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Jacqueline
E.
Rudolph

Assistant Scientist
Jacqueline Rudolph

Departmental Affiliations

Primary

Contact Info

615 N. Wolfe St, Room E6608
Baltimore
Maryland
21205
US        

Research Interests

Epidemiology; Epidemiologic Methods; Causal Inference; Survival Analysis; Time-varying Analysis; HIV/AIDS
Experiences & Accomplishments
Overview
My research has largely sat at the intersection of causal inference and epidemiologic methods with substantive questions, with work spanning the areas of HIV/AIDS, environmental, and reproductive epidemiology. I am broadly interested in the application of advanced methods to overcome the challenges encountered in complex longitudinal data, such as competing events, time-varying data structures, and generalizability. For example, my post-doctoral work centered on different methodological complications surrounding per-protocol analyses in the Effects of Aspirin in Gestation and Reproduction trial. Currently, I am pursuing similar work within the context of HIV/AIDS observational cohort studies, focusing on the intersection of HIV and chronic comorbidities. I am also dedicated to the dissemination of novel, advanced methods within the epidemiologic community – making these methods more accessible to use while also making their theory, strengths, and weakness better understood. To me, an incredibly important part of this is consistently rooting these methods within the broader context of the causal inference roadmap, both in my own research and when communicating these ideas to others.
Select Publications
Selected Publications:
  • Rudolph JE, Benkeser D, Kennedy EH, Schisterman EF, Naimi AI. Estimation of the average causal effect in longitudinal data with time-varying exposures: the challenge of non-positivity and the impact of model flexibility. Am J Epidemiol. 2022; In Press.
  • Rudolph JE, Cartus A, Bodnar LM, Schisterman EF, Naimi AI. The role of the natural course in causal analysis. Am J Epidemiol. 2021; 191(2): 341-348. DOI: 10.1093/aje/kwab248
  • Rudolph JE, Fox MP, Naimi AI. Simulation as a tool for teaching and learning epidemiologic methods. Am J Epidemiol. 2021; 190(5): 900-907. DOI: 10.1093/aje/kwaa232
  • Rudolph JE, Lesko CR, Naimi AI. Causal inference in the face of competing events. Curr Epi Reports. 2020; 7: 125-131. DOI: 10.1007/s40471-020-00240-7
  • Rudolph JE, Cole SR, Eron JJ, Kashuba AD, Adimora AA. Estimating HIV prevention effects in low incidence settings. Epidemiology. 2019; 30(3): 358-364. DOI: 10.1097/EDE.0000000000000966