I’ve been working on RWE most of my career and the clinical trial data for a while now. And 20 years ago, we would have never thought about merging them together but there’s a lot of opportunities if we do this.
The recent advances in analytical methods for combining evidence from RCTs and non-RCTs, and the development of new frameworks for the inclusion of RWE in HTA have provided a greater insight on how issues around RWE uncertainty can be dealt with when estimating treatment effects for new technologies.
Join our discussion today as Thomas and I talk about this.
Never miss an episode!
Join thousends of your peers and subscribe to get our latest updates by email!
Get the
Learn on demand
Click on the button to see our Teachble Inc. cources.
Featured courses
Click on the button to see our Teachble Inc. cources.
Thomas Debray
Founder and Owner of Smart Data Analysis
Thomas Debray is an assistant professor specialized in prognosis research and evidence synthesis. He develops theoretical approaches, practical guidelines, training modules and statistical software to conduct meta-analysis of individual participant data (IPD) and published studies. Thomas currently leads various international projects involving the meta-analysis of IPD and the development of statistical methods for analyzing data from pooled cohort studies.
Key references:
- Debray TPA, Moons KGM, van Valkenhoef G, Efthimiou O, Hummel N, Groenwold RHH, et al. Get real in individual participant data (IPD) meta-analysis: a review of the methodology. Res Synth Methods. 2015 Aug 19;6:239–309.
- Debray TPA, Riley R, Rovers M, Reitsma JB, Moons K, on behalf of the Cochrane IPD Meta-analysis Methods group. Individual Participant Data (IPD) Meta- analyses of Diagnostic and Prognostic Modeling Studies: Guidance on Their Use. PLoS Med. 2015;12(10):e1001886.
- Debray TP, Schuit E, Efthimiou O, Reitsma JB, Ioannidis JP, Salanti G, et al. An overview of methods for network meta-analysis using individual participant data: when do benefits arise? Stat Methods Med Res. 2018;7(5):1351–64.
- Nguyen T-L, Debray TPA. The use of prognostic scores for causal inference with general treatment regimes. Stat Med. 2019 Jan 16;38:2013–29.
- Sarri G, Patorno E, Yuan H, Guo J (Jeff), Bennett D, Wen X, et al. Framework for the synthesis of non-randomised studies and randomised controlled trials: a guidance on conducting a systematic review and meta-analysis for healthcare decision making. BMJ EBM. 2020 Dec 9;bmjebm-2020-111493.
Listen to this episode and share this with your friends and colleagues!
Join The Effective Statistician LinkedIn group
This group was set up to help each other to become more effective statisticians. We’ll run challenges in this group, e.g. around writing abstracts for conferences or other projects. I’ll also post into this group further content.
I want to help the community of statisticians, data scientists, programmers and other quantitative scientists to be more influential, innovative, and effective. I believe that as a community we can help our research, our regulatory and payer systems, and ultimately physicians and patients take better decisions based on better evidence.
I work to achieve a future in which everyone can access the right evidence in the right format at the right time to make sound decisions.
When my kids are sick, I want to have good evidence to discuss with the physician about the different therapy choices.
When my mother is sick, I want her to understand the evidence and being able to understand it.
When I get sick, I want to find evidence that I can trust and that helps me to have meaningful discussions with my healthcare professionals.
I want to live in a world, where the media reports correctly about medical evidence and in which society distinguishes between fake evidence and real evidence.
Let’s work together to achieve this.
