Analysis of trials, meta-analyses and observational studies
Lead Research Organisation:
University College London
Department Name: UNLISTED
Abstract
We cover issues such as survival analysis methods that allow for non-proportional hazards in both trials and IPD meta-analyses, modelling of prognostic and predictive factors, and analysis of longitudinal and clustered data. On the application of causal models, CTU trials are a rich resource for evaluating aspects of patient management other than the randomised comparison, such as the impact of second-line or concomitant treatments. Although often left to clinician discretion, the trial will usually collect information on the basis for these decisions. Exploiting such data in a major causal analysis of the DART trial in HIV, we showed that 24-weekly and 12 weekly CD4 monitoring give similar results, and that a single CD4 count at 48 weeks leads to better survival than no CD4 monitoring. Finally, on missing data, even modest amounts of missing data can lead to bias and make study conclusions unreliable and/or imprecise. Some methods to deal with it can lead to further bias or imprecision, yet prevail in many disease areas, and are recommended by some regulators. In collaboration with the London School of Hygiene and Tropical Medicine (LSHTM), we have proposed a new framework for the analysis of clinical trials with missing data, which has been adopted by a Drug Information Association working group and for a pharmaceutical company regulatory submission.
Technical Summary
Appropriate analysis of trials is vital to obtain a full return on the investment made through efficient design and excellent conduct. However, this is often far from straightforward. We are focusing on those which arise directly from our clinical studies. Thus we are investigating how to analyse multi-arm multi-stage (MAMS) trials, and how best to analyses time-to-event outcomes and recurrent events within trials, as well as using causal models to answer questions not addressed by randomisation. We are also examining the analysis, validation and handling of missing data in multivariable prognostic models; and one versus two-stage and network meta-analysis approaches using individual participant data. Clustered data often arise in trials and observational studies so another focus is the Appropriate analysis of longitudinal and clustered data.
Organisations
- University College London (Lead Research Organisation)
- UNIVERSITY OF LEICESTER (Collaboration)
- National Institute of Health and Medical Research (INSERM) (Collaboration)
- National Institute for Health Research (Collaboration)
- Association of the British Pharmaceutical Industry (Collaboration)
- UNIVERSITY OF CAMBRIDGE (Collaboration)
- IMPERIAL COLLEGE LONDON (Collaboration)
- Health Data Research UK (Collaboration)
- UNIVERSITY OF OXFORD (Collaboration)
- AstraZeneca (United Kingdom) (Collaboration)
- QUEEN MARY UNIVERSITY OF LONDON (Collaboration)
- Alan Turing Institute (Collaboration)
- Amgen Inc (Collaboration)
- University of Bern (Collaboration)
- BANGOR UNIVERSITY (Collaboration)
- Albert Ludwig University of Freiburg (Collaboration)
- University of Toronto (Collaboration)
- UNIVERSITY OF LEEDS (Collaboration)
- GlaxoSmithKline (GSK) (Collaboration)
- Medical Research Council (MRC) (Collaboration)
- UNIVERSITY OF LIVERPOOL (Collaboration)
- KING'S COLLEGE LONDON (Collaboration)
- Monash University (Collaboration)
- University of Bristol (Collaboration)
People |
ORCID iD |
Publications
Xie W
(2019)
A Systematic Review and Recommendation for Reporting of Surrogate Endpoint Evaluation Using Meta-analyses.
in JNCI cancer spectrum
Wood AM
(2015)
The estimation and use of predictions for the assessment of model performance using large samples with multiply imputed data.
in Biometrical journal. Biometrische Zeitschrift
Wilson E
(2017)
Internet-accessed sexually transmitted infection (e-STI) testing and results service: A randomised, single-blind, controlled trial.
in PLoS medicine
WHO Rapid Evidence Appraisal For COVID-19 Therapies (REACT) Working Group
(2020)
Association Between Administration of Systemic Corticosteroids and Mortality Among Critically Ill Patients With COVID-19: A Meta-analysis.
in JAMA
White IR
(2020)
A causal modelling framework for reference-based imputation and tipping point analysis in clinical trials with quantitative outcome.
in Journal of biopharmaceutical statistics
White IR
(2018)
A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials.
in Statistica Sinica
White IR
(2019)
A comparison of arm-based and contrast-based models for network meta-analysis.
in Statistics in medicine
White IR
(2019)
Meta-analysis of non-linear exposure-outcome relationships using individual participant data: A comparison of two methods.
in Statistics in medicine
White I
(2020)
Handbook of Meta-Analysis
| Description | IW and BK on EIWG |
| Geographic Reach | Europe |
| Policy Influence Type | Participation in a guidance/advisory committee |
| URL | https://www.efspi.org/EFSPI/Working_Groups/EFSPI_EFPIA_EIWG.aspx |
| Description | IW on AGS trial DMC |
| Geographic Reach | National |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Description | IW on MHCOVID steering committee |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Membership of a guideline committee |
| URL | https://mhcovid.ispm.unibe.ch/ |
| Description | Missing data course |
| Geographic Reach | Local/Municipal/Regional |
| Policy Influence Type | Influenced training of practitioners or researchers |
| Description | Missing data course (Philadelphia) |
| Geographic Reach | Local/Municipal/Regional |
| Policy Influence Type | Influenced training of practitioners or researchers |
| Description | Missing data course: LSHTM |
| Geographic Reach | Local/Municipal/Regional |
| Policy Influence Type | Influenced training of practitioners or researchers |
| Description | Missing data course: Swiss Winter Epidemiology School |
| Geographic Reach | Europe |
| Policy Influence Type | Influenced training of practitioners or researchers |
| Description | Missing data course: focus on electronic health record data |
| Geographic Reach | Local/Municipal/Regional |
| Policy Influence Type | Influenced training of practitioners or researchers |
| Description | Cost-effectiveness analysis with informative missing data: tools and strategies |
| Amount | £283,777 (GBP) |
| Funding ID | DRF-2015-08-047 |
| Organisation | National Institute for Health Research |
| Department | NIHR Fellowship Programme |
| Sector | Public |
| Country | United Kingdom |
| Start | 10/2015 |
| End | 09/2018 |
| Description | Cross-Unit Appointment: Using causal analyses to add value to large RCTs |
| Amount | £185,000 (GBP) |
| Organisation | Medical Research Council (MRC) |
| Department | MRC Population Health Sciences Research Network (PHSRN) |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 04/2014 |
| End | 03/2018 |
| Description | MRC Methodology Research Panel |
| Amount | £495,140 (GBP) |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 10/2018 |
| End | 10/2021 |
| Description | MRC Methodology Research Panel: Missing data in propensity score analyses of Electronic Health Records Data |
| Amount | £400,000 (GBP) |
| Funding ID | MR/M013278/1 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2015 |
| End | 09/2018 |
| Description | MRC Methodology Research Panel: Multiple imputation by chained equations for data that are missing not at random |
| Amount | £164,000 (GBP) |
| Funding ID | MR/M025012/1 |
| Organisation | Medical Research Council (MRC) |
| Department | MRC/NIHR Methodology Research Programme |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 02/2016 |
| End | 01/2019 |
| Description | NIHR Development and Skills Enhancement Award |
| Amount | £35,175 (GBP) |
| Funding ID | NIHR301653 |
| Organisation | National Institute for Health Research |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2021 |
| End | 03/2022 |
| Description | Alan Turing Institute and AI |
| Organisation | Alan Turing Institute |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | This collaboration is in two parts: clinical trial monitoring, and treatment effect heterogeneity. 1. We have described the problems in monitoring of clinical trial data. We are supplying data sets, information about monitoring and on-hand expertise to allow an exploration of the use of AI. 2. For the treatment effect heterogeneity project, we are supplying our expertise on treatment effect estimation in clinical trials, new work on performance measures for treatment effect heterogeneity, and data from a trial in treatmnet of severe anaemia in African children |
| Collaborator Contribution | Scientists of the Alan Turing Institute are using AI including ML on our clinical trial data and on simulated data to find out what methods can be used in the two settings above. A data study group has been run on the monitoring project (entered separately in Research Fish). |
| Impact | A data study group was run in November and December 2021, allowing a group of volunteers to work intensively on the monitoring problem. |
| Start Year | 2019 |
| Description | Alan Turing Institute and AI |
| Organisation | Health Data Research UK |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | This collaboration is in two parts: clinical trial monitoring, and treatment effect heterogeneity. 1. We have described the problems in monitoring of clinical trial data. We are supplying data sets, information about monitoring and on-hand expertise to allow an exploration of the use of AI. 2. For the treatment effect heterogeneity project, we are supplying our expertise on treatment effect estimation in clinical trials, new work on performance measures for treatment effect heterogeneity, and data from a trial in treatmnet of severe anaemia in African children |
| Collaborator Contribution | Scientists of the Alan Turing Institute are using AI including ML on our clinical trial data and on simulated data to find out what methods can be used in the two settings above. A data study group has been run on the monitoring project (entered separately in Research Fish). |
| Impact | A data study group was run in November and December 2021, allowing a group of volunteers to work intensively on the monitoring problem. |
| Start Year | 2019 |
| Description | Alan Turing Institute and AI - Data Study Group |
| Organisation | Alan Turing Institute |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | We have described the problems in monitoring of clinical trial data. Phase III clinical trials are typically multicentre (50-200 sites) and recruit several hundred patients (300-10000). ICH GCP E6(R2) say "Clinical trialists monitor trial data in order to protect the rights and well-being of participants, to ensure that the trial data are accurate, complete, and verifiable, and to confirm that the trial is being run in compliance with the currently approved protocol, with the principles of good clinical practice (GCP), and with the relevant regulatory requirements". This monitoring can take 25% of the CTU trial budget. With risk based monitoring, we consider the risks to the patients and the trial and devise the monitoring to reduce or mitigate these risks. It may be more efficient to use AI (or ML) to look at the full dataset and find what data areas and sites we need to target, rather than use our ideas of risk and solution. We are supplying data sets, information about monitoring and on-hand expertise to allow an exploration of the use of AI. |
| Collaborator Contribution | ATI will provide funding for project work for the week. Scientists of the Alan Turing Institute will use AI including ML on our clinical trial data to find out what data areas and sites we need to approach to improve the clinical trial data. We will have monitoring experts from the clinical trials unit on hand to give any explanations required and check the process is on track. |
| Impact | The data study group was run in November and December 2021, allowing a group of volunteers to work intensively on the monitoring problem. A report is currently being written. |
| Start Year | 2021 |
| Description | Alan Turing Institute and AI - Data Study Group |
| Organisation | Health Data Research UK |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | We have described the problems in monitoring of clinical trial data. Phase III clinical trials are typically multicentre (50-200 sites) and recruit several hundred patients (300-10000). ICH GCP E6(R2) say "Clinical trialists monitor trial data in order to protect the rights and well-being of participants, to ensure that the trial data are accurate, complete, and verifiable, and to confirm that the trial is being run in compliance with the currently approved protocol, with the principles of good clinical practice (GCP), and with the relevant regulatory requirements". This monitoring can take 25% of the CTU trial budget. With risk based monitoring, we consider the risks to the patients and the trial and devise the monitoring to reduce or mitigate these risks. It may be more efficient to use AI (or ML) to look at the full dataset and find what data areas and sites we need to target, rather than use our ideas of risk and solution. We are supplying data sets, information about monitoring and on-hand expertise to allow an exploration of the use of AI. |
| Collaborator Contribution | ATI will provide funding for project work for the week. Scientists of the Alan Turing Institute will use AI including ML on our clinical trial data to find out what data areas and sites we need to approach to improve the clinical trial data. We will have monitoring experts from the clinical trials unit on hand to give any explanations required and check the process is on track. |
| Impact | The data study group was run in November and December 2021, allowing a group of volunteers to work intensively on the monitoring problem. A report is currently being written. |
| Start Year | 2021 |
| Description | Collaborative research with MRC BSU |
| Organisation | University of Cambridge |
| Department | MRC Biostatistics Unit |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Joint collaborative research in design and analysis of clinical trials |
| Collaborator Contribution | Joint collaborative research in design and analysis of clinical trials |
| Impact | Several papers including PUBMED id: 19153970; 19452569; 21225900 |
| Description | Collaborative research with the University of Freiburg |
| Organisation | Albert Ludwig University of Freiburg |
| Department | Centre for Medical Biometry and Medical Informatics |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | Joint collaborative research on the analysis of clinical trials |
| Collaborator Contribution | Joint collaborative research on the analysis of clinical trials |
| Impact | Several papers including PUBMED ID 20191601 |
| Description | Collaborative research with the University of Leicester |
| Organisation | University of Leicester |
| Department | Department of Health Sciences |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Joint collaborative research on analysis of clinical trials and meta-analysis and simulation studies |
| Collaborator Contribution | Joint collaborative research on analysis of clinical trials and meta-analysis and simulation studies |
| Impact | Several papers including in the STATA journal and PUBMED ID 20213719 |
| Start Year | 2016 |
| Description | IW - GSK/Royes |
| Organisation | GlaxoSmithKline (GSK) |
| Country | Global |
| Sector | Private |
| PI Contribution | Supervising post-doc then collaborating in onging project |
| Collaborator Contribution | Supervising post-doc and providing clinical trial data for methodological re-analysis, then collaborating in onging project |
| Impact | Multi-disciplinary - biostatistics and clinical trials/drug development. This project tackles multiple imputation of data after treatment discontinuation. Presentation to GSK's annual UK biostatistics meeting about the results. Paper: Ian R. White, Royes Joseph, Nicky Best. A causal modelling framework for reference-based imputation and tipping point analysis in clinical trials with quantitative outcome. Journal of Biopharmaceutical Statistics 2020:30;334-350. Ongoing work to code the methods in R. |
| Start Year | 2015 |
| Description | IW advisor to Daisy Gaunt |
| Organisation | University of Bristol |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Advised on preparation of a successful NIHR doctoral fellwoship application |
| Collaborator Contribution | Submitted the successful NIHR doctoral fellwoship application |
| Impact | none at present |
| Start Year | 2017 |
| Description | IW advisor to Kara-Louise Royle |
| Organisation | University of Leeds |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Advised on developing and submitting a successful NIHR doctoral fellowship proposal and on carrying out the work |
| Collaborator Contribution | Developed and submitted a NIHR doctoral fellowship proposal and carried out the work |
| Impact | None at present |
| Start Year | 2020 |
| Description | IW advisor to Orestis Efthimiou grant |
| Organisation | University of Bern |
| Country | Switzerland |
| Sector | Academic/University |
| PI Contribution | Advised on development and submission of successful research grant proposal to Swiss National Science Foundation (SNSF). Collaborated on two papers. |
| Collaborator Contribution | Developed and submitted successful research grant proposal to Swiss National Science Foundation (SNSF). Collaborated on two papers. |
| Impact | Two papers published so far. |
| Start Year | 2017 |
| Description | IW advisor to Suzie Cro |
| Organisation | Imperial College London |
| Department | Imperial Clinical Trials Unit (ICTU) |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Advised on development and submission of a successful NIHR Advanced Fellowship application |
| Collaborator Contribution | Developed and submitted the successful NIHR Advanced Fellowship application |
| Impact | Workshop planned April 2022 to improve how UK clinical trials units approach estimands. |
| Start Year | 2019 |
| Description | IW collaborator on SNSF grant, PI Georgia Salanti |
| Organisation | University of Bern |
| Country | Switzerland |
| Sector | Academic/University |
| PI Contribution | Advised on development and submission of successful grant application to SNF. Collaborating on papers. |
| Collaborator Contribution | Developed and submitted successful grant application to SNF. Collaborating on papers. |
| Impact | Multi-disciplinary - epidemiology, systematic review, biostatistics. One publication on treatment hierarchy question. Future impact on conduct of network meta-analysis. |
| Start Year | 2017 |
| Description | IW in ROBIS-NMA group |
| Organisation | University of Bristol |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Contributed expertise in network meta-analysis |
| Collaborator Contribution | Contributed expertise in systematic review and network meta-analysis |
| Impact | Multidisciplinary work - systematic review and biostatistics. Current outputs - two publications on methods for developing the tool. Future outputs - tool for evaluating risk of bias in a network meta-analysis, expected impact on use of network meta-analysis in health policy making |
| Start Year | 2020 |
| Description | IW in ROBIS-NMA group |
| Organisation | University of Toronto |
| Country | Canada |
| Sector | Academic/University |
| PI Contribution | Contributed expertise in network meta-analysis |
| Collaborator Contribution | Contributed expertise in systematic review and network meta-analysis |
| Impact | Multidisciplinary work - systematic review and biostatistics. Current outputs - two publications on methods for developing the tool. Future outputs - tool for evaluating risk of bias in a network meta-analysis, expected impact on use of network meta-analysis in health policy making |
| Start Year | 2020 |
| Description | Methodology Research Collaboration with industry |
| Organisation | Amgen Inc |
| Country | United States |
| Sector | Private |
| PI Contribution | Commitment to developing research activity in to design, conduct or analysis methodology in areas of mutual interest. Detailed discussions ongoing |
| Collaborator Contribution | Commitment to developing research activity in to design, conduct or analysis methodology in areas of mutual interest. Detailed discussions ongoing |
| Impact | None yet |
| Start Year | 2014 |
| Description | Methodology Research Collaboration with industry |
| Organisation | AstraZeneca |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Commitment to developing research activity in to design, conduct or analysis methodology in areas of mutual interest. Detailed discussions ongoing |
| Collaborator Contribution | Commitment to developing research activity in to design, conduct or analysis methodology in areas of mutual interest. Detailed discussions ongoing |
| Impact | None yet |
| Start Year | 2014 |
| Description | Methodology Research Collaboration with industry |
| Organisation | GlaxoSmithKline (GSK) |
| Country | Global |
| Sector | Private |
| PI Contribution | Commitment to developing research activity in to design, conduct or analysis methodology in areas of mutual interest. Detailed discussions ongoing |
| Collaborator Contribution | Commitment to developing research activity in to design, conduct or analysis methodology in areas of mutual interest. Detailed discussions ongoing |
| Impact | None yet |
| Start Year | 2014 |
| Description | Methods for multiple imputation with multiple rating scales |
| Organisation | Bangor University |
| Department | Institute of Medical and Social Care Research |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Expertise and intellectual input |
| Collaborator Contribution | Expertise and intellectual input |
| Impact | Paper published in 2016 |
| Start Year | 2014 |
| Description | Multiple imputation using multilevel models |
| Organisation | National Institute of Health and Medical Research (INSERM) |
| Country | France |
| Sector | Academic/University |
| PI Contribution | Expertise and intellectual input |
| Collaborator Contribution | Expertise and intellectual input |
| Impact | One publication so far; a second in planning (March 2020) |
| Start Year | 2014 |
| Description | NIHR & MRC Trials Methodology Research Parternship Executive Group |
| Organisation | Association of the British Pharmaceutical Industry |
| Country | United Kingdom |
| Sector | Charity/Non Profit |
| PI Contribution | Intellectual input and plans for further collaboration on future projects Member of Executive Group (Coordinated from University of Liverpool) Co-chair of Health Informatics Working Group (Co-chaired from University of Leeds) Co-chair of Statistical Analysis Working Group (Co-chaired from Kings College London) |
| Collaborator Contribution | 25 partner organisations around UK (not all listed at this stage) Intellectual input and plans for further collaboration on future projects |
| Impact | (None yet) |
| Start Year | 2019 |
| Description | NIHR & MRC Trials Methodology Research Parternship Executive Group |
| Organisation | King's College London |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Intellectual input and plans for further collaboration on future projects Member of Executive Group (Coordinated from University of Liverpool) Co-chair of Health Informatics Working Group (Co-chaired from University of Leeds) Co-chair of Statistical Analysis Working Group (Co-chaired from Kings College London) |
| Collaborator Contribution | 25 partner organisations around UK (not all listed at this stage) Intellectual input and plans for further collaboration on future projects |
| Impact | (None yet) |
| Start Year | 2019 |
| Description | NIHR & MRC Trials Methodology Research Parternship Executive Group |
| Organisation | Medical Research Council (MRC) |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | Intellectual input and plans for further collaboration on future projects Member of Executive Group (Coordinated from University of Liverpool) Co-chair of Health Informatics Working Group (Co-chaired from University of Leeds) Co-chair of Statistical Analysis Working Group (Co-chaired from Kings College London) |
| Collaborator Contribution | 25 partner organisations around UK (not all listed at this stage) Intellectual input and plans for further collaboration on future projects |
| Impact | (None yet) |
| Start Year | 2019 |
| Description | NIHR & MRC Trials Methodology Research Parternship Executive Group |
| Organisation | National Institute for Health Research |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | Intellectual input and plans for further collaboration on future projects Member of Executive Group (Coordinated from University of Liverpool) Co-chair of Health Informatics Working Group (Co-chaired from University of Leeds) Co-chair of Statistical Analysis Working Group (Co-chaired from Kings College London) |
| Collaborator Contribution | 25 partner organisations around UK (not all listed at this stage) Intellectual input and plans for further collaboration on future projects |
| Impact | (None yet) |
| Start Year | 2019 |
| Description | NIHR & MRC Trials Methodology Research Parternship Executive Group |
| Organisation | University of Leeds |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Intellectual input and plans for further collaboration on future projects Member of Executive Group (Coordinated from University of Liverpool) Co-chair of Health Informatics Working Group (Co-chaired from University of Leeds) Co-chair of Statistical Analysis Working Group (Co-chaired from Kings College London) |
| Collaborator Contribution | 25 partner organisations around UK (not all listed at this stage) Intellectual input and plans for further collaboration on future projects |
| Impact | (None yet) |
| Start Year | 2019 |
| Description | NIHR & MRC Trials Methodology Research Parternship Executive Group |
| Organisation | University of Liverpool |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Intellectual input and plans for further collaboration on future projects Member of Executive Group (Coordinated from University of Liverpool) Co-chair of Health Informatics Working Group (Co-chaired from University of Leeds) Co-chair of Statistical Analysis Working Group (Co-chaired from Kings College London) |
| Collaborator Contribution | 25 partner organisations around UK (not all listed at this stage) Intellectual input and plans for further collaboration on future projects |
| Impact | (None yet) |
| Start Year | 2019 |
| Description | NIHR & MRC Trials Methodology Research Parternship Executive Group |
| Organisation | University of Oxford |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Intellectual input and plans for further collaboration on future projects Member of Executive Group (Coordinated from University of Liverpool) Co-chair of Health Informatics Working Group (Co-chaired from University of Leeds) Co-chair of Statistical Analysis Working Group (Co-chaired from Kings College London) |
| Collaborator Contribution | 25 partner organisations around UK (not all listed at this stage) Intellectual input and plans for further collaboration on future projects |
| Impact | (None yet) |
| Start Year | 2019 |
| Description | Re-randomisation of patients within a trial |
| Organisation | Monash University |
| Country | Australia |
| Sector | Academic/University |
| PI Contribution | Intellectual input to original design and resulting paper; currently working on follow up projects to demonstrate strengths and weaknesses and to understand practicalities of the design. |
| Collaborator Contribution | Intellectual input to original design and resulting paper; currently working on follow up projects to demonstrate strengths and weaknesses and to understand practicalities of the design. |
| Impact | Several invited seminars (Leeds, Leicester, LSHTM), conference papers, one published article. |
| Start Year | 2014 |
| Description | Re-randomisation of patients within a trial |
| Organisation | Queen Mary University of London |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Intellectual input to original design and resulting paper; currently working on follow up projects to demonstrate strengths and weaknesses and to understand practicalities of the design. |
| Collaborator Contribution | Intellectual input to original design and resulting paper; currently working on follow up projects to demonstrate strengths and weaknesses and to understand practicalities of the design. |
| Impact | Several invited seminars (Leeds, Leicester, LSHTM), conference papers, one published article. |
| Start Year | 2014 |
| Description | Strengthening the Analytical Thinking for Observational Studies: STRATOS initiative |
| Organisation | Albert Ludwig University of Freiburg |
| Department | Centre for Medical Biometry and Medical Informatics |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | Member of the steering committee (James Carpenter) and co-author of the paper in Statistics in Medicine which introduced this initiative Dr Tim Morris is a member of the simulation panel and the visualisation panel. |
| Collaborator Contribution | Besides steering group membership, James Carpenter is chair of the Topic Group on missing data, responsible for leading the activities of this group |
| Impact | Two papers have been published |
| Start Year | 2014 |
| Description | Using causal analyses to add value to large RCTs |
| Organisation | University of Bristol |
| Department | MRC Centre for Causal Analyses in Translational Epidemiology |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | To help develop "best practice" examples to guide statisticians working in clinical academia in the use of causal methods To help systematically survey the clinical literature, and to conduct a scoping exercise to identify types of questions which causal models have most potential to answer in RCTs To help apply causal inference methods to use RCT data to answer two new clinical questions of different types To help disseminate best practice through workshops etc |
| Collaborator Contribution | To help develop "best practice" examples to guide statisticians working in clinical academia in the use of causal methods To help systematically survey the clinical literature, and to conduct a scoping exercise to identify types of questions which causal models have most potential to answer in RCTs To help apply causal inference methods to use RCT data to answer two new clinical questions of different types To help disseminate best practice through workshops etc |
| Impact | Funding from the MRC Population and Health Sciences Network (to fund a cross-unit appointment for a joint appointment between CTU and CaiTE) commenced in April 2014 (until 2017) |
| Start Year | 2012 |
| Title | 'metan': module for fixed and random effects meta-analysis |
| Description | The routines in this package provide facilities to conduct meta-analyses of binary (event) or continuous data from two groups, or intervention effect estimates with corresponding standard errors or confidence intervals. This is an updated version of metan as published in Stata Journal Issue 8, and prior to that in STB-44, authored by Michael J Bradburn, Jonathan J Deeks and Douglas G Altman. Updates include a wide range of random-effects models; cumulative and influence analysis; meta-analysis of proportions; and better handling of heterogeneity, continuity correction and returned values. The routine for constructing forest plots has been separated off ('forestplot' command) and hugely extended; extremely flexible and generalised forest plots may now be produced. |
| Type Of Technology | Software |
| Year Produced | 2020 |
| Impact | The 'metan' package was originally developed by a group of prominent meta-analysis researchers including Profs. Jonathan Sterne, Jonathan Deeks and Douglas Altman; and was for many years the only meta-analysis package available for Stata. However, for a decade the package has not been maintained due to lack of resources, whilst the meta-analysis field has continued to evolve. The latest version of Stata 16 now has an official meta-analysis suite, but this is not available to users of older versions and is limited in scope. The update to the 'metan' package described in this Record, with vastly increased functionality, has the full blessing of the original authors; and therefore represents a comprehensive, peer-reviewed and validated update to a package which continues to be downloaded an order of magnitude greater than any other user-written meta-analysis package, and which is used in multiple courses and textbooks. |
| URL | https://www.statalist.org/forums/forum/general-stata-discussion/general/1585265-metan-a-comprehensiv... |
| Title | Jomo |
| Description | R package for multilevel joint modelling imputation |
| Type Of Technology | Software |
| Year Produced | 2016 |
| Open Source License? | Yes |
| Impact | Use of the jomo method by applied researchers |
| URL | https://cran.r-project.org/web/packages/jomo/jomo.pdf |
| Title | R package for multiple imputation of missing data in IPD meta-analysis |
| Description | This is a software package developed in R that enables multiple imputation of missing data in IPD meta-analysis |
| Type Of Technology | Software |
| Year Produced | 2015 |
| Impact | No impacts as yet |
| Title | Software for substantive model compatible multiple imputation |
| Description | Statistical software for performing multiple imputation, consistent with the assumptions made by the substantive statistical analysis model. |
| Type Of Technology | Software |
| Year Produced | 2015 |
| Open Source License? | Yes |
| Impact | None so far. |
| URL | http://www.missingdata.org.uk |
| Title | Stata package for reference based sensitvity analysis for longitudinal trials with protocol deviation via multiple imputation |
| Description | This software implements, in the statistical software package Stata, Reference Based Sensitivity Analysis for missing outcome data in clinical trials. Within Stata, simply type ssc install mimix to install the software |
| Type Of Technology | Software |
| Year Produced | 2016 |
| Impact | None yet |
| Title | fp_select: model selection for univariable fractional polynomials |
| Description | The fp_select routine to perform model selection for univariable fractional polynomial models |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Impact | None yet - just released |
| Title | mfpa: extension of mfp using the acd covariate transformation for enhanced parametric multivariable modelling |
| Description | An extension of mfp using the acd covariate transformation for enhanced parametric multivariable modelling |
| Type Of Technology | Software |
| Year Produced | 2016 |
| Impact | Use of this software |
| URL | http://www.homepages.ucl.ac.uk/~ucakjpr/stata |
| Title | network software for Stata |
| Description | The -network- module for the statistical package Stata is an easy-to-use package to handle data for network meta-analysis and to fit consistency and inconsistency models (via calls to the -mvmeta- module). |
| Type Of Technology | Software |
| Year Produced | 2014 |
| Open Source License? | Yes |
| Impact | Widely used, as judged from - large number of emails received about it. - has featured in various publications including two in Chinese: http://www.cjebm.org.cn/Upload/PaperUpLoad/b24bfc39-bb0d-41b9-9f0f-93c3ba90353b.pdf and http://www.cjebm.org.cn/oa/DArticle.aspx?type=view&id=20150818. - used in ~40% of network meta-analyses published in major medical journals: I estimate it has been used in 100-200 published works - often used without explicit citation. |
| URL | http://www.stata-journal.com/article.html?article=st0410 |
| Description | Cochrane SMG |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Invited to present recent research at a meeting of the Cochrane Collaboration's Statistical Mrthods Group. |
| Year(s) Of Engagement Activity | 2018 |
| Description | EME workshop |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | 30 professionals attended a training course on efficacy and mechanisms evaluation with relevance to the MRC board of the same name. |
| Year(s) Of Engagement Activity | 2017,2018,2019 |
| Description | IW - Bern causal inference course |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The Swiss network of trial statisticians invited me to give a course on causal inference in RCTs. ~20 people attended and reported learning about new methods of anlaysis. |
| Year(s) Of Engagement Activity | 2020 |
| Description | Marie Curie workshop |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | 40 people from a range of backgrounds attended a workshop "Missing Data in Palliative Care Studies" at which Ian White spoke. This led to a draft document for use by the Marie Curie charity and others. |
| Year(s) Of Engagement Activity | 2017 |
| Description | Member of trial steering committee, POSNOC trial |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | Yes |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Appointed member of trial steering committee, POSNOC trial (2014-2024): POSNOC - A randomised trial of armpit (axilla) treatment for women with early stage breast cancer. The POSNOC trial will provide evidence relevant to patients and to the NHS. The protocol has been designed to integrate into current NHS practice. The hypothesis of the POSNOC trial is that low axillary tumour burden patients (clinically and ultrasound negative) with macrometastases in 1 or 2 SNs, receiving systemic therapy, would have non-inferior outcomes whether they are randomised to adjuvant therapy alone or adjuvant therapy plusaxillary treatment (ANC or ART) |
| Year(s) Of Engagement Activity | 2014,2015,2016,2017,2018,2019,2020,2021 |
| URL | http://www.nets.nihr.ac.uk/__data/assets/pdf_file/0004/111469/PRO-12-35-17.pdf |
| Description | Multiple Imputation Course |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Each course is a 2 day course for 30 scientists needing to tackle missing data in their research. Feedback has always been excellent. Many of the attendees have gone on to use multiple imputation. We haved gone on to write two methodological papers with people who have attended the course (doi 10.1186/s13104-016-1853-5; 2nd accepted but not yet published). We have also written a highly cited tutorial paper (>1000 citations at March 2016) based in the material in the course and our experiences of teaching the course. Course has now been repeated successfully 9 times in Cambridge and once (as a 1-day course) in Birmingham. |
| Year(s) Of Engagement Activity | 2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020 |
| URL | https://www.ucl.ac.uk/clinical-trials-and-methodology/education/short-courses/missing-data |
| Description | PSI course 2019 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | 50 members of PSI (European Statistical Organisation focussing on pharmaceutical industry) attended a pre-confernece course on "Demystifying causal inference" |
| Year(s) Of Engagement Activity | 2018 |
| Description | Paris NMA course |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | 25 professionals attended a 3-day course on network meta-analysis methods given by experts from UK, France and Germany. |
| Year(s) Of Engagement Activity | 2019 |
| URL | http://livenetworkmetaanalysis.com/nma-training/ |
| Description | Simulations course |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | We gave a one-day course on "Using simulation studies to evaluate statistical methods". The audience were researchers either using or planning to use simulation studies in order to develop or evaluate statistical methods. Feedback indicated that the audience felt empowered to start or improve their use of simulation studies. We repeated the course in subsequent years as a 2-day course and also (from 2020) online. |
| Year(s) Of Engagement Activity | 2015,2016,2017,2018,2019,2020,2021,2022 |
| URL | https://sites.google.com/site/simulationstudies/home |
| Description | UMIT course |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | A series of lectures and practicals on Treatment Switching in Randomised Trials was given in Hall, Austria as part of a 4-day course on Causal Inference Methods. Repeated annually including online in 2021 - 2023 as a 5-day course. |
| Year(s) Of Engagement Activity | 2016,2017,2018,2019,2020,2021,2022,2023 |
| URL | https://www.umit.at/page.cfm?vpath=departments/public_health/htads-continuing-education-program/caus... |