How We Work: You’ll spend most of your time writing R code to execute research projects — building cohorts and running the analyses that produce results. You’ll work alongside team members, coordinating through GitLab and staying in close contact as the code comes together. Project code runs on remote servers where the data live — usually ours, but sometimes a client or data provider environment. A research project moves through two main phases — cohort building, then analysis and reporting — and you’ll work in both.
Cohort Building: This is the largest piece of the role – turning raw healthcare databases into analytic-ready data sets. You will work with deidentified administrative claims and electronic health record (EHR) data, which come as relational tables covering patient demographics, diagnoses, procedures, medications, and lab results. Each data source has its own conventions, and learning them is critical to being successful in this role. In practice, you will:
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Draw on domain knowledge of how each database represents clinical events to make appropriate choices when building study variables.
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Use dbplyr to query source databases from R, generating SQL against tables that are often too large to hold in memory.
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Apply eligibility criteria from a study protocol and/or SAP to identify the study population.
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Determine the index date and start of follow-up for each patient.
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Build the analytic data set — often one row per patient, with columns for baseline characteristics, changes in treatment and health status over time, outcomes of interest, and censoring.
Analysis and Reporting: Our statisticians often lead this phase of research projects, with you in a supporting role — although this arrangement may vary depending on the study and your experience level. You will extend the project pipeline to take the analytic-ready data set through to the finished report. In practice, you will:
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Fit the models specified in the study design, often using causalRisk, our internal package for causal inference with observational data.
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Run diagnostics on the analysis — such as checking propensity score distributions, weight behavior, and covariate balance — and flag problems back to the study design team.
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Produce the tables and figures that make up the study results — baseline characteristics, effect estimates, and study-specific outputs like risk curves and treatment patterns — and assemble them into client deliverables.
Collaboration and Quality Control: Across both phases of a research project, you will:
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Build project repositories as orchestrated pipelines that reliably reproduce the study results each time they are run.
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Participate in quality control procedures including code review, testing, and double coding of portions of an analysis as an independent check on the primary implementation.
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Work closely with the study design team throughout the study lifecycle, incorporating changing requirements, sharing interim results, and providing feedback on how well the study design suits the available data.