5 papers
Robust Causal Inference for EHR-based Studies of Point Exposures with Missingness in Eligibility Criteria
Luke Benz, Rajarshi Mukherjee, Rui Wang +6
Missingness in variables that define study eligibility criteria is a seldom addressed challenge in electronic health record (EHR)-based settings. It is typically the case that pati…
A Statistical Framework for Understanding Causal Effects that Vary by Treatment Initiation Time in EHR-based Studies
Luke Benz, Rajarshi Mukherjee, Rui Wang +6
Standard practice in electronic health record (EHR)-based studies evaluating the comparative effectiveness of bariatric surgery relative to no surgery is to estimate and report a c…
Behavior of prediction performance metrics with rare events
Emily Minus, R. Yates Coley, Susan M. Shortreed +1
Objective: Area under the receiving operator characteristic curve (AUC) is commonly reported alongside prediction models for binary outcomes. Recent articles have raised concerns t…
Causal Quantile Treatment Effects with missing data by double-sampling
Shuo Sun, Sebastien Haneuse, Alexander W. Levis +5
Causal weighted quantile treatment effects (WQTE) are a useful complement to standard causal contrasts that focus on the mean when interest lies at the tails of the counterfactual…
Adjusting for Selection Bias Due to Missing Eligibility Criteria in Emulated Target Trials
Luke Benz, Rajarshi Mukherjee, Rui Wang +5
Target trial emulation (TTE) is a popular framework for observational studies based on electronic health records (EHR). A key component of this framework is determining the patient…