Neugebauer, Romain S.
Research focuses on the development, application, and dissemination of statistical methodologies.
Separating Algorithms from Questions and Causal Inference with Unmeasured Exposures: An Application to Birth Cohort Studies of Early BMI Rebound
Observational studies reporting on adjusted associations between childhood body mass index (BMI; weight (kg)/height (m)2) rebound and subsequent cardiometabolic outcomes…
Mendelian randomization analysis of C-reactive protein on colorectal cancer risk
Chronic inflammation is a risk factor for colorectal cancer (CRC). Circulating C-reactive protein (CRP) is also moderately associated with CRC…
Contrasting Causal Effects of Workplace Interventions.
Occupational exposure guidelines are ideally based on estimated effects of static interventions that assign constant exposure over a working lifetime.…
Advancing Health Policy and Program Research in Diabetes: Findings from the Natural Experiments for Translation in Diabetes (NEXT-D) Network
To advance our understanding of the impacts of policies and programs aimed at improving detection, engagement, prevention, and clinical diabetes…
Clinical implications of low skeletal muscle mass in early-stage breast and colorectal cancer
Although obesity has now been widely accepted to be an important risk factor for cancer survival, the associations between BMI…
Influence of a New Diabetes Diagnosis on the Health Behaviors of the Patient’s Partner
When a person is given a diagnosis of diabetes, the changes in his or her health behaviors may influence the…
A recursive partitioning approach to investigating correlates of self-rated health: The CARDIA Study
Self-rated health (SRH) is an independent predictor of mortality; studies have investigated correlates of SRH to explain this predictive capability.…
Feasibility and Performance Assessments of TMLE and hdPS Methodologies to Fit MSM in a Real-World CER Study
This study aims to evaluate the practicability and practical advantages of: 1) Targeted Minimum Loss based Estimation (TMLE), and 2)…
Software to Automate Implementation of Causal Inference Methods with Time-Varying Interventions
This project aims to develop SAS macros to automate the data structuring required for implementation of Causal Inference methods with…
Causal Analyses of Electronic Health Record Data for Assessing the Comparative Effectiveness of Treatment Regimens
This study aims to strengthen causal inferences based on Marginal Structural Modeling (MSM) and large healthcare databases for Comparative Effectiveness…
Post-traumatic stress disorder and cardiometabolic disease: improving causal inference to inform practice
Post-traumatic stress disorder (PTSD) has been declared 'a life sentence' based on evidence that the disorder leads to a host…
simcausal R Package: Conducting Transparent and Reproducible Simulation Studies of Causal Effect Estimation with Complex Longitudinal Data
The simcausal R package is a tool for specification and simulation of complex longitudinal data structures that are based on…
Semi-Parametric Estimation and Inference for the Mean Outcome of the Single Time-Point Intervention in a Causally Connected Population.
AbstractWe study the framework for semi-parametric estimation and statistical inference for the sample average treatment-specific mean effects in observational settings…
A Case Study of the Impact of Data-Adaptive Versus Model-Based Estimation of the Propensity Scores on Causal Inferences from Three Inverse Probability Weighting Estimators
Consistent estimation of causal effects with inverse probability weighting estimators is known to rely on consistent estimation of propensity scores.…
Balancing Score Adjusted Targeted Minimum Loss-based Estimation
Adjusting for a balancing score is sufficient for bias reduction when estimating causal effects including the average treatment effect and…
Targeted learning in real-world comparative effectiveness research with time-varying interventions
In comparative effectiveness research (CER), often the aim is to contrast survival outcomes between exposure groups defined by time-varying interventions.…
Super learning to hedge against incorrect inference from arbitrary parametric assumptions in marginal structural modeling
OBJECTIVE: Clinical trials are unlikely to ever be launched for many comparative effectiveness research (CER) questions. Inferences from hypothetical randomized…
Causal inference in epidemiological studies with strong confounding
One of the identifiability assumptions of causal effects defined by marginal structural model (MSM) parameters is the experimental treatment assignment…
Dynamic marginal structural modeling to evaluate the comparative effectiveness of more or less aggressive treatment intensification strategies in adults with type 2 diabetes
PURPOSE: Chronic disease care typically involves treatment decisions that are frequently adjusted to the patient's evolving clinical course (e.g., hemoglobin…
Robust extraction of covariate information to improve estimation efficiency in randomized trials
In randomized trials, investigators typically rely upon an unadjusted estimate of the mean outcome within each treatment arm to draw…
Causal inference in longitudinal studies with history-restricted marginal structural models
A new class of Marginal Structural Models (MSMs), History-Restricted MSMs (HRMSMs), was recently introduced for longitudinal data for the purpose…
