The courses included in the programme will address comparative effectiveness research, “the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care”.
Comparative Effectiveness Research courses
|R-programme||Introduction to R software, statistical tests with R, linear regression, analysis of variance and analysis of covariance with R, logistic regression with R, random variables, laws of probability, simulation and bootstrap with R||Elodie PERRODEAU|
|Methods in randomised controlled trials I (UE1)||General principles for pharmacologic & non-pharmacologic treatments.|
Principles, interest, and limits of different experimental designs/transparency of the research
|Mike CLARKE - Isabelle BOUTRON|
| Methods in randomised controlled trials II (UE1)||Specific designs: cluster, N of 1, large sample trials. Methodological issues encountered in evaluating non-pharmacological treatments/adapting of experimental designs. Specific experimental designs (cluster, stepped wedge, split body design)||David TORGERSON - Viet-Thi TRAN|
|Methods in diagnostic tests, biomarkers, and screening evaluation (UE2)||Methodological aspects of evaluating diagnostic tests and of biomarkers. Implementation and evaluation of screening||Patrick BOSSUYT - Jérémie COHEN|
|Methods in systematic reviews and meta-analysis I (UE4)||Systematic review of randomised trials, individual patient data meta-analysis. Methods of systematic reviews and meta-analysis: development of the protocol, search and selection of studies, data extraction and assessment of risk of bias.|
Meta-analysis: statistical analysis (combined estimation, heterogeneity evaluation, investigation of heterogeneity, bias in publication, use of software). Meta-analysis of personal data: pros and cons, statistical analysis
|Sally HOPEWELL - Lina EL CHALL|
|Methods in systematic reviews and meta-analysis II (UE5)||Methods for systematic review and meta-analysis of diagnostic tests accuracy studies. Systematic review and meta-analysis of observational studies: challenges and benefits. Meta-analysis of non-standard designs (cross-over studies, cluster randomised studies). Missing data in meta-analysis: imputation methods and pattern mixture models.||Mariska LEEFLANG
- Anna CHAIMANI
|Network meta-analysis (UE6)||Synthesis of multiple treatments: indirect and mixed comparison, network meta-analysis. Conceptual and statistical assumptions, different approaches for statistical synthesis, development of the protocol, critical appraisal of the results. Ranking of treatments: benefits and challenges. Discussion of published network meta-analysis and common mistakes in interpretation of the findings: reporting bias, heterogeneity and inconsistency, network structure and sparse networks. Software: R, CINeMA, NMAstudio.||Anna CHAIMANI - Dimitris MAVRIDIS|
|Methods of observational studies in CER (UE3)||General principles of causal inference: counterfactual model, types of causal effects, and causal assumptions. Principles, theory and application of causal methods using regression (g-computation), propensity scores, inverse probability of treatment weighting, and instrumental variables. Introduction to target trial emulation, and methods for time-varying treatments.||Raphaël PORCHER - Els GOETGHEBEUR - Saskia LE CESSIE - Ingeborg WAERNBAUM|
|CER and personalised or stratified medicine (UE7)||Principles and methods of the development of risk prediction models: study design, choice of a model, sample size considerations, handling of missing data, predictor selection, model specification, and model presentation.|
Principles and methods for the validation of risk prediction models: different kinds of validation, measures of performance (calibration, discrimination).
Clinical trials for precision medicine: performance of a marker to guide treatment decision, individualised treatment effects, trial designs for personalised medicine (biomarker-strategy designs; umbrella, basket and platform trials; principles of adaptive designs; adaptive enrichment designs).
|Gary COLLINS - Raphaël PORCHER|
|Routinely collected data in CER (UE8)||Routinely collected data (RCD) classification and concepts. Study designs using RCD to generate Real World Evidence (RWE). Promises and challenges of RWE and analyses of large-scale data sets. Randomized RWE (trials embedded in RCD infrastructures). Decentralized and remote trials with RCD. Pragmatic trials with RCD. Learning care systems with digital tools.||Lars HEMKENS
- Rémi FLICOTEAUX
- Jérôme LAMBERT
|CER case studies (UE9)||Case examples of comparative effectiveness research; development of methodological solutions to questions of comparative effectiveness; basic concepts of GRADE (Grading of Recommendations Assessment, Development and Evaluation).||Gerald GARTLEHNER - Joerg MEERPOHL|
|Internshp and thesis defence (UE10)||Six-month internship in France or abroad, in a public health agency or company, or in a research laboratory.|
By the end of their internship, trainees must have written a thesis in the form of a scientific article ready for publication.
Classes are full-time and will take place from October 23rd 2023 until January 18th 2024 (Monday to Friday, all day) with two weeks of holidays (detailed calendar here). Exams are right after, followed by an internship. See the page “programme” for more details.