3 citations · 5 across the 9 of their papers we have counts for
10 papers
A cautionary note for plasmode simulation studies in the setting of causal inference
Pamela A Shaw, Susan Gruber, Brian D. Williamson +5
Plasmode simulation has become an important tool for evaluating the operating characteristics of different statistical methods in complex settings, such as pharmacoepidemiological…
Assessing treatment effects in observational data with missing confounders: A comparative study of practical doubly-robust and traditional missing data methods
Brian D. Williamson, Chloe Krakauer, Eric Johnson +13
In pharmacoepidemiology, safety and effectiveness are frequently evaluated using readily available administrative and electronic health records data. In these settings, detailed co…
Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention
Brian D. Williamson, Erica E. M. Moodie, Gregory E. Simon +2
Risk of suicide attempt varies over time. Understanding the importance of risk factors measured at a mental health visit can help clinicians evaluate future risk and provide approp…
Variable Selection for Individualized Treatment Rules with Discrete Outcomes
Zeyu Bian, Erica EM Moodie, Susan M Shortreed +2
An individualized treatment rule (ITR) is a decision rule that aims to improve individual patients health outcomes by recommending optimal treatments according to patients specific…
Estimating Individualized Treatment Rules in Longitudinal Studies with Covariate-Driven Observation Times
Janie Coulombe, Erica E. M. Moodie, Susan M. Shortreed +1
The sequential treatment decisions made by physicians to treat chronic diseases are formalized in the statistical literature as dynamic treatment regimes. To date, methods for dyna…
Using Pilot Data to Size Observational Studies for the Estimation of Dynamic Treatment Regimes
Eric J. Rose, Erica E. M. Moodie, Susan Shortreed
There has been significant attention given to developing data-driven methods for tailoring patient care based on individual patient characteristics. Dynamic treatment regimes forma…
Monte Carlo Sensitivity Analysis for Unmeasured Confounding in Dynamic Treatment Regimes
Eric J. Rose, Erica E. M. Moodie, Susan Shortreed
Data-driven methods for personalizing treatment assignment have garnered much attention from clinicians and researchers. Dynamic treatment regimes formalize this through a sequence…
Privacy-preserving estimation of an optimal individualized treatment rule : A case study in maximizing time to severe depression-related outcomes
Erica EM Moodie, Janie Coulombe, Coraline Danieli +2
Estimating individualized treatment rules - particularly in the context of right-censored outcomes - is challenging because the treatment effect heterogeneity of interest is often…
Variable Selection in Regression-based Estimation of Dynamic Treatment Regimes
Zeyu Bian, Erica EM Moodie, Susan M Shortreed +1
Dynamic treatment regimes (DTRs) consist of a sequence of decision rules, one per stage of intervention, that finds effective treatments for individual patients according to patien…
Unsupervised spectral learning
Susan Shortreed, Marina Meila
In spectral clustering and spectral image segmentation, the data is partioned starting from a given matrix of pairwise similarities S. the matrix S is constructed by hand, or learn…