Yingying Lu, PhD, MBA
Biostatistician, Palliative and Advanced Illness Research (PAIR) Center
Dr. Yingying Lu is a biostatistician at the Palliative and Advanced Illness Research (PAIR) Center. She received her PhD in Statistics from the University of Arizona, where she specialized in bioinformatics and multi-omics data analysis. Her current research interests include methods for clinical and cluster randomized trails, Bayesian modeling, and causal inference, as well as the application of integrative omics approaches to better understand complex disease mechanisms. She collaborates with investigators across diverse clinical areas on study design and statistical modeling for clinical trials and health services research.
Research Areas
Statistical Methods for Clinical and Cluster Randomized Trials, Bayesian Inference, Causal Inference, Bioinformatics, Multi-Omics Data Integration
Education
- PhD, University of Arizona 2023
- MS, Southern Illinois University Edwardsville 2015
- MBA, University of Arkansas 2012
- BA, Central South University of Forestry and Technology, China 2010
Contact
Research Highlights
Promoting Algorithmic Equity in In-Hospital Mortality Prediction
Overview: Models to predict death in the hospital are used in clinical medicine and research; but existing models perform differently across different groups, which really matters for patients hospitalized with acute respiratory failure (breathing problems) or sepsis (infection with an abnormal immune system response) because (1) such patients face high risks of worse model performance due to diagnostic uncertainty and high death risk; (2) racial and ethnic minority patients have increased death risk with acute respiratory failure or sepsis; and (3) models to predict death in the hospital are frequently used for these populations. Therefore, we are planning to create and test a new model to predict death in the hospital that is as fair as possible across the axes of race, ethnicity, sex, age, primary language, insurance status, and social vulnerability.
Advancing the Design, Analysis, and Interpretation of Acute Respiratory Distress Syndrome Trials Using Modern Statistical Tools
Overview: Acute respiratory distress syndrome (ARDS) is a common condition of severe respiratory failure that can be caused by a variety of illnesses (e.g., Covid-19 and other respiratory viruses, sepsis, and pneumonia), with hospital mortality of 30-40% and significant morbidity among survivors. ARDS randomized clinical trials (RCTs) have been hampered by (1) analyses that yield a strict binary conclusion concerning treatment efficacy, (2) inadequate statistical methods to assess heterogeneity of treatment effect among varying patient types, and (3) death-truncated non-mortality outcomes such as length of stay.
This research seeks to offer more intuitive probabilistic interpretations of an intervention’s efficacy and maximally leverage the information learned from a trial. The researchers will apply a suite of Bayesian causal inference and machine learning methods to 29 international and NIH/NHLBI-funded multicenter ARDS RCTs to (1) determine the probability that the tested interventions produce benefit or harm on the absolute and relative scale for both mortality and non-mortality clinical outcomes, (2) quantify the degree of confidence in these conclusions, and (3) identify clinically meaningful subgroups most likely to benefit.