Existing methods that use propensity scores for heterogeneous treatment effect estimation on non-experimental data do not readily extend to the case of more than two treatment options. In this work, we develop a new propensity score-based method for heterogeneous treatment effect estimation when there are three or more treatment options, and prove that it generates unbiased estimates. We demonstrate our method on a real patient registry of patients in Singapore with diabetic dyslipidemia.
View Article and Find Full Text PDFWhile some stakeholders presume that studying abroad distracts students from efficient pursuit of their programs of study, others regard education abroad as a high impact practice that fosters student engagement and hence college completion. The Consortium for Analysis of Student Success through International Education (CASSIE), compiled semester-by-semester records from 221,981 students across 35 institutions. Of those students, 30,549 had studied abroad.
View Article and Find Full Text PDFWe describe a new method to combine propensity-score matching with regression adjustment in treatment-control studies when outcomes are binary by multiply imputing potential outcomes under control for the matched treated subjects. This enables the estimation of clinically meaningful measures of effect such as the risk difference. We used Monte Carlo simulation to explore the effect of the number of imputed potential outcomes under control for the matched treated subjects on inferences about the risk difference.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2021
The weaponization of digital communications and social media to conduct disinformation campaigns at immense scale, speed, and reach presents new challenges to identify and counter hostile influence operations (IOs). This paper presents an end-to-end framework to automate detection of disinformation narratives, networks, and influential actors. The framework integrates natural language processing, machine learning, graph analytics, and a network causal inference approach to quantify the impact of individual actors in spreading IO narratives.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
August 2020
Data analyses typically rely upon assumptions about the missingness mechanisms that lead to observed versus missing data, assumptions that are typically unassessable. We explore an approach where the joint distribution of observed data and missing data are specified in a nonstandard way. In this formulation, which traces back to a representation of the joint distribution of the data and missingness mechanism, apparently first proposed by J.
View Article and Find Full Text PDFPurpose: Examine association of health literacy (HL) and menu-labeling (ML) usage with sugar-sweetened beverage (SSB) intake among adults in Mississippi.
Design: Quantitative, cross-sectional study.
Setting: 2016 Mississippi Behavioral Risk Factor Surveillance System data.
Proc Natl Acad Sci U S A
June 2020
A catalytic prior distribution is designed to stabilize a high-dimensional "working model" by shrinking it toward a "simplified model." The shrinkage is achieved by supplementing the observed data with a small amount of "synthetic data" generated from a predictive distribution under the simpler model. We apply this framework to generalized linear models, where we propose various strategies for the specification of a tuning parameter governing the degree of shrinkage and study resultant theoretical properties.
View Article and Find Full Text PDFis a common parasite that infects warm-blooded animals, including humans, and is a foodborne pathogen. We report a case of acute toxoplasmosis in a 76-year-old man after ingestion of the undercooked heart of a white-tailed deer () in Tennessee. The patient's adult grandson, who also consumed part of the heart, became ill with nearly identical symptoms, though he did not seek medical care.
View Article and Find Full Text PDFMatching on an estimated propensity score is frequently used to estimate the effects of treatments from observational data. Since the 1970s, different authors have proposed methods to combine matching at the design stage with regression adjustment at the analysis stage when estimating treatment effects for continuous outcomes. Previous work has consistently shown that the combination has generally superior statistical properties than either method by itself.
View Article and Find Full Text PDFThe seminal work of Morgan & Rubin (2012) considers rerandomization for all the units at one time.In practice, however, experimenters may have to rerandomize units sequentially. For example, a clinician studying a rare disease may be unable to wait to perform an experiment until all the experimental units are recruited.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
September 2018
Although complete randomization ensures covariate balance on average, the chance of observing significant differences between treatment and control covariate distributions increases with many covariates. Rerandomization discards randomizations that do not satisfy a predetermined covariate balance criterion, generally resulting in better covariate balance and more precise estimates of causal effects. Previous theory has derived finite sample theory for rerandomization under the assumptions of equal treatment group sizes, Gaussian covariate and outcome distributions, or additive causal effects, but not for the general sampling distribution of the difference-in-means estimator for the average causal effect.
View Article and Find Full Text PDFStat Methods Med Res
July 2019
Consider a statistical analysis that draws causal inferences from an observational dataset, inferences that are presented as being valid in the standard frequentist senses; i.e. the analysis produces: (1) consistent point estimates, (2) valid -values, valid in the sense of rejecting true null hypotheses at the nominal level or less often, and/or (3) confidence intervals, which are presented as having at least their nominal coverage for their estimands.
View Article and Find Full Text PDFBlinded randomized controlled trials (RCT) require participants to be uncertain if they are receiving a treatment or placebo. Although uncertainty is ideal for isolating the treatment effect from all other potential effects, it is poorly suited for estimating the treatment effect under actual conditions of intended use-when individuals are certain that they are receiving a treatment. We propose an experimental design, randomization to randomization probabilities (R2R), which significantly improves estimates of treatment effects under actual conditions of use by manipulating participant expectations about receiving treatment.
View Article and Find Full Text PDFThis workshop addressed challenges of clinical research in neurosurgery. Randomized controlled clinical trials (RCTs) have high internal validity, but often insufficiently generalize to real-world practice. Observational studies are inclusive but often lack sufficient rigor.
View Article and Find Full Text PDFTo date, no antiviral agents have been approved for treating Zika virus (ZIKV) infection. Two recent drug-repurposing studies published in Cell Host & Microbe and Nature Medicine demonstrated that screening FDA-approved drugs for antiviral activity is a promising strategy for identifying therapeutics with novel activity against ZIKV infection.
View Article and Find Full Text PDFViruses require host cellular factors for successful replication. A comprehensive systems-level investigation of the virus-host interactome is critical for understanding the roles of host factors with the end goal of discovering new druggable antiviral targets. Gene-trap insertional mutagenesis is a high-throughput forward genetics approach to randomly disrupt (trap) host genes and discover host genes that are essential for viral replication, but not for host cell survival.
View Article and Find Full Text PDFThe wise use of statistical ideas in practice essentially requires some Bayesian thinking, in contrast to the classical rigid frequentist dogma. This dogma too often has seemed to influence the applications of statistics, even at agencies like the FDA. Greg Campbell was one of the most important advocates there for more nuanced modes of thought, especially Bayesian statistics.
View Article and Find Full Text PDFBackground: Meals on Wheels (MOW) organizations are ideal community partners for delivering social support relating to health information exchange for vulnerable and home-bound older adults.
Objectives: This article illustrates how formative organizational evaluation can be used to adapt health literacy interventions delivered by community partners.
Methods: Key informant interviews and ethnographic observations were conducted as part of a formative organizational evaluation of potential community partners.
The cultural and linguistic diversity of the U.S. health care provider workforce is expanding.
View Article and Find Full Text PDFObjective: Patient question-asking is essential to shared decision making. We sought to describe patients' questions when faced with cancer prevention and screening decisions, and to explore differences in question-asking as a function of health literacy with respect to spoken information (health literacy-listening).
Methods: Four-hundred and thirty-three (433) adults listened to simulated physician-patient interactions discussing (i) prophylactic tamoxifen for breast cancer prevention, (ii) PSA testing for prostate cancer and (iii) colorectal cancer screening, and identified questions they would have.
Health and medical data are increasingly being generated, collected, and stored in electronic form in healthcare facilities and administrative agencies. Such data hold a wealth of information vital to effective health policy development and evaluation, as well as to enhanced clinical care through evidence-based practice and safety and quality monitoring. These initiatives are aimed at improving individuals' health and well-being.
View Article and Find Full Text PDFClostridium difficile is the leading cause of hospital-acquired diarrhea in the United States. The two main virulence factors of C. difficile are the large toxins, TcdA and TcdB, which enter colonic epithelial cells and cause fluid secretion, inflammation, and cell death.
View Article and Find Full Text PDFEstimation of causal effects in non-randomized studies comprises two distinct phases: design, without outcome data, and analysis of the outcome data according to a specified protocol. Recently, Gutman and Rubin (2013) proposed a new analysis-phase method for estimating treatment effects when the outcome is binary and there is only one covariate, which viewed causal effect estimation explicitly as a missing data problem. Here, we extend this method to situations with continuous outcomes and multiple covariates and compare it with other commonly used methods (such as matching, subclassification, weighting, and covariance adjustment).
View Article and Find Full Text PDFBy 'partially post-hoc' subgroup analyses, we mean analyses that compare existing data from a randomized experiment-from which a subgroup specification is derived-to new, subgroup-only experimental data. We describe a motivating example in which partially post hoc subgroup analyses instigated statistical debate about a medical device's efficacy. We clarify the source of such analyses' invalidity and then propose a randomization-based approach for generating valid posterior predictive p-values for such partially post hoc subgroups.
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