Publication

The Trend-in-Trend Research Design for Causal Inference. Ji X, Small DS, Leonard CE, Hennessy S. Cohort studies can be biased by unmeasured confounding. We propose a hybrid ecologic-epidemiologic design called […]

Constructed Second Control Groups and Attenuation of Unmeasured Biases Samuel D. Pimentel, Dylan S. Small & Paul R. Rosenbaum The informal folklore of observational studies claims that if an irrelevant […]

An adaptive Mantel-Haenszel test for sensitivity analysis in observational studies. Rosenbaum PR, Small DS In a sensitivity analysis in an observational study with a binary outcome, is it better to […]

A framework for Bayesian nonparametric inference for causal effects of mediation. Kim C, Daniels MJ, Marcus BH, Roy JA We propose a Bayesian non-parametric (BNP) framework for estimating causal effects […]

PREVAIL: In this work, our lab developed a model for predicting the degree and spatial pattern of MS lesion tissue recovery. The model is based solely on lesion presentation at […]

A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome. Roy J, Lum KJ, Daniels MJ. Marginal structural models (MSMs) are a general […]

Selection Bias When Using Instrumental Variable Methods to Compare Two Treatments But More Than Two Treatments Are Available. Ertefaie A, Small D, Flory J, Hennessy S. Instrumental variable (IV) methods […]

An evaluation of bias in propensity score-adjusted non-linear regression models Fei Wan, Nandita Mitra Propensity score methods are commonly used to adjust for observed confounding when estimating the conditional treatment […]