterton JC
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Stepped wedge cluster randomized controlled trial designs: A review of reporting quality and design features

Trials 2017; 18:33
DOI .bib .enw .ris

An adaptive design for updating the threshold value of a continuous biomarker

Statistics in Medicine 2016; 35(27):4909-4923
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Improving outcomes in adults with epilepsy and intellectual disability (EpAID) using a nurse-led intervention: Study protocol for a cluster randomised controlled trial

Trials 2016; 17(1):297
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Improving the power of clinical trials of rheumatoid arthritis by using data on continuous scales when analysing response rates: An application of the augmented binary method

Rheumatology 2016; 55(1):1796-1802
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Endoplasmic reticulum stress, the unfolded protein response and development of colon adenocarcinoma

Virchows Archiv 2016; 469(2):145-154
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HLA associations in South Asian multiple sclerosis

Multiple Sclerosis 2016; 22(1):19-24
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Use of an embedded, micro-randomised trial to investigate non-compliance in telehealth interventions

Clinical Trials 2016; 13(4):417-424
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Evaluation of multisystemic therapy pilot services in Services for Teens Engaging in Problem Sexual Behaviour (STEPS-B): Study protocol for a randomized controlled trial

Trials 2015; 16:492
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Prospective study evaluating the relative sensitivity of 18F-NaF PET/CT for detecting skeletal metastases from renal cell carcinoma in comparison to multidetector CT and 99mTc-MDP bone scintigraphy, using an adaptive trial design

Annals of Oncology 2015; 26(10):2113-2118
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OptGS: An R package for finding near-optimal group-sequential designs

Journal of Statistical Software 2015; 66(2):1-13
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A Bayesian adaptive design for biomarker trials with linked treatments

British Journal of Cancer 2015; 113(5):699-705
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Multi-Arm Multi-Stage Designs for Clinical Trials with Treatment Selection

In Modern Adaptive Randomized Clinical Trials: Statistical and Practical Aspects

Two-stage phase II oncology designs using short-term endpoints for early stopping

Statistical Methods in Medical Research 2017; 26(4):1671-1683
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Noninterventional statistical comparison of BTS and CHEST guidelines for size and severity in primary pneumothorax

European Respiratory Journal 2015; 45(6):1731–1734
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Response‐adaptive randomization for multi‐arm clinical trials using the forward looking Gittins index rule

Biometrics 2015; 71(4):969-978
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A review of statistical designs for improving the efficiency of phase II studies in oncology

Statistical Methods in Medical Research 2016; 25(3):1010-1021
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The power of phase II end-points for different possible mechanisms of action of an experimental treatment

European Journal of Cancer 2015; 51(8):984-992
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Multi-armed bandit models for the optimal design of clinical trials: benefits and challenges

Statistical Science 2015; 30(2):199-215
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Design of telehealth trials – Introducing adaptive approaches

International Journal of Medical Informatics 2014; 83(12):870-880
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Confounding in association studies: Month of birth and multiple sclerosis

Journal of Neurology 2014; 261(10):1851-1856
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A multi-stage drop-the-losers design for multi-arm clinical trials

Statistical Methods in Medical Research 2017; 26(1):508-524
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Recent Developments in Group-Sequential Designs

In Developments in Statistical Evaluation of Clinical Trials 97-118
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Correcting for multiple-testing in multi-arm trials: Is it necessary and is it done?

Trials 2014; 15:364
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Comment on: Month of birth and risk of multiple sclerosis: Confounding and adjustments

Annals of Clinical and Translational Neurology 2014; 1(5):375
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Adaptive designs for clinical trials assessing biomarker-guided treatment strategies.

British Journal of Cancer 2014; 110:1950-1957
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A comparison of Bayesian adaptive randomization and multi-stage designs for multi-arm clinical trials

Statistics in Medicine 2014; 33(13):2206-2221
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Reducing the average number of patients needed in a phase II trial through novel design

Clinical Research and Regulatory Affairs 2013; 30(4):47-54
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Evaluation of multisystemic therapy pilot services in the Systemic Therapy for At Risk Teens (START) trial: Study protocol for a randomised controlled trial

Trials 2013; 14:265
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Confounding underlies the apparent month of birth effect in multiple sclerosis

Annals of Neurology 2013; 73(6):714–720
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Using continuous data on tumour measurements to improve inference in phase II cancer studies

Statistics in Medicine 2013; 32(26):4639-4650
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The endoplasmic reticulum stress marker CHOP predicts survival in malignant mesothelioma

British Journal of Cancer 2013; 108(6):1340-1347
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Planning multi-arm screening studies within the context of a drug development program.

Statistics in Medicine 2013; 32(20):3424-3435
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Some recommendations for multi-arm multi-stage trials

Statistical Methods in Medical Research 2012; 25(2):716-727
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Identifying combined design and analysis procedures in two-stage trials with a binary end point

Statistics in Medicine 2012; 31(29):3874-3884
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Minimizing the maximum expected sample size in two-stage phase II clinical trials with continuous outcomes

Journal of Biopharmaceutical Statistics 2012; 22(4):836-852
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A General Framework for Two-Stage Analysis of Genome-wide Association Studies and Its Application to Case-Control Studies

American Journal of Human Genetics 2012; 90(5):P760-P773
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Risk in complex genetics: "All models are wrong but some are useful"

Annals of Neurology 2012; 72(4):502–509
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Optimal multistage designs for randomised clinical trials with continuous outcomes

Statistics in Medicine 2012; 31(4):301-312
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Admissible two-stage designs for phase II cancer clinical trials that incorporate the expected sample size under the alternative hypothesis

Pharmaceutical Statistics 2012; 11(2):91-96
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Optimal design of multi-arm multi-stage trials

Statistics in Medicine 2012; 31(30):4269-4279
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The choice of test in phase II cancer trials assessing continuous tumour shrinkage when complete responses are expected

Statistical Methods in Medical Research 2015; 24(6):909-919
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Accelerated BEP: A phase I trial of dose-dense BEP for intermediate and poor prognosis metastatic germ cell tumour

British Journal of Cancer 2011; 105(6):766-772
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Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis

Nature 2011; 476(7359):214-219
The International Multiple Sclerosis Genetics Consortium & The Wellcome Trust Case Control Consortium 2, Sawcer S, Hellenthal G, Pirinen M, Spencer CCA, Patsopoulos NA, Moutsianas L, Dilthey A, Su Z, Freeman C, Hunt SE, Edkins S, Gray E, Booth DR, Potter SC, Goris A, Band G, Bang Oturai A, Strange A, Saarela J, Bellenguez C, Fontaine B, Gillman M, Hemmer B, Gwilliam R, Zipp F, Jayakumar A, Martin R, Leslie S, Hawkins S, Giannoulatou E, D’alfonso S, Blackburn H, Martinelli Boneschi F, Liddle J, Harbo HF, Perez ML, Spurkland A, Waller MJ, Mycko MP, Ricketts M, Comabella M, Hammond N, Kockum I, McCann OT, Ban M, Whittaker P, Kemppinen A, Weston P, Hawkins C, Widaa S, Zajicek J, Dronov S, Robertson N, Bumpstead SJ, Barcellos LF, Ravindrarajah R, Abraham R, Alfredsson L, Ardlie K, Aubin C, Baker A, Baker K, Baranzini SE, Bergamaschi L, Bergamaschi R, Bernstein A, Berthele A, Boggild M, Bradfield JP, Brassat D, Broadley SA, Buck D, Butzkueven H, Capra R, Carroll WM, Cavalla P, Celius EG, Cepok S, Chiavacci R, Clerget-Darpoux F, Clysters K, Comi G, Cossburn M, Cournu-Rebeix I, Cox MB, Cozen W, Cree BAC, Cross AH, Cusi D, Daly MJ, Davis E, de Bakker PIW, Debouverie M, D’hooghe MB, Dixon K, Dobosi R, Dubois B, Ellinghaus D, Elovaara I, Esposito F, Fontenille C, Foote S, Franke A, Galimberti D, Ghezzi A, Glessner J, Gomez R, Gout O, Graham C, Grant SFA, Guerini FR, Hakonarson H, Hall P, Hamsten A, Hartung H-P, Heard RN, Heath S, Hobart J, Hoshi M, Infante-Duarte C, Ingram G, Ingram W, Islam T, Jagodic M, Kabesch M, Kermode AG, Kilpatrick TJ, Kim C, Klopp N, Koivisto K, Larsson M, Lathrop M, Lechner-Scott JS, Leone MA, Leppä V, Liljedahl U, Lima Bomfim I, Lincoln R, Link J, Liu J, Lorentzen ÅR, Lupoli S, Macciardi F, Mack T, Marriott M, Martinelli V, Mason D, McCauley JL, Mentch F, Mero I-L, Mihalova T, Montalban X, Mottershead J, Myhr K-M, Naldi P, Ollier W, Page A, Palotie A, Pelletier J, Piccio L, Pickersgill T, F Piehl F, Pobywajlo S, Quach HL, Ramsay PP, Reunanen M, Reynolds R, Rioux JD, Rodegher M, Roesner S, Rubio JP, Rückert I-M, Salvetti M, Salvi E, Santaniello A, Schaefer CA, Schreiber S, Schulze C, Scott RJ, Sellebjerg F, Selmaj KW, Sexton D, Shen L, Simms-Acuna B, Skidmore S, Sleiman PMA, Smestad C, Solberg Sørensen P, Søndergaard HB, Stankovich J, Strange RC, Sulonen A-M, Sundqvist E, Syvänen A-C, Taddeo F, Taylor B, Blackwell JM, Tienari P, Bramon E, Tourbah A, Brown MA, Tronczynska E, Casas JP, Tubridy N, Corvin A, Vickery J, Jankowski J, Villoslada P, Markus HS, Wang K, Mathew CG, Wason J, Palmer CNA, Wichmann H-E, Plomin R, Willoughby E, Rautanen A, Winkelmann J, Wittig M, Trembath RC, Yaouanq J, Viswanathan AC, Zhang H, Wood NW, Zuvich R, Deloukas P, Langford C, Duncanson A, Oksenberg JR, Pericak-Vance MA, Haines JL, Olsson T, Hillert J, Ivinson AJ, De Jager PL, Peltonen L, Stewart GJ, Hafler DA, Hauser SL, McVean G, Donnelly P, Compston A
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Reducing sample sizes in two-stage phase II cancer trials by using continuous tumour shrinkage end-points

European Journal of Cancer 2011; 47(7):983-989
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Comparison of multimarker logistic regression models, with application to a genomewide scan of schizophrenia

BMC Genetics 2010; 11:80
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A non-synonymous SNP within membrane metalloendopeptidase-like 1 (MMEL1) is associated with multiple sclerosis

Genes and Immunity 2010; 11(8):660-664
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What role for genetics in the prediction of multiple sclerosis?

Annals of Neurology 2010; 67(1):3-10
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Replication analysis identifies TYK2 as a multiple sclerosis susceptibility factor

European Journal of Human Genetics 2009; 17(10):1309-1313
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