Dr Emma C Martin

Research Associate

CONTACT DETAILS

Biostatistics Research Group
Department of Health Sciences,
College of Life Sciences,
University of Leicester, George Davies Centre,
University Road, Leicester, LE1 7RH, UK
Tel: +44 116 252 5726

Email:   emma.martin@le.ac.uk

Personal Details

I am a research associate in the biostatistics methods group at the University of Leicester. My main areas of interest are joint modelling of multiple outcomes and multistate modelling using electronic health records. 

Research

Research areas of interest

  • Joint modelling of multiple outcomes
  • Multistate disease modelling
  • Efficient use of electronic health records
  • Software development
  • Population pharmacokinetics and pharmacodynamics

 

Software development

I developed software merlin in R for fitting linear, non-linear and user-defined mixed effects regression models (https://CRAN.R-project.org/package=merlin). The flexibility of merlin allows it to fit a wide range of models, with multiple outcomes of any type, including longitudinal and time-to-event outcomes, with any number of levels of random effects.

I have also been involved in the development of pmsampsize in R (https://CRAN.R-project.org/package=pmsampsize) to calculate the minimum sample size required for developing multivariable prediction models and pmcalplot in Stata (https://ideas.repec.org/c/boc/bocode/s458486.html) to produce calibration plots of prediction model performance.

Publications

Published

E.C. Martin, L. Aarons, and J.W.T. Yates, Pharmacodynamic modelling of resistance to epidermal growth factor receptor inhibition in brain metastasis mouse models. Cancer Chemotherapy and Pharmacology, 2018. 82(4): p. 669-675. 

J. Ensor, J.J. Deeks, E.C. Martin and R.D. Riley, Meta-analysis of test accuracy studies using imputation for partial reporting of multiple thresholds. Research Synthesis Methods, 2018. 9(1): p. 100-115. 

E.C. Martin, J.W.T. Yates, K. Ogungbenro and L. Aarons, Choosing an optimal input for an intravenous glucose tolerance test to aid parameter identification. J Pharm Pharmacol, 2017. 69(10): p. 1275-1283. 

E.C. Martin, L. Aarons, and J.W. Yates, Designing More Efficient Preclinical Experiments: A Simulation Study in Chemotherapy-Induced Myelosupression. Toxicol Sci, 2016. 150(1): p. 109-16. 

E.C. Martin, L. Aarons, and J.W. Yates, Accounting for dropout in xenografted tumour efficacy studies: integrated endpoint analysis, reduced bias and better use of animals. Cancer Chemother Pharmacol, 2016. 78(1): p. 131-41. 

D.M. Burns, S. Rana, E.C. Martin, S. Nagra, J. Ward, H. Osman, A.I. Bell, P. Moss, N.H. Russell, C.F. Craddock, C.P. Fox and S. Chaganti, Greatly reduced risk of EBV reactivation in rituximab-experienced recipients of alemtuzumab-conditioned allogeneic HSCT. Bone Marrow Transplant, 2016. 51(6): p. 825-32. 

H. Mehanna, T. Al-Maqbili, B. Carter, E.C. Martin, N. Campain, J. Watkinson, C. McCabe, K. Boelaert and J.A. Franklyn, Differences in the recurrence and mortality outcomes rates of incidental and nonincidental papillary thyroid microcarcinoma: a systematic review and meta-analysis of 21 329 person-years of follow-up. J Clin Endocrinol Metab, 2014. 99(8): p. 2834-43.

 

Working papers

E.C. Martin, A. Gasparini, and M.J. Crowther, merlin: An R package for Mixed Effects Regression for Linear, Nonlinear and User-defined models

E.C. Martin, M.J. Crowther and G. Edgren, Sampling weights for computationally intensive methods using case-cohort designs in big data  

 

Software

E.C. Martin, A. Gasparini, and M.J. Crowther (2018), MERLIN: R package mixed effects regression for linear, non-linear and user-defined models (https://CRAN.R-project.org/package=merlin).

J. Ensor, K.I.E. Snell, and E.C. Martin (2018), PMCALPLOT: Stata module to produce calibration plot of prediction model performance (https://ideas.repec.org/c/boc/bocode/s458486.html).

J. Ensor, R.D. Riley, and E.C. Martin (2018), PMSAMPSIZE: Calculates the minimum sample size required for developing a multivariable prediction model (https://CRAN.R-project.org/package=pmsampsize).

 

Selected presentations

E.C. Martin, G. Edgren, and M.J. Cwother, Sampling weights for computationally intensive methods using case-cohort designs in big. International Society for Clinical Biostatistics 2018, Melboure, Australia

E.C. Martin, L. Aarons, and J.W.T. Yates, Using mixed-effects modelling improves detection of drug-gene interactions in mouse trials. Population Approach Group Europe 2017, Budapest, Hungary

E.C. Martin, L. Aarons, and J.W.T. Yates, Accounting for dropout in xenografted tumour efficacy studies: Integrated endpoint analysis, reduced bias and better use of animals. Non-clinical statistics 2016, Cambridge, UK, 6th October 2016.

E.C. Martin, L. Aarons, and J.W.T. Yates, Models to account for dropouts in xenograft experiments due to tumour burden limit. Pharmacokinetics UK 2015, Chester, UK, 20th November 2015. 

E.C. Martin, L. Aarons, and J.W.T. Yates, Models to account for dropouts in xenograft experiments due to tumour burden limit. Population Approach Group Europe 2015, Lisbon, Portugal

E.C. Martin, L. Aarons, and J.W.T. Yates, Designing More Efficient Preclinical Experiments: A Simulation Study in Chemotherapy-Induced Myelosupression. Population Approach Group Europe 2014, Crete, Greece

 

 

Share this page: