Publication

In Annals of Internal Medicine, Nicholas Seewald, PhD, introduces a framework for target trial emulation, enabling researchers to evaluate health policies with the rigor of clinical trials in nonexperimental studies.

Michael Harhay, PhD, MPH, co-authors "Bayesian Statistics for Clinical Research" in The Lancet, comparing Bayesian and frequentist methods and highlighting the growing accessibility and impact of Bayesian analysis in medical research.

Dr. Mingyao Li's article in Nature Methods discusses how artificial intelligence is revolutionizing spatial omics, enhancing integration of diverse data and accelerating biological discoveries for improved health outcomes in biomedical research.

NEMS measures have played an important role in the growth of research on food environments and have helped researchers to explore the relationships among healthy food availability, demographic variables, eating […]

Patients come to a hospital nearly 36 million times each year in the US, and antibiotics are often part of the picture: One large study showed that clinicians prescribed them […]

In 1991, the National Academies of Sciences, Engineering and Medicine declared that computer-based patient records were an essential technology for health care. The new records would not only support patient […]

At the start of the pandemic, professionals working in health systems across the US realized that if a sizable portion of the many people infected with SARS-CoV-2 went to hospitals, […]

Masking policies during Covid-19 have inspired plenty of political debate, but scientific evidence about the policies’ effects has been very limited. This isn’t surprising, given that it is not practical […]

Rebecca Hubbard, PhD, co-led a new JNCI paper that studied >14k patients with advanced cancer in a national EHR database to show that time from diagnosis to treatment and choice of therapy […]

In addition to making events such as catastrophic flooding more frequent, climate change will negatively affect human health in many other ways. Prior research has demonstrated, for instance, that high […]

“Pregnant women CAN drink coffee – it could even slash risk of disease for mum and baby,” proclaimed a headline in the English paper The Sun about a recent study […]

Our new paper “Assessing Fairness in the Presence of Missing Data” has been accepted for publication at NeurIPS 2021, a leading machine learning /artificial intelligence conference. Learn More

Our recent PNAS paper introduced the Layer-Peeled Model in deep learning that can: 1) predict a hitherto unknown phenomenon termed “Minority Collapse” in imbalanced training; and 2) explain neural collapse […]

Rebecca Hubbard, PhD, and Qi Long, PhD, are co-authors on this new JAMA Oncology paper that finds solid-tumor cancer patients ineligible for clinical trials receive immunotherapy at greater rates — […]

A key factor in designing randomized clinical trials is the sample size required to achieve a particular level of power to detect the benefit of a treatment. Sample size calculations […]

Doubly robust nonparametric instrumental variable estimators for survival outcomes Youjin Lee, Edward H Kennedy, Nandita Mitra Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in causal […]

The presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis lesion. Increased prevalence […]

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple […]

While many findings in neuroimaging studies pertain to multiple imaging modalities, statistical methods underlying intermodal comparisons have varied. In our recently published paper in Human Brain Mapping, we propose the […]

Many human diseases start out with single cells, which establish the problem by spreading out. Spatial transcriptomics, a groundbreaking method, allows scientists to take advantage of that activity, measuring all […]

Given rising rates of obesity in the U.S. and the many associated health problems, researchers have tested various strategies for weight loss. Both financial incentives and environmental changes — such […]

Although mammography reduces breast cancer mortality by 15 to 20 percent, the diagnosis in many cases — approximately 15 percent of all breast cancers — occurs after a patient has […]

While many studies have looked at how to limit patients to a short course of opioids, few have examined the other side of the issue: Are there some patients for […]

Job Vacancies and Immigration: Evidence from the Mariel Supply Shock L. Jason Anastasopoulos, George J. Borjas, Gavin G. Cook, and Michael Lachanski We use the Conference Board’s Help-Wanted Index (HWI) […]

Controlling for Unmeasured Confounding in the Presence of Time: Instrumental Variable for Trend Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy, Dylan S. Small Unmeasured confounding is a key threat […]

Bayesian Nonparametric Cost-Effectiveness Analyses: Causal Estimation and Adaptive Subgroup Discovery Arman Oganisian, Nandita Mitra, Jason Roy Cost-effectiveness analyses (CEAs) are at the center of health economic decision making. While these […]

Moving Toward Rigorous Evaluation of Mobile Health Interventions Kristin A. Linn Qian, Klasnja and Murphy provide an assumption that allows for unbiased estimation of treatment effects in microrandomized trials when […]

While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce […]

School administrators and public health officials are important players in the choices our children make during meals at school. This evidence review of environmental and policy strategies to improve school […]

Like many scientific disciplines, neuroscience has increasingly attempted to confront pervasive gender imbalances within the field. While much of the conversation has centered around publishing and conference participation, recent research […]

Total brain white matter lesion volume is the most widely established MRI outcome measure in studies of multiple sclerosis. To estimate white matter lesion volume, there are a number of […]

A non‐parametric projection‐based estimator for the probability of causation, with application to water sanitation in Kenya Maria Cuellar and Edward H. Kennedy Current estimation methods for the probability of causation […]

A great deal of neuroimaging research focuses on voxel-wise analysis or segmentation of damaged tissue, yet many diseases are characterized by diffuse or non-regional neuropathology. In simple cases, these processes […]

The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using […]

Spatial extent inference (SEI) is widely used across neuroimaging modalities to adjust for multiple comparisons when studying brain-phenotype associations that inform our understanding of disease. Recent studies have shown that […]

A Bayesian Nonparametric Model for Zero-Inflated Outcomes: Prediction, Clustering, and Causal Estimation Arman Oganisian, Nandita Mitra, and Jason Roy Researchers are often interested in predicting outcomes, conducting clustering analysis to […]

Bayesian Longitudinal Causal Inference in the Analysis of the Public Health Impact of Pollutant Emissions Chanmin Kim, Corwin M Zigler, Michael J Daniels, Christine Choirat, and Jason A Roy Pollutant […]

Patterns of Effects and Sensitivity Analysis for Differences-in-Differences Luke J. Keele, Dylan S. Small, Jesse Y. Hsu, and Colin B. Fogarty Applied analysts often use the differences-in-differences (DID) method to […]

Robust causal inference with continuous instruments using the local instrumental variable curve Edward H. Kennedy, Scott Lorch, and Dylan S. Small Instrumental variables are commonly used to estimate effects of […]

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis (MS). The most widely established MRI outcome measure is the […]

In neuroimaging, hundreds to hundreds of thousands of tests are performed across a set of brain regions or all locations in an image. Recent studies have shown that the most […]

The central vein sign is a promising MR imaging diagnostic biomarker for multiple sclerosis. Recent studies have demonstrated that patients with MS have higher proportions of white matter lesions with […]

As the field of neuroimaging grows, it can be difficult for scientists within the field to gain and maintain a detailed understanding of its ever-changing landscape. While collaboration and citation […]

Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables Wang L, Tchetgen Tchetgen E. Instrumental variables (IVs) are widely used for estimating causal effects in the […]

In the fields of neuroimaging and genetics, a key goal is testing the association of a single outcome with a very high-dimensional imaging or genetic variable. Often, summary measures of […]

Estimating cost-effectiveness from claims and registry data with measured and unmeasured confounders Elizabeth Handorf, Daniel Heitjan, Justin Bekelman, Nandita Mitra Link The analysis of observational data to determine the cost-effectiveness […]

Estimating scaled treatment effects with multiple outcomes Edward H Kennedy, Shreya Kangovi, Nandita Mitra Link In classical study designs, the aim is often to learn about the effects of a […]

Eliminating survivor bias in two-stage instrumental variable estimators. Vansteelandt S, Walter S, Tchetgen Tchetgen E. Link Mendelian randomization studies commonly focus on elderly populations. This makes the instrumental variables analysis […]

Sensitivity analysis and power for instrumental variable studies Wang X, Jiang Y, Zhang NR, Small DS. Link In observational studies to estimate treatment effects, unmeasured confounding is often a concern. […]

Bayesian nonparametric generative models for causal inference with missing at random covariates Jason Roy, Kirsten J. Lum, Bret Zeldow , Jordan D. Dworkin, Vincent Lo Re III, and Michael J. […]

A general approach to evaluating the bias of 2‐stage instrumental variable estimators Fei Wan, Dylan Small, and Nandita Mitra Unmeasured confounding is a common concern when researchers attempt to estimate […]

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis. While WML have been studied for over two decades using […]

This study introduces a novel technique for counting pathologically distinct lesions using cross-sectional data, and demonstrates its ability to recover obscured longitudinal information. The proposed count works by incorporating information […]

With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, […]

Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer […]

MR imaging can be used to measure structural changes in the brains of individuals with multiple sclerosis and is essential for diagnosis, longitudinal monitoring, and therapy evaluation. The North American […]

A doubly robust approach for cost–effectiveness estimation from observational data Li, Vachani, Epstein, Mitra Estimation of common cost–effectiveness measures, including the incremental cost–effectiveness ratio and the net monetary benefit, is […]

A new, powerful approach to the study of effect modification in observational studies Kwonsang Lee, Dylan S. Small, Paul R. Rosenbaum Effect modification occurs when the magnitude or stability of […]

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 […]