Research Associate (Comparative Effectiveness and Real-World Data Analytics)

Job Title: Research Associate (Comparative Effectiveness and Real-World Data Analytics)

University-Level Unit: Saw Swee Hock School of Public Health

Faculty/Department-Level Unit: Saw Swee Hock School of Public Health

Employee Category: Research Staff

Location_ONB: Kent Ridge Campus

Posting Start Date: 16/04/2026

Job Description

Applications are invited for the following full-time position in the Saw Swee Hock School of Public Health:

Research Associate (Comparative Effectiveness and Real-World Data Analytics)

Position Summary

The Centre for Health Intervention and Policy Evaluation Research (HIPER) ( https://hiper.nus.edu.sg/) at the Saw Swee Hock School of Public Health (SSHSPH), NUS, is seeking to hire a researcher with a relevant Master’s degree to lead Phase III of the RODEO project, focusing on comparative analyses using oncology real-world data. The candidate will be responsible for designing and conducting comparative effectiveness studies across multiple myeloma, non-small cell lung cancer, and ovarian cancer using structured and processed unstructured data from electronic medical records.

The role is particularly suited to candidates with training in biostatistics, epidemiology, health services research, health economics, public health, data science, or a related field, and who have experience working with observational healthcare datasets.

Key Responsibilities:

Lead the design and execution of comparative effectiveness analyses using real-world oncology data

Develop study protocols and statistical analysis plans for observational cohort analyses

Apply appropriate methods to address confounding, selection bias, missing data, and other threats to internal validity in real-world studies

Conduct analyses using methods such as propensity score matching, inverse probability weighting, direct covariate adjustment, difference-in-differences, instrumental variables, and survival analysis

Perform time-to-event analyses, including overall survival and progression-related outcomes

Support partitioned survival analyses and other methods relevant to economic evaluation and health technology assessment

Work closely with clinicians, health economists, and data scientists to identify clinically relevant treatment comparisons and confounders

Collaborate with data engineers and analysts to ensure appropriate variable construction and dataset readiness

Interpret findings in the context of health technology assessment, reimbursement, and policy decision-making

Prepare technical reports, presentations, manuscripts, and conference abstracts

Ensure analyses adhere to methodological standards such as STROBE and ISPOR good practice recommendations for real-world evidence studies

Participate in project meetings with collaborators across NUS, NCIS, NUH, TTSH, NCCS, and other partner institutions

Requirements:

Experience working with large observational healthcare datasets, registries, or electronic medical records

Strong knowledge of causal inference and comparative effectiveness methods

Experience using statistical software such as R, Stata, SAS, or Python

Familiarity with oncology data, survival analysis, and longitudinal data analysis will be an advantage

Understanding of health technology assessment, outcomes research, or economic evaluation is desirable

Strong written and verbal communication skills

Ability to work independently while collaborating effectively in a multidisciplinary research team

Preferred Attributes:

Prior experience with real-world evidence generation in oncology

Familiarity with OMOP common data model, OHDSI tools, or TRUST platform datasets

Experience translating statistical findings into policy-relevant insights for clinicians, hospital leaders, or HTA agencies

Interest in contributing to publications and future grant applications

Qualifications

Master’s degree in Biostatistics, Epidemiology, Public Health, Health Economics, Data Science, Statistics, Health Services Research, or a related discipline