157 citations · 173 across the 8 of their papers we have counts for
10 papers
A Bayesian Method for Adverse Effects Estimation in Observational Studies with Truncation by Death
Anthony Sisti, Andrew Zullo, Roee Gutman
Death among subjects is common in observational studies evaluating the causal effects of interventions among geriatric or severely ill patients. High mortality rates complicate the…
Bayesian Record Linkage with Variables in One File
Gauri Kamat, Mingyang Shan, Roee Gutman
In many healthcare and social science applications, information about units is dispersed across multiple data files. Linking records across files is necessary to estimate the assoc…
A Bayesian Approach to Linking Data Without Unique Identifiers
Edwin Farley, Roee Gutman
Existing file linkage methods may produce sub-optimal results because they consider neither the interactions between different pairs of matched records nor relationships between va…
A Bayesian Multi-Layered Record Linkage Procedure to Analyze Functional Status of Medicare Patients with Traumatic Brain Injury
Mingyang Shan, Kali Thomas, Roee Gutman
Understanding the association between injury severity and patients' potential for recovery is crucial to providing better care for patients with traumatic brain injury (TBI). Estim…
Approximate Bayesian Bootstrap Procedures to Estimate Multilevel Treatment Effects in Observational Studies with Application to Type 2 Diabetes Treatment Regimens
Anthony D. Scotina, Andrew R. Zullo, Robert J. Smith +1
Randomized clinical trials are considered the gold standard for estimating causal effects. Nevertheless, in studies that are aimed at examining adverse effects of interventions, su…
Matching Algorithms for Causal Inference with Multiple Treatments
Anthony D. Scotina, Roee Gutman
Randomized clinical trials (RCTs) are ideal for estimating causal effects, because the distributions of background covariates are similar in expectation across treatment groups. Wh…
Matching Estimators for Causal Effects of Multiple Treatments
Anthony D. Scotina, Francesca L. Beaudoin, Roee Gutman
Matching estimators for average treatment effects are widely used in the binary treatment setting, in which missing potential outcomes are imputed as the average of observed outcom…
Development of a Common Patient Assessment Scale across the Continuum of Care: A Nested Multiple Imputation Approach
Chenyang Gu, Roee Gutman
Evaluating and tracking patients' functional status through the post-acute care continuum requires a common instrument. However, different post-acute service providers such as nurs…
Estimation of causal effects with multiple treatments: a review and new ideas
Michael J Lopez, Roee Gutman
The propensity score is a common tool for estimating the causal effect of a binary treatment in observational data. In this setting, matching, subclassification, imputation, or inv…
Estimating the average treatment effects of nutritional label use using subclassification with regression adjustment
Michael Lopez, Roee Gutman
Propensity score methods are common for estimating a binary treatment effect when treatment assignment is not randomized. When exposure is measured on an ordinal scale (i.e., low -…