Dr Darren C Greenwood

Dr Darren C Greenwood

Senior Lecturer in Biostatistics

0113 343 1813

Summary: The development and application of statistical methods for advancing epidemiological research. Specifically hierarchical modelling focusing on perinatal & nutrition epidemiology & meta-analysis.

Location: Division of Biostatistics & Leeds Institute of Data Analytics, Worsley Building

Teaching Commitments: I am module lead for the following postgraduate modules: MSc in Health Research: MEDR5120M, Analytic research (twice a year). MEDR5130M, Intervention research (twice a year). MEDR5140M, Statistical inference in health research. MEDR5145M, Statistical methods in health research. MEDR5150M, Statistical modelling in health research. MSc in Epidemiology & Biostatistics: MEDR5025M, Multlevel & latent variable modelling. I also make substantial contributions to the following postgraduate and undergraduate modules: MSc in Health Research: MEDR5120M, Analytic research (twice a year). MEDR5130M, Intervention research (twice a year). MSc in Epidemiology & Biostatistics: EPIB5001M, Research project. EPIB5034M, Mini-project. Masters in Public Health / International Health: MEDR5200M, Health research methods. MSc in Child Health: CPCH5007M, Research methods and medical statistics. MbChB: Research, Evaluation & Special Studies module 2 (RESS2). Research, Evaluation & Special Studies module 3 (RESS3). Extended Student-led Research or Evaluation Project (ESREP). I currently supervise 6 PhD students.

Overview

Qualifications

BSc, MSc, PhD, PGCLTHE

Research Area

The development and application of statistical methods for advancing epidemiological research. Specifically hierarchical modelling focusing on perinatal & nutrition epidemiology & meta-analysis. Current research includes meta-analysis of observational studies, statistical methods applied to perinatal & nutrition epidemiology, life course epidemiology, pooling individual participant data across several epidemiological studies, handling measurement error, missing data problems including multiple imputation. Most of these issues can be conveniently dealt with within a Bayesian framework.