Dr Suzanne Freeman

I am a Lecturer in Biostatistics based in the Department of Population Health Sciences. My main research interests include network meta-analysis, individual participant data meta-analysis and synthesis of continuous and time-to-event outcomes. 

I hold an NIHR Post-Doctoral Research Fellowship investigating evidence synthesis methods for continuous and time-to-event outcomes with the aim to improve the proportion of clinical evidence that informs health technology assessments and improve the reliability of downstream decision making.

I am a member of the NIHR Complex Reviews Support Unit (CRSU) where I provide advice and support to NIHR-funded researchers with complex evidence synthesis problems including both practical application of methodology and development of statistical methodology.

I am a Fellow of the Higher Education Academy and currently teach on the MSc in Medical Statistics. 

My main research areas are:
- Network meta-analysis
- Component network meta-analysis
- Individual participant data meta-analysis and network meta-analysis
- Synthesis of time-to-event and continuous outcomes
- Diagnostic test accuracy meta-analysis
- Health technology assessment
- Bayesian analysis
- Clinical Trials

Selected publications:
1. Quinn T, Burton JK, Carter B, Cooper NJ, Dwan K, Field R, Freeman SC, Geue C, Hsieh PH, McGill K, Nevill CR, Rana D, Sutton A, Rowan MT, Xin, Y. Following the Science? Comparison of the methodological and reporting quality of covid-19 and other research. BMC Medicine 2021; 19: 46, DOI: https://doi.org/10.1186/s12916-021-01920-x
2. Hartmann-Boyce J, Livingstone-Banks J, Ordóñez-Mena JM, Fanshawe TR, Lindson N, Freeman SC, Sutton AJ, Theodoulou A, Aveyard P. Behavioural interventions for smoking cessation: An overview and network meta-analysis. Cochrane Database of Systematic Reviews 2020, Issue 12. DOI: https://doi.org/10.1002/14651858.CD013229.pub2
3. Freeman SC, Sutton AJ, Cooper NJ. Uptake of methodological advances for synthesis of continuous and time-to-event outcomes would maximise use of the evidence base. Journal of Clinical Epidemiology. 2020; 124: 94-105, DOI: https://doi.org/10.1016/j.jclinepi.2020.05.010
4. Doleman B, Freeman SC, Lund JN, William JP, Sutton AJ. Funnel plots may show asymmetry in the presence of publication bias with continuous outcomes dependent on baseline risk: presentation of a new publication bias test. Research Synthesis Methods. 2020; 11: 522-534, DOI: https://doi.org/10.1002/jrsm.1414
5. Freeman SC, Fisher D, Carpenter JR, White I. Identifying inconsistency in network meta-analysis: Is the net heat plot a fake friend? Statistics in Medicine. 2019; 38: 5547-5564, DOI: https://doi.org/10.1002/sim.8383
6. Freeman SC, Kerby CR, Patel A, Cooper NJ, Quinn T, Sutton AJ. Development of an interactive web-based tool to conduct and interrogate meta-analysis of diagnostic test accuracy studies: MetaDTA. BMC Medical Research Methodology. 2019; 19: 81, DOI: https://doi.org/10.1186/s12874-019-0724-x
7. Freeman SC, Scott NW, Powell R, Johnston M, Sutton AJ, Cooper NJ. Utilising component network meta-analysis to identify the most effective components of psychological preparation for adults undergoing surgery under general anaesthesia. Journal of Clinical Epidemiology 2018; 98: 105, DOI:  https://doi.org/10.1016/j.jclinepi.2018.02.012
8. Freeman SC, Fisher D, Tierney JF, Carpenter JR. A framework for identifying treatment-covariate interactions in individual participant data network meta-analysis. Research Synthesis Methods 2018; 9: 393-407, DOI: https://doi.org/10.1002/jrsm.1300 
9. Freeman SC, Carpenter JR. Bayesian one-step IPD network meta-analysis of time-to-event data using Royston-Parmar models. Research Synthesis Methods 2017; 8: 451, DOI: https://doi.org/10.1002/jrsm.1253
10. Fisher D, Carpenter J, Morris T, Freeman SC, Tierney J. Meta-analytical methods to identify who benefits most from treatments: daft, deluded, or deft approach? BMJ 2017; 356; j573, DOI: https://doi.org/10.1136/bmj.j573

I currently supervise one PhD student:
- Ellesha Smith - Complex evidence synthesis using network meta-analysis, individual participant data and multiple outcomes

I can supervise PhD students in the areas of:
- Network meta-analysis
- Component network meta-analysis
- Individual participant data meta-analysis and network meta-analysis
- Synthesis of time-to-event and continuous outcomes
- Clinical Trials

As part of the MSc in Medical Statistics I currently teach on the following modules: Fundamentals of Medical Statistics Advanced Statistical Modelling.

Evidence synthesis methodology

Selected conferences:

Freeman SC, Cooper, NJ, Sutton AJ, Crowther MJ, Carpenter, JR, Hawkins N. Evidence synthesis of time-to-event outcomes in the presence of non-proportional hazards. International Society of Clinical Biostatistics, 2021

Freeman SC, Patel A, Kerby CR, Cooper NJ, Quinn T, Sutton AJ. MetaDTA: An interactive web-based tool for meta-analysis of diagnostic test accuracy studies. Evidence Synthesis & Meta-Analysis in R, 2021

Patel A, Cooper, NJ, Freeman SC, Sutton AJ. Graphical enhancements to summary receiver operating characteristic plots to facilitate diagnostic test accuracy meta-analysis. Methods for Evaluation of Medical Prediction Models, Tests and Biomarkers, 2020

Freeman SC, Sutton AJ, Cooper NJ. A review of the methodology used to synthesise continuous and time-to-event outcomes for clinical and cost-effectiveness. Health Technology Assessment International, 2020

Freeman SC, Kerby C, Cooper NJ, Sutton AJ. An interactive web application to aid diagnostic test accuracy meta-analysis. Cochrane Colloquium, 2018