Prof. Dr. Vanessa Didelez

Since July 2016 Professor of Statistics and Causal Inference, University of Bremen, and Department of Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology - BIPS (Deputy Head of Department)

Presentation "Causal reasoning in survival and time-to-event analyses" at the Online Causal Inference Seminar
https://www.youtube.com/watch?v=5dMdNGZGVR8&feature=youtu.be

2022-2026 Principal investigator, Project "Causal Discovery across the Lifespan" as part of the DFG Research Unit "LifespanAI"

2021-2026 Principal investigator, Project as part of DFG collaborative research center EASE "Sensory-motor and Causal Human Activity Models for Cognitive Architectures"

2021-2024 Principal investigator, UBRA Project "IDEAL" Causal inference and target trials to develop electronic medical guidelines

2018-2021: Principal investigator, DFG grant "Causal Discovery for Cohort Data"

2015-2018: CoIlaborator on MRC grant “Development of a multilevel and mixture-model framework for modelling epigenetic changes over time”

2013-2023: Collaborator on MRC Integrative Epidemiology Unit, Theme 3: "Statistical and Econometric Methodology"

2013- June 2016: Reader in Statistics at the School of Mathematics, University of Bristol

2011-2013: Leverhulme Research Fellowship “Statistical models and methods for complex causal inference”

2009-2013: Senior Lecturer at the School of Mathematics, University of Bristol

2007-2010: CoI on MRC grant “Inferring epidemiological causality using Mendelian Randomisation”

2007-2009: Lecturer at the School of Mathematics, University of Bristol

2001-2007: Lecturer at the Department of Statistical Science, University College London

Dec 2000: PhD (Dr rer.nat.) at the Department of Statistics, University of Dortmund, Germany

1997-2000: Research Assistant at the Department of Statistics, University of Munich, Germany

1996: Diploma in "Statistics with Psychology" at the Department of Statistics, University of Dortmund, Germany

    Selected BIPS-Publications

  • Articles with peer review

  • Braitmaier M, Didelez V. Emulierung von Target Trials mit Real World Daten - Ein allgemeines Prinzip, um den Herausforderungen von Beobachtungsdaten zu begegnen. Prävention und Gesundheitsförderung. 2024; (Epub 2022 Jul 29).
    https://doi.org/10.1007/s11553-022-00967-9
  • Evans R, Didelez V. Parameterizing and simulating from causal models. Journal of the Royal Statistical Society. Series B (Statistical Methodology). 2024; (Epub 2023 May 23).
    https://doi.org/10.1093/jrsssb/qkad058
  • Hanke M, Dijkstra L, Foraita R, Didelez V. Variable selection in linear regression models: Choosing the best subset is not always the best choice. Biometrical Journal. 2024;66(1):2200209.
    https://doi.org/10.1002/bimj.202200209
  • Andrews R, Shpitser I, Didelez V, Chaves PH, Lopez O, Carlson MC. Examining the causal mediating role of cardiovascular disease on the effect of subclinical cardiovascular disease on cognitive impairment via separable effects. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences. 2023;78(7):1172-1178.
    https://doi.org/10.1093/gerona/glad077
  • Börnhorst C, Pigeot I, De Henauw S, Formisano A, Lissner L, Molnár D, Moreno LA, Tornaritis M, Veidebaum T, Vrijkotte T, Didelez V, Wolters M, on behalf of the GrowH! consortium. The effects of hypothetical behavioral interventions on the 13-year incidence of overweight/obesity in children and adolescents. International Journal of Behavioral Nutrition and Physical Activity. 2023;20:100.
    https://doi.org/10.1186/s12966-023-01501-6
  • Curnow E, Carpenter JR, Heron JE, Cornish RP, Rach S, Didelez V, Langeheine M, Tilling K. Multiple imputation of missing data under missing at random: Compatible imputation models are not sufficient to avoid bias if they are mis-specified. Journal of Clinical Epidemiology. 2023;160:100-109.
    https://doi.org/10.1016/j.jclinepi.2023.06.011
  • Rojas-Saunero LP, Young JG, Didelez V, Ikram A, Swanson SA. Considering questions before methods in dementia research with competing events and causal goals. American Journal of Epidemiology. 2023;192(8):1415-1423.
    https://doi.org/10.1093/aje/kwad090
  • Braitmaier M, Kollhorst B, Heinig M, Langner I, Czwikla J, Heinze F, Buschmann L, Minnerup H, García-Albéniz X, Hense H-W, Karch A, Zeeb H, Haug U, Didelez V. Effectiveness of mammography screening on breast cancer mortality - A study protocol for emulation of target trials using German health claims data. Clinical Epidemiology. 2022;14:1293-1303.
    https://doi.org/10.2147/CLEP.S376107
  • Braitmaier M, Schwarz S, Kollhorst B, Senore C, Didelez V, Haug U. Screening colonoscopy similarly prevented distal and proximal colorectal cancer: A prospective study among 55-69-year-olds. Journal of Clinical Epidemiology. 2022;149:118-126.
    https://doi.org/10.1016/j.jclinepi.2022.05.024
  • Didelez V, Stensrud MJ. On the logic of collapsibility for causal effect measures. Biometrical Journal. 2022;64(2):235-242. (This paper has been recognized as a top cited and top downloaded research article from 2021-2022 in Biometrical Journal).
    https://doi.org/10.1002/bimj.202000305
  • Morris TT, Heron J, Sanderson E, Smith GD, Didelez V, Tilling K. Interpretation of Mendelian randomization using a single measure of an exposure that varies over time. International Journal of Epidemiology. 2022;51(6):1899-1909.
    https://doi.org/10.1093/ije/dyac136
  • Stensrud MJ, Young JG, Didelez V, Robins JM, Hernán MA. Separable effects for causal inference in the presence of competing events. Journal of the American Statistical Association. 2022;117(537):175-183.
    https://doi.org/10.1080/01621459.2020.1765783
  • Witte J, Foraita R, Didelez V. Multiple imputation and test-wise deletion for causal discovery with incomplete cohort data. Statistics in Medicine. 2022;41(23):4716-4743.
    https://doi.org/10.1002/sim.9535
  • Andrews R, Didelez V. Insights into the cross-world independence assumption of causal mediation analysis. Epidemiology. 2021;32(2):209-219.
    https://doi.org/10.1097/EDE.0000000000001313
    https://repository.publisso.de/resource/frl%3A6425953
  • Börnhorst C, Reinders T, Rathmann W, Bongaerts B, Haug U, Didelez V, Kollhorst B. Avoiding time-related biases: A feasibility study on antidiabetic drugs and pancreatic cancer applying the parametric g-formula to a large German healthcare database. Clinical Epidemiology. 2021;(13):1027-1038.
    https://doi.org/10.2147/CLEP.S328342
  • Pigeot I, Kollhorst B, Didelez V. Nutzung von Sekundärdaten für die pharmakoepidemiologische Forschung - Machen wir das Beste draus! Das Gesundheitswesen. 2021;83(S 02):S69-S76.
    https://dx.doi.org/10.1055/a-1633-3827
    https://repository.publisso.de/resource/frl%3A6431669
  • Stensrud MJ, Hernán MA, Tchetgen Tchetgen E, Robins JM, Didelez V, Young JG. A generalized theory of separable effects in competing event settings. Lifetime Data Analysis. 2021;27(4):588-631. (This paper was one of 2021’s top downloaded Lifetime Data Analysis research articles).
    https://doi.org/10.1007/s10985-021-09530-8
  • Aalen OO, Stensrud MJ, Didelez V, Daniel R, Roysland K, Strohmaier S. Time-dependent mediators in survival analysis: Modeling direct and indirect effects with the additive hazards model. Biometrical Journal. 2020;62(3):532-549. (This paper has been recognized as a top cited research article from 2020-2021 in Biometrical Journal).
    https://doi.org/10.1002/bimj.201800263
    https://ui.adsabs.harvard.edu/abs/2020arXiv201113415A/abstract
  • Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V. Causal discovery of gene regulation with incomplete data. Journal of the Royal Statistical Society. Series A (Statistics in Society). 2020;183(4):1747-1775.
    https://doi.org/10.1111/rssa.12565
  • Sheehan N, Didelez V. Epidemiology, genetic epidemiology and Mendelian randomisation: More need than ever to attend to detail. Human Genetics. 2020;139(1):121-136.
    https://doi.org/10.1007/s00439-019-02027-3
  • Witte J, Henckel L, Maathuis MH, Didelez V. On efficient adjustment in causal graphs. Journal of Machine Learning Research. 2020;21(246):1-45.
    http://jmlr.org/papers/v21/20-175.html
  • Didelez V. Defining causal mediation with a longitudinal mediator and a survival outcome. Lifetime Data Analysis. 2019;25(4):593-610. (This paper was one of 2020’s top downloaded and one of 2021's top cited Lifetime Data Analysis research articles).
    https://doi.org/10.1007/s10985-018-9449-0
  • Witte J, Didelez V. Covariate selection strategies for causal inference: Classification and comparison. Biometrical Journal. 2019;61(5):1270-1289.
    https://doi.org/10.1002/bimj.201700294
  • Phillippo DM, Dias S, Ades AE, Didelez V, Welton NJ. Sensitivity of treatment recommendations to bias in network meta-analysis. Journal of the Royal Statistical Society. Series A (Statistics in Society). 2018;181(3):843-867.
    https://doi.org/10.1111/rssa.12341
  • Vansteelandt S, Didelez V. Improving the robustness and efficiency of covariate-adjusted linear instrumental variable estimators. Scandinavian Journal of Statistics, Theory and Applications. 2018;45(4):941-961.
    https://doi.org/10.1111/sjos.12329
    https://researchonline.lshtm.ac.uk/id/eprint/4658753/
  • Farewell D, Huang C, Didelez V. Ignorability for general longitudinal data. Biometrika. 2017;104(2):317-326.
    https://doi.org/10.1093/biomet/asx020
  • Conference proceedings

  • Bang CW, Didelez V. Do we become wiser with time? On causal equivalence with tiered background knowledge. In: Evans R, Shpitser I, editors. Proceedings of the thirty-ninth conference on uncertainty in artificial intelligence (UAI 2023), Pittsburgh, USA. 2023. S. 119-129.
    https://proceedings.mlr.press/v216/bang23a
  • Commentaries

  • Didelez V. Perspective on interviews with Heckman, Pearl, Robins and Rubin. Observational Studies. 2022;8(2):95-104.
    http://doi.org/10.1353/obs.2022.0010
  • Didelez V. Comment: Invariance, Causality and Robustness. Statistical Science. 2020;35(3):427-429.
    https://doi.org/10.1214/20-STS768
  • Book chapters

  • Didelez V. Causal concepts and graphical models. In: Maathuis M, Drton M, Lauritzen SL, Wainwright M, editors. Handbook of graphical models. Boston: CRC Press. 2018. S. 353-380.
  • Didelez V, Evans R. Causal inference from case-control studies. In: Borgan O, Breslow N, Chatterjee N, Gail M, Scott A, Wild C, editors. Handbook of statistical methods for case-control studies. Boca Raton: Chapman & Hall/CRC. 2018. S. 87-115.
  • Presentations at scientific meetings/conferences (invited)

  • Didelez V. Introduction to causal discovery. 52nd Workshop Statistical Computing, 24-27 July 2022, Günzburg.
  • Didelez V. Causal estimands and interventions. "Learning from Interventions"-Workshop at the Simons Institute for the Theory of Computing, 14-17 February 2022, Berkeley, USA.
  • Didelez V. Causal inference for survival outcomes: A censored edition. 54th Annual Meeting of the Society for Epidemiologic Research (SER), 22-25 June 2021, online presentation.
  • Didelez V. Causal reasoning in survival and time-to-event analyses. Online Causal Inference Seminar, 1 December 2020, online presentation.

For further publications please follow this link:

  • Invited participant Simons Institute Program on Causality
    University of California, Simons Institue for the Theory of Computing, Berkeley, USA (2022)
  • Eingeladene IMS Medallion lecture at the 9th World Congress of Probability and Statistics, Toronto
    Institute of Mathematical Statistics (IMS), Ohio/USA (2016)
  • Invited participant at program "High-Dimensional Causal Inference and its Application to Genetics"
    Centre de Recherches Mathématiques (CRM), University of Montréal, Canada (2016)

The responsibility for the content of this page lies with Vanessa Didelez.

Vanessa Didelez

Contact

Phone:
+49 (0)421 218-56-939