Title: Statistical Methodology Programme
Funder: Medical Research Council

Abstract: Statistical methodology research in the CTU is driven by problems arising from the ongoing clinical research programmes and from March 2009 has formed part of the activity of the London Hub for Trials Methodology Research based at CTU. The careful, and where appropriate innovative, design and conduct of a trial or study lead to high quality data and hence results which are reliable, clear and understandable. However, expert and often innovative analyses are fundamental to optimal use of the data and to proper interpretation of the results, and finally, therefore, to maximum - including spaces impact on clinical practice. Furthermore, scientific questions may arise post hoc from trials (especially those with complex designs and several endpoints), and additional or novel analyses may give valuable insights beyond the original remit. Knowledge and sometimes development of appropriate methodology is therefore paramount. In general, statistical research at CTU may involve comparing current statistical methods relevant to a particular scientific issue, updating or developing new methods if required, assessing advantages and disadvantages, and recommending methods appropriate for use in clinical settings. The results of the research are reported in peer-reviewed clinical and/or biostatistical journals. Where necessary methods will be implemented as computer software within a standard statistical package disseminated widely through the medical statistics community, for example as freeware through the Stata Journal or the Web resource Statlib [see below for examples].|Current research themes include:|1) The use and extension of methods known as causal modelling in the analysis of trials and observational studies. For example, an analysis was performed with data from the DART trial to assess the causal impact of cotrimoxazole on mortality.||2) Methods to develop and validate prognostic and predictive models, under the general aim of stratified medicine. For example, methods were developed to create a single model concerning superficial bladder cancer from several MRC and EORTC trials and then validate that model with respect to the nine datasets||3) Developments in the design of trials, including multi-arm multi-stage trials, such as the ICON6 trial in ovarian cancer and the STAMPEDE trial in prostate cancer, and sample size calculation for complex designs, research which led to a freely available Stata package (ART Assessment of Resources for Trials), which has been adopted as a standard trial design tool at the CTU.||4) Methods to deal with missing data, including multiple imputation methods implemented in Stata package, and combined imputation and weighting methods for drop-out which when applied to the INITIO trial provided some reassurance that drop-out had not substantially affected the analysis of primary outcome.||5) More-flexible parametric methods to analyse survival data. Recent research has resulted in a paper proposing a new way of assessing treatment effects in trials with time-to-event outcomes but non-proportional hazards.||6) Extending current methods for the analysis of longitudinal data, for example we developed a method to assess how HIV-1 virus subtype influences CD4 response to starting HIV treatment


Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: CANCER AND NEOPLASMS;INFECTION