PhD Student Biostatistics at University of Waterloo, Canada.
Alexandra Bühler is a highly skilled statistical scientist specializing in the design, analysis, and interpretation of clinical trials in oncology, autoimmune diseases, and other complex life history processes. She combines academic rigor with hands-on experience, making significant contributions to the development of innovative statistical methods for addressing challenging issues in clinical trials. Alexandra’s expertise encompasses estimand strategies, causal inference, and multistate modeling, which have been pivotal in her roles with top-tier organizations such as Novartis and Roche. Her academic achievements are matched by her commitment to disseminating research through high-impact publications and presentations at prominent statistical conferences. Known for her analytical acumen and leadership in cross-functional teams, Alexandra consistently pushes boundaries in statistical methodology, making her a valuable contributor to the fields of biostatistics and applied health research.
Publication Profile
Education 🌱🎓
Alexandra Bühler’s educational background in mathematical biometry and biostatistics underpins her expertise in statistical analysis for clinical research. She completed her PhD in Biostatistics at the University of Waterloo, where her thesis focused on estimands in randomized clinical trials involving complex life history processes. Her doctoral studies were advised by renowned statisticians Richard Cook and Jerald Lawless. Alexandra earned her MSc in Mathematical Biometry from the University of Ulm, Germany, with a thesis that explored time-to-event and recurrent event methods in multiple sclerosis trials. Her research was recognized with the GMDS Award and the Boehringer Ingelheim Award. She also holds a BSc in Mathematical Biometry from the University of Ulm, where she developed foundational expertise in causal analysis applied to intensive care research.
With diverse experience across academia and industry, Alexandra Bühler has held roles that strengthen her skills in statistical methodologies for clinical trials. As a Post-Doctoral Statistical Scientist at Novartis, she worked on estimand strategies to improve data interpretation in oncology trials affected by treatment switching. She also served as a Graduate Research and Teaching Assistant at the University of Waterloo, assisting students and addressing analytical challenges in trial settings with semi-competing risks. Alexandra’s early experience includes internships in statistical methodology and programming at Roche and Boehringer Ingelheim, where she explored event methodologies in multiple sclerosis trials and collaborated on real-world evidence in clinical development. Alexandra’s experience is marked by her proficiency in advanced statistical software, including R and SAS, enhancing her ability to conduct complex data analyses.
Alexandra Bühler’s research interests lie at the intersection of statistical methodology and clinical applications, particularly in oncology and autoimmune diseases. She is passionate about developing and applying statistical methods to analyze complex life history processes in clinical trials, with a specific focus on estimand frameworks, causal inference, and joint modeling. Alexandra is dedicated to improving the interpretability and robustness of clinical trial data, employing multistate models and utility functions to address treatment switching and other intercurrent events. Her recent work on mean function regression and recurrent disability endpoints in clinical trials reflects her commitment to refining statistical approaches to enhance real-world applications, contributing valuable insights to the fields of biostatistics and epidemiology.
Throughout her career, Alexandra Bühler has received numerous accolades recognizing her contributions to statistical science and clinical research. She was honored with the prestigious GMDS Award and Boehringer Ingelheim Award for her master’s thesis at the University of Ulm, which compared time-to-event and recurrent event methodologies in multiple sclerosis trials. These awards highlight her innovation and technical expertise in statistical applications to complex medical conditions. Alexandra’s research and contributions have also been featured in leading statistical and medical journals, and she is a sought-after presenter at conferences, such as the Joint Statistical Meetings and the International Society for Clinical Biostatistics. Her achievements underscore her dedication to advancing statistical methodologies in clinical research.
Conclusion
Alexandra Bühler is a strong candidate for the Social Sciences Best Researcher Award, bringing in-depth expertise in biostatistics and statistical methodologies critical to health sciences. Her ability to innovate in statistical modeling, coupled with a dedication to publishing influential research, positions her as a leader in this field. Alexandra’s impressive contributions to clinical trial design and her use of estimand frameworks and multistate modeling techniques fulfill the award’s criteria for research excellence. She could further strengthen her candidacy by integrating her statistical expertise with interdisciplinary research in the social sciences, potentially broadening her impact across social health domains.