Postdoctoral Fellow - Breast Surgical Oncology - Research

The postdoctoral fellow will work on an externally funded methods development project focused on causal inference for long‑term pharmacotherapy. The central methodological contribution is a framework for “patient‑choice (PC) protocols”: a class of treatment strategies that allow patients to flexibly balance quality of life and clinical outcomes during sustained pharmacotherapy. The applied context is adjuvant endocrine therapy for patients with breast cancer. The fellow will contribute to theoretical development, estimation, and real‑world application of these methods across large healthcare claims databases (Merative MarketScan, SEER‑Medicare) and data from an international phase‑III clinical trial (PALLAS). Under the general guidance of the Principal Investigator, the postdoctoral fellow will: Develop and formalize causal estimands using counterfactual theory, causal directed acyclic graphs, and single world intervention graphs. Derive efficient influence functions and develop targeted minimum loss‑based estimation (TMLE) algorithms for a general class of PC protocols, including extensions to dynamic regime marginal structural models. Implement doubly robust, semiparametrically efficient estimators that incorporate flexible machine learning algorithms for confounding control in high‑dimensional longitudinal data. Develop and apply sensitivity analysis methods. Analyze large‑scale observational claims data and contribute to open‑source software development in R. Prepare manuscripts and present at national and international conferences. All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations. Learning Objectives The fellow will develop expertise in a novel class of causal estimands for longitudinal treatment strategies with non‑adherence, building from foundational identification theory through to efficient nonparametric estimation and open‑source implementation. The fellow will gain hands‑on experience applying causal inference methods in large‑scale healthcare claims data. The postdoctoral fellow will participate in a weekly causal inference conference with opportunities to collaborate on methods development and applied projects in causal inference research. The fellow will develop an independent publication record through authorship on methods and applied manuscripts, and will have opportunities to present work and lead workshops at major statistical and epidemiologic conferences. Eligibility Requirements Ph.D. in Biostatistics, Statistics, or Epidemiology with a strong focus on causal inference methods. Strong programming skills in R and SAS required, with experience using large administrative claims datasets. Position Information MD Anderson offers full‑time postdoctoral positions with a salary ranging from $64,000 to $76,000 depending on the number of years of postgraduate experience. The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition. Offsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements. This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment. It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. #J-18808-Ljbffr