• Research groups

Responsible Health Data Analysis

Jessica Rohmann

The Responsible Health Data Analysis research group develops and applies methods to improve the quality, reliability, and interpretability of health research. We are particularly interested in how observational health data can be used to answer descriptive, predictive, and causal questions, with a strong focus on causal inference. We combine methodological research with practical applications. Our work addresses how research questions can be translated into appropriate study designs and analyses, how assumptions and limitations affect the conclusions that can be drawn, and how new methodological developments can be brought into practice. We also conduct meta-research to study how research methods are used and reported in practice and develop approaches to support researchers in producing and critically evaluating robust evidence.

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Dr. Jessica Rohmann

Head of research group "Responsible Health Data Analysis"

E-mail:
jessica.rohmann[at]charite.de

Our research focuses on responsible health data analysis and research quality, with a particular emphasis on causal inference.

A major current project is 3P-CAUSAL ("Promises, Pitfalls, and Pathways for Causal Inference with Synthetic and Anonymized Health Data"), a BMFTR-funded junior consortium project within the Zukunft eHealth program, launched in October 2026 and running for three years. In addition to coordinating the consortium, our group leads the subproject "Causal Framework for Anonymized and Synthetic Health Data." We investigate how privacy-preserving transformations of health data affect “causal utility" and aim to develop methods for assessing whether and under what conditions anonymized and synthetic data can be used for reliable causal analyses. This is particularly relevant with the growing availability of health data for secondary research and the development of the European Health Data Space.

More broadly, our group aims to bridge the gap between the development of new causal inference methods and their use in applied research. This includes applying new methods to real-world health questions, developing practical guidance and examples, and strengthening exchange between different disciplines. This work builds on the Einstein Circle on Causal Inference from Observational Health Data, launched in 2025.

A further area of our work is research quality and meta-research. We study how research methods are used, reported, and interpreted in practice and develop practical approaches to help researchers improve their methods and critically evaluate evidence. This includes peer-review training through the Peerspectives Training Initiative and recent work on the responsible use of generative AI in epidemiology teaching and practice.

Across these areas, we combine methodological research, applied research, and researcher training to improve how health data are analyzed and how scientific evidence is produced and evaluated.

Selected publications

  1. Bartz A, Glaser N, Bayer O, Hallsson LR, Jaeger VK, Krumkamp R, Minnerup H, Pickford R, Standahl Olsen K, Wuelk N, Lukic M, Rohmann JL. Ten recommendations for the use of generative artificial intelligence into epidemiology teaching: Results from an expert workshop. Research Square [Preprint]. 2026. https://doi.org/10.21203/rs.3.rs-10974790/v1
  2. Rohmann JL, Wülk N, Rubarth K, Grillmaier H, Abdikarim I, Lopes Simões M, Schroter S, Piccininni M, Kurth T, Glatz T. Effectiveness of the researcher-led “Peerspectives” peer review training course on review quality, knowledge, and skills among doctoral students in the biomedical sciences: a pre-post study. Res Integr Peer Rev. 2026. https://doi.org/10.1186/s41073-026-00220-3
  3. Reitzle L, Rohmann JL, Kurth T, Audebert HJ, Piccininni M. External validation of risk prediction models for post-stroke mortality in Berlin. BMJ Open. 2025. https://doi.org/10.1136/bmjopen-2024-089320
  4. Forrest MR, Weissgerber TL, Lieske ES, Tamayo Cuartero E, Fischer E, Jones L, Piccininni M, Rohmann JL. Use of stacked proportional bar graphs (“Grotta bars”) to visualize functional outcome distributions in observational neurology research. Neurology. 2025. https://doi.org/10.1212/WNL.0000000000210169
  5. Collazo A, Kuhn H-G, Kurth T, Piccininni M, Rohmann JL. Rethinking animal attrition in preclinical research: expressing causal mechanisms of selection bias using directed acyclic graphs. J Cereb Blood Flow Metab. 2024. https://doi.org/10.1177/0271678X241275760
  6. Rohmann JL, Huerta-Gutierrez R, Audebert HJ, Kurth T, Piccininni M. Adjusted horizontal stacked bar graphs ("Grotta bars") for consistent presentation of observational stroke study results. Eur Stroke J. 2023. https://doi.org/10.1177%2F23969873221149464   
  7. Piccininni M, Kurth T, Audebert HJ, Rohmann, JL. The effect of Mobile Stroke Unit care on functional outcomes: an application of the front-door formula. Epidemiology. 2023. https://doi.org/10.1097/ede.0000000000001642
  8. Rohmann JL, Piccininni M, Ebinger M, Wendt M, Weber JE, Schwabauer E, Geisler F, Freitag E, Harmel P, Lorenz-Meyer I, Rohrpasser-Napierkowski I, Nolte CH, Nabavi DG, Schmehl I, Ekkernkamp A, Endres M, Audebert HJ. Effect of Mobile Stroke Unit Dispatch in all Patients with Acute Stroke or TIA. Ann Neurol. 2023. https://doi.org/10.1002/ana.26541
  9. Wong A, Kramer SC, Piccininni M, Rohmann JL, Kurth T, Escolano S, Grittner U, de Cellès MD. Using LASSO regression to estimate the population-level impact of pneumococcal conjugate vaccines. Am J Epidemiol. 2023. https://doi.org/10.1093/aje/kwad061
  10. Piccininni M, Rohmann JL, Wechsung M, Logroscino G, Kurth T. Should cognitive screening tests be corrected for age and education? Insights from a causal perspective. Am J Epidemiol. 2023. https://doi.org/10.1093/aje/kwac159

Team

Current Projects

Einstein Circle: Causal Inference from Observational Health Data

Peerspectives

Berlin Editors’ Exchange Network