About

I’m Paulina Del Mundo: a physician and biostatistician. This site is my personal working reference for the methods that sit under a clinical recommendation, from the bedside guideline down to the datapoint it rests on, and back up. It has three parts: the pathway, a step-by-step methods reference; the glossary; and a set of worked traces on public data.

Background

By day I’m 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.

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. It is also what lets me trace a recommendation in both directions: down from the bedside guideline to the datapoint it rests on, and back up. Methodologists tend to own the bottom of that chain and clinicians the top; the work I care about is walking the whole thing.

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.