3 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
Debiased Machine Learning for Count Data: a Partially Linear Poisson Model Based on Neural Networks
Ningkun Zhou, Bryan E. Shepherd, Qingyan Xiang
We develop a novel debiased machine learning (DML) estimator to analyze count data, which are common in clinical or biomedical studies. Specifically, we estimate the effect of a bi…
Marginal generalized raking with parametric working models
Brian D Williamson, Runjia Zou, Thomas Lumley +2
Generalized raking (GR) was originally developed in the survey statistics literature to incorporate auxiliary information in estimation. Recently, it has been used in the biostatis…
Methods to address measurement error in both Outcome and Covariates
Pamela A. Shaw, Bryan E. Shepherd
Biomedical research is increasingly relying on readily available routine data, such as electronic health records. Routinely collected data, as well as datasets from large cohorts,…
Addressing errors in multiple variables using generalized raking and cumulative probability models
Eric S. Kawaguchi, Chun Li, Frank E. Harrell +3
Routinely collected data, such as electronic health record (EHR) data, are frequently used for biomedical research, but these data are prone to errors, which can bias study finding…