Postdoctoral Fellow - Breast Surgical Oncology - Research

The Role

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.

Responsibilities

• 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.

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

Qualifications

Not specified

Skills

Not specified

Benefits

Not specified

Pay

Not specified

Schedule

Not specified

Learning Objectives

• 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.
• Gain hands-on experience applying causal inference methods in large-scale healthcare claims data.
• Participate in a weekly causal inference conference with opportunities to collaborate on methods development and applied projects in causal inference research.
• Develop an independent publication record through authorship on methods and applied manuscripts, and have opportunities to present work and lead workshops at major statistical and epidemiologic conferences.

Eligibility Requirements

Not specified

Position Information

• 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.

University Policy

The University of Texas MD Anderson Cancer Center is committed to providing 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.