Research Associate – Biostatistics Full-time

Research Associate – Biostatistics

Brubacher Lab in the Department of Emergency and the UBC Road Safety and Public Health Research Group is seeking a full-time Research Associate to support a research program focused on recovery following traumatic injury, with emphasis on post-traumatic stress symptoms (PTSS), post-traumatic growth (PTG), and functional outcomes. Our work integrates clinical, epidemiologic, and quantitative methods to understand the complex and heterogeneous trajectories of recovery after road trauma and other injuries, with the goal of improving patient outcomes and informing policy.

The successful candidate will bring advanced statistical expertise and substantive knowledge in trauma recovery and mental health. They will play a central role in analyzing longitudinal cohort data to characterize patterns of psychological and functional recovery, identify predictors of adverse and resilient trajectories, and examine the interplay between mental and physical health outcomes. This role is ideally suited to candidates with experience in PTSD or injury outcomes research who are motivated to apply rigorous quantitative methods to clinically meaningful questions.

The Research Associate will provide statistical leadership in the design and analysis of complex longitudinal and observational studies, including large inception cohorts of injured patients. Working within an interdisciplinary team, the candidate will apply advanced statistical and computational methods – including longitudinal and survival modeling, Bayesian hierarchical models, spatial and spatio-temporal approaches, machine learning, and latent variable and mixture modeling – to large-scale cohort data. These analyses will identify predictors of recovery outcomes, characterize mental health trajectories (including PTSS and PTG), and quantify injury-related health burdens to inform clinical practice and public health policy.

The Research Associate will work closely with the Principal Investigator to support ongoing projects and develop new research initiatives. Responsibilities include leading statistical analysis plans, developing reproducible workflows, mentoring trainees, and disseminating findings through peer-reviewed publications and scientific presentations.

This position offers the opportunity to contribute to high-impact interdisciplinary research addressing critical public health challenges in injury prevention, trauma recovery, and population health.

Responsibilities:

• Serve as the primary biostatistical expertise in the design, analysis, and interpretation of observational and longitudinal cohort studies.
• Develop and implement advanced statistical models, including longitudinal models, latent mixture models, survival and competing risks models, Bayesian hierarchical models, and structural equation models.
• Apply spatial and spatio-temporal statistical methods to investigate geographic variation and inequities in injury and healthcare access.
• Implement machine learning and predictive modeling approaches to construct robust clinical prediction tools and identify outcome trajectories.
• Lead analyses examining trajectories of PTSS, PTG, and functional recovery following traumatic injury.
• Apply person-centered analytic approaches (e.g., latent profile analysis (LPA), latent growth mixture modeling (LGMM), latent transition analysis (LTA)) to identify distinct recovery patterns and transitions over time.
• Integrate mental health, functional, and quality-of-life outcomes to examine the joint evolution of psychological and physical recovery.
• Help define and measure key trauma recovery outcomes, including PTSD symptoms and patterns of recovery.
• Work with clinical investigators to ensure appropriate interpretation of findings in the context of trauma care, mental health, and rehabilitation.
• Translate complex longitudinal findings into clinically relevant insights to inform patient care, policy, and intervention development.
• Lead the development of comprehensive statistical analysis plans, reproducible workflows, and rigorous data management strategies for complex research projects.
• Conduct statistical analyses using R and specialized statistical software, including Bayesian modeling tools (e.g., Stan, JAGS) and structural equation modeling software (e.g., Mplus).
• Collaborate closely with clinicians, epidemiologists, and health services researchers to integrate quantitative methods into interdisciplinary research projects.
• Mentor graduate students, trainees, and research staff in statistical methodology and quantitative data analysis.
• Contribute to grant applications and new research proposals by generating preliminary data and developing methodological frameworks.
• Take a lead role in authoring peer-reviewed publications and presenting at international scientific conferences.

Qualifications:

• PhD in Biostatistics or Statistics.
• Strong expertise in advanced statistical modeling, including longitudinal data analysis, survival analysis, Bayesian modeling, structural equation modeling, and clustering or latent mixture methods.
• Experience applying spatial and spatio-temporal statistical methods in health or epidemiological research.
• Substantive knowledge of mental health outcomes, trauma recovery, or related fields (e.g., PTSD, rehabilitation, quality of life), with the ability to link statistical methods to clinically meaningful questions.
• Strong programming and analytical skills in R and statistical modeling software.
• Demonstrated track record of first-author peer-reviewed publications in quantitative or health-related research.
• Experience analyzing large and complex health or population-based datasets.
• Strong communication skills and ability to translate quantitative findings to multidisciplinary audiences.
• Demonstrated ability to work independently and provide visionary methodological leadership within collaborative research teams
• Effective oral and written communications, with the ability to compose clear and grammatically correct correspondence, business documents, and administrative communications.
• Willingness to respect diverse perspectives, including perspectives in conflict with one's own.
• Demonstrates a commitment to enhancing one's own awareness, knowledge, and skills related to equity, diversity, and inclusion.