Adrian Vickers, PhD

Director, Data Analytics and Design Strategy
Adrian Vickers
Practice Area:
Manchester, United Kingdom

PhD, Ecology and Applied Statistics
Sheffield Hallam University, Sheffield, United Kingdom

MSc, Ecology
University of Aberdeen, Aberdeen, United Kingdom

BSc, Biology
University of York, York, United Kingdom

Adrian Vickers, PhD, is a Statistics Director with RTI-HS. He has 18 years of experience as a statistician within various consulting environments. Dr. Vickers is experienced in the application of novel and standard statistical methodologies to large medical/biological data sets in diverse therapeutic areas. His role at RTI HS includes planning, executing, and interpreting the analysis of a variety of studies, including health economics and clinical trials. At RTI-HS, Dr. Vickers mainly served as the lead statistician on a variety of different projects and also has led and managed a number of projects, including systematic review and meta-analysis projects to support National Institute for Health and Care Excellence and Scottish Medicines Consortium submissions, quality-of-life analyses of clinical trial data, and medical database analyses. Dr. Vickers has conducted many types of statistical analyses, such as network meta-analyses (frequentist and Bayesian methods) for a variety of different outcomes (binomial, ordinal, change from baseline, hazard ratios, and network survival meta-analyses), including different types of methods to assess heterogeneity (statistical measures of heterogeneity, Bayesian inconsistency model, and node-splitting); survival analyses with a range of extrapolation methods; cross-validation techniques; quality-of-life analyses; and parametric and multivariate modeling, including time series generalized additive mixed models. In addition, Dr. Vickers is an experienced R programmer. His previous positions have involved the statistical analysis and design of clinical trials, including longitudinal studies and analysis of market research data, and designing, analyzing, and building models for various types of conjoint studies.