Sarah Robertson
Dartmouth College, NH·Public Health & Epidemiology·Early-career
Lung cancer screening can save lives, but most evidence for it comes from clinical trials. Trial volunteers tend to be carefully selected and to follow screening schedules closely, and trials often test strategies that differ from current practice.
- Fields
Public Health & Epidemiology
Mathematics & Statistics
Medicine & Clinical Research
- Topics
lung cancer screening
causal inference
transportability
- Grants
- 1 known grant, through Aug 2028
What they work on
- Transport the effects of lung cancer screening strategies from trials such as NLST to nationally representative target populations by combining information from claims, registries, and studies.
- Identify who in nationally representative populations is most likely to benefit from screening, including groups underrepresented in trials, by evaluating effect differences across several characteristics at once.
- Model real-world adherence to lung cancer screening and estimate how screening effects change at different adherence levels in target populations.
- Compare screening strategies never tested head-to-head by linking trials that share a common comparator, and use multi-source data in a simulation model to evaluate untested strategies.
More about the research
As a result, even the National Lung Cancer Screening Trial (NLST), the largest such trial, cannot say on its own how well screening works in the broader, more diverse populations now being screened. Insurance claims data, screening registries, and observational studies hold real-world information. However, standard ways of combining evidence, such as meta-analyses and simulation models, do not give answers with a clear cause-and-effect meaning for a specific population.
Why it matters
Better evidence on which lung cancer screening strategies work in real-world, nationally representative populations could guide screening policy and reduce lung cancer deaths. By producing evidence for groups traditionally underrepresented in trials, the work may help make screening decisions fairer across the population.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Jennifer Lund
University of North Carolina at Chapel Hill, NC·Public Health & Epidemiology
Estimate the real-world benefits and harms of low-dose CT lung cancer screening among people eligible for screening, using trial transport to adjust National Lung Screening Trial results to national survey populations.
Dominique Michaud
Tufts University, MA·Public Health & Epidemiology
Develop a pre-screening tool using blood leukocyte DNA methylation markers to sort high-risk NLST participants into finer risk categories and reduce the number of low-dose CT scans needed.
Karla Kerlikowske
University of California-Davis, CA·Medicine & Clinical Research
Develop and validate models that predict a woman's 6-year risk of advanced breast cancer, combining clinical risk factors, race and ethnicity, imaging features and AI output in Breast Cancer Surveillance Consortium data, and test their fairness across racial and ethnic groups.
The school
Dartmouth College: 16 labs on PI Pages, $418M a year on research, 104 research doctorates a year.
Reach out
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