4 citations · 7 across the 6 of their papers we have counts for
6 papers
Data-integration with pseudoweights and survey-calibration: application to developing US-representative lung cancer risk models for use in screening
Lingxiao Wang, Yan Li, Barry Graubard +1
Accurate cancer risk estimation is crucial to clinical decision-making, such as identifying high-risk people for screening. However, most existing cancer risk models incorporate da…
Nonprobability follow-up sample analysis: an application to SARS-CoV-2 infection prevalence estimation
Yan Li, Laura Yee, Sally Hunsberger +3
Public health policy makers are faced with making crucial decisions rapidly during infectious disease outbreaks such as that caused by SARS-CoV-2. Ideally, rapidly deployed represe…
Representative Pure Risk Estimation by Using Data from Epidemiologic Studies, Surveys, and Registries: Estimating Risks for Minority Subgroups
Lingxiao Wang, Yan Li, Barry I. Graubard +1
Representative risk estimation is fundamental to clinical decision-making. However, risks are often estimated from non-representative epidemiologic studies, which usually underrepr…
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
Yan Li, Lingxiao Wang, Jiachen Yang +4
Multi-agent reinforcement learning (MARL) becomes more challenging in the presence of more agents, as the capacity of the joint state and action spaces grows exponentially in the n…
Efficient and Robust Propensity-Score-Based Methods for Population Inference using Epidemiologic Cohorts
Lingxiao Wang, Barry I. Graubard, Hormuzd A. Katki +1
Most epidemiologic cohorts are composed of volunteers who do not represent the general population. To enable population inference from cohorts, we and others have proposed utilizin…
Adjusted Logistic Propensity Weighting Methods for Population Inference using Nonprobability Volunteer-Based Epidemiologic Cohorts
Lingxiao Wang, Richard Valliant, Yan Li
Many epidemiologic studies forgo probability sampling and turn to nonprobability volunteer-based samples because of cost, response burden, and invasiveness of biological samples. H…