Max Parmar, Director, Professor of Medical Statistics & Epidemiology

Mahesh Parmar is a Professor of Medical Statistics and Epidemiology and Director of both the MRC Clinical Trials Unit at UCL and the Institute of Clinical Trials and Methodology at University College London.

He was for over 10 years an Associate Director of the National Cancer Research Network since its inception in 2001, an organisation which more than doubled the number of patients going into cancer studies in England.

Max joined the MRC in 1987. He has more than 400 publications in peer reviewed journals, many of which have had direct impact on policy, clinical practice and improving outcomes for patients. The Unit he directs is at the forefront of resolving internationally important questions, particularly in infectious diseases and cancer, and also aims to deliver swifter and more effective translation of scientific research into patient benefits.

It does this by carrying out challenging and innovative studies and by developing and implementing methodological advances in study design, conduct and analysis.

Selected publications

Parmar MK, Carpenter J, Sydes MR. More multiarm randomised trials of superiority are needed. Lancet. 2014 Jul 26;384(9940):283-4.

NSCLC Meta-analysis Collaborative Group. Preoperative chemotherapy for non-small cell lung cancer: a systematic review and meta-analysis of individual participant data. Lancet. 2014;383(9928):1561-1571.

Kaplan R, Maughan T, Crook A, Fisher D, Wilson R, Brown L and Parmar M. Evaluating Many Treatments and Biomarkers in Oncology: A New Design. J Clin Onc Nov 2013.

Warde P, Mason M, Ding K, Kirkbride P, Brundage M, Cowan R, Gospodarowicz M, Sanders K, Kostashuk E, Swanson G, Barber J, Hiltz A, Parmar MK, Sathya J, Anderson J, Hayter C, Hetherington J, Sydes MR, Parulekar W. Combined androgen deprivation therapy and radiation therapy for locally advanced prostate cancer: a randomised, phase 3 trial. Lancet 2011;378(9809):2104-2111.

Royston P, Parmar MKB (2002). Flexible parametric proportional hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine, 21: 2175-2197.

Research Interests

IRIS Profile