About

Biostatistics consultant for medical research. Physician + Hopkins epi/biostats.

I’m a physician and biostatistician. By day, a Clinical Data Scientist at the Institute for Health Metrics in Manchester, MA, building real-world-data analyses (EHR, claims, and social-determinants measures) across 24.6M+ patient encounters from 50+ community hospitals. Outside that role, I take on biostatistics consulting work for medical researchers, small companies, and nonprofits.

My MPH is in epidemiology and biostatistics from Johns Hopkins Bloomberg School of Public Health, with a Public Health Economics graduate certificate. Before moving full-time into data science I practiced general and occupational medicine in the Philippines, led a systematic review whose findings shaped the Philippine Department of Health’s Wilms tumor chemotherapy guidelines, and ran a national advocacy program that integrated medical certification of cause of death into the country’s medical school curriculum. I keep one foot in each world (clinic, dataset, policy) because the questions worth modeling are the ones clinicians and patients actually live.

What I’m working on now

• Taking on biostatistics consulting engagements in study design, causal inference, sensitivity analysis, and methods writing for medical researchers, small companies, and nonprofits.
• Building out the portfolio of methodological case studies: published field notes on NHANES (population epi) and Medicaid (real-world data), with Medicare Part D (causal inference) and CDISC SDTM/ADaM (clinical-trial data standards) field notes coming next.
• Drafting v0.1s of methodology frameworks at the boundary of evidence synthesis and clinical AI evaluation; the Risk-of-bias framework for AI training corpora is the first published.

Toolkit

I work fluently in R (tidyverse, data.table, survminer, Shiny), Python, and SQL on AWS, with Stata and SAS in reach. I’m comfortable across ICD-10, RxNorm, LOINC, and SNOMED-CT, and methods I reach for routinely include difference-in-differences, target trial emulation, propensity score methods (matching, weighting, doubly-robust estimators), negative-binomial and ordinal-logistic regression, Markov ICERs, PCA-based SDoH composites, predictive modeling for mortality and readmission risk, and Monte Carlo simulation for bias quantification and sample size. On the synthesis side: GRADE, PRISMA, Cochrane RoB 2, ROBINS-I, AMSTAR, TRIPOD-AI, PROBAST, decision-curve analysis, calibration.

The full record (publications, training, professional memberships) is on the CV.

Contact

• Email pauline@informedica.llc
• Phone +1 (609) 509-2411
• LinkedIn linkedin.com/in/paulinedelmundo
• Based in Audubon, New Jersey

For consulting inquiries, the Contact page describes what to include in an initial note so we can scope the engagement on a 20-minute discovery call.