Contrast-specific propensity scores for causal inference with multiple interventions.

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. On this dataset, our method generates heterogeneous treatment recommendations for patients among three options: Statins, fibrates, and non-pharmacological treatment to control patients' lipid ratios (total cholesterol divided by high-density lipoprotein level). In our numerical study, our proposed method generated more stable estimates compared to a benchmark method based on a multi-dimensional propensity score.

Education Abroad and College Completion.

While 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. Using nearest-neighbor matching techniques that accounted for a myriad of potentially confounding variables along with matching on institution, the analysis found positive impacts of education abroad on graduation within 4 and 6 years and on cumulative GPA at graduation. A very small increase in credit hours earned emerged, counterbalanced by a small decrease in time-to-degree associated with studying abroad. Overall, the results warrant conclusions that studying abroad does not impede timely graduation. To the contrary, encouraging students to study abroad promotes college completion. These results held similarly for students who had multiple study abroad experiences, and who have studied abroad for varying program lengths.

Estimating adjusted risk differences by multiply-imputing missing control binary potential outcomes following propensity score-matching.

We 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. We found that imputing potential outcomes under control (either single imputation or multiple imputation) resulted in a substantial reduction in bias compared with what was achieved using conventional nearest neighbor matching alone. Increasing the number of imputed potential outcomes under control resulted in more efficient estimation, with more efficient estimation of the estimated risk difference when increasing the number of the imputed potential outcomes. The greatest relative increase in efficiency was achieved by imputing five potential outcomes; once 20 outcomes under control were imputed for each matched treated subject, further improvements in efficiency were negligible. We also examined the effect of the number of these imputed potential outcomes on: (i) estimated standard errors; (ii) mean squared error; (iii) coverage of estimated confidence intervals. We illustrate the application of the method by estimating the effect on the risk of death within 1 year of prescribing beta-blockers to patients discharged from hospital with a diagnosis of heart failure.

Automatic detection of influential actors in disinformation networks.

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. We demonstrate its capability on real-world hostile IO campaigns with Twitter datasets collected during the 2017 French presidential elections and known IO accounts disclosed by Twitter over a broad range of IO campaigns (May 2007 to February 2020), over 50,000 accounts, 17 countries, and different account types including both trolls and bots. Our system detects IO accounts with 96% precision, 79% recall, and 96% area-under-the precision-recall (P-R) curve; maps out salient network communities; and discovers high-impact accounts that escape the lens of traditional impact statistics based on activity counts and network centrality. Results are corroborated with independent sources of known IO accounts from US Congressional reports, investigative journalism, and IO datasets provided by Twitter.

Nonstandard conditionally specified models for nonignorable missing data.

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. W. Tukey, the modeling assumptions about the distributions are either assessable or are designed to allow relatively easy incorporation of substantive knowledge about the problem at hand, thereby offering a possibly realistic portrayal of the data, both observed and missing. We develop Tukey's representation for exponential-family models, propose a computationally tractable approach to inference in this class of models, and offer some general theoretical comments. We then illustrate the utility of this approach with an example in systems biology.

Associations of Health Literacy and Menu-Labeling Usage With Sugar-Sweetened Beverage Intake Among Adults in Mississippi, 2016.

PURPOSE: 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. PARTICIPANTS: Adults living in Mississippi (n = 4549). MEASURES: Outcome variable was SSB intake (regular soda, fruit drinks, sweet tea, and sports/energy drinks). Exposure variables were 3 HL questions (find information, understand oral information, and understand written information) and ML usage among adults who eat at fast-food/chain restaurants (user, nonuser, and do not notice ML). ANALYSIS: Multinomial logistic regressions were used to estimate adjusted odds ratios (aOR) and 95% confidence intervals (CI) for SSB intake ≥1 time/d (reference: 0 times/d) associated with HL and ML. RESULTS: In Mississippi, 46.8% of adults consumed SSB ≥1 time/d, and 26.9% consumed ≥2 times/d. The odds of consuming SSBs ≥1 time/d were higher among adults with lower HL (aOR = 1.7; 95% CI = 1.3-2.2) than those with higher HL. Among adults who ate at fast-food/chain restaurants, the odds of consuming SSBs ≥1 time/d were higher among nonusers of ML (aOR = 2.3; 95% CI = 1.7-3.1) and adults who did not notice ML (aOR = 1.8; 95% CI = 1.3-2.6) than ML users. CONCLUSION: Adults with lower HL and adults who do not use or notice ML consumed more SSBs in Mississippi. Understanding why lower HL and no ML usage are linked to SSB intake could guide the design of interventions to reduce SSB intake in this population.

Catalytic prior distributions with application to generalized linear models.

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. In simulations, the resulting posterior estimation using such a catalytic prior outperforms maximum likelihood estimation from the working model and is generally comparable with or superior to existing competitive methods in terms of frequentist prediction accuracy of point estimation and coverage accuracy of interval estimation. The catalytic priors have simple interpretations and are easy to formulate.

Covariate-adjusted survival analyses in propensity-score matched samples: Imputing potential time-to-event outcomes.

Matching 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. In biomedical and epidemiological research, survival or time-to-event outcomes are common. We propose a method to combine regression adjustment and propensity score matching to estimate survival curves and hazard ratios based on estimating an imputed potential outcome under control for each successfully matched treated subject, which is accomplished using either an accelerated failure time parametric survival model or a Cox proportional hazard model that is fit to the matched control subjects. That is, a fitted model is then applied to the matched treated subjects to allow simulation of the missing potential outcome under control for each treated subject. Conventional survival analyses (e.g., estimation of survival curves and hazard ratios) can then be conducted using the observed outcome under treatment and the imputed outcome under control. We evaluated the repeated-sampling bias of the proposed methods using simulations. When using nearest neighbor matching, the proposed method resulted in decreased bias compared to crude analyses in the matched sample. We illustrate the method in an example prescribing beta-blockers at hospital discharge to patients hospitalized with heart failure.

Toxoplasmosis: The Heart of the Diagnosis.

Toxoplasma gondii is 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 (Odocoileus virginianus) 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. This case highlights important public health concerns about deer-to-human transmission of Toxoplasma.

Bridging observational studies and randomized experiments by embedding the former in the latter.

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 p-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. For the hypothetical validity of these statements, the analysis must embed the observational study in a hypothetical randomized experiment that created the observed data, or a subset of that hypothetical randomized data set. This multistage effort with thought-provoking tasks involves: (1) a purely conceptual stage that precisely formulate the causal question in terms of a hypothetical randomized experiment where the exposure is assigned to units; (2) a design stage that approximates a randomized experiment before any outcome data are observed, (3) a statistical analysis stage comparing the outcomes of interest in the exposed and non-exposed units of the hypothetical randomized experiment, and (4) a summary stage providing conclusions about statistical evidence for the sizes of possible causal effects. Stages 2 and 3 may rely on modern computing to implement the effort, whereas Stage 1 demands careful scientific argumentation to make the embedding plausible to scientific readers of the proffered statistical analysis. Otherwise, the resulting analysis is vulnerable to criticism for being simply a presentation of scientifically meaningless arithmetic calculations. The conceptually most demanding tasks are often the most scientifically interesting to the dedicated researcher and readers of the resulting statistical analyses. This perspective is rarely implemented with any rigor, for example, completely eschewing the first stage. We illustrate our approach using an example examining the effect of parental smoking on children's lung function collected in families living in East Boston in the 1970s.

Sequential rerandomization.

The 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. Our work offers a mathematical framework for sequential rerandomization designs, where the experimental units are enrolled in groups. We formulate an adaptive rerandomization procedure for balancing treatment/control assignments over some continuous or binary covariates, using Mahalanobis distance as the imbalance measure. We prove in our key result that given the same number of rerandomizations, in expected value, under certain mild assumptions, sequential rerandomization achieves better covariate balance than rerandomization at one time.

Asymptotic theory of rerandomization in treatment-control experiments.

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. We develop asymptotic theory for rerandomization without these assumptions, which reveals a non-Gaussian asymptotic distribution for this estimator, specifically a linear combination of a Gaussian random variable and truncated Gaussian random variables. This distribution follows because rerandomization affects only the projection of potential outcomes onto the covariate space but does not affect the corresponding orthogonal residuals. We demonstrate that, compared with complete randomization, rerandomization reduces the asymptotic quantile ranges of the difference-in-means estimator. Moreover, our work constructs accurate large-sample confidence intervals for the average causal effect.

Randomization to randomization probability: Estimating treatment effects under actual conditions of use.

Blinded 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. In the R2R design, participants are first randomized to a value, π, denoting their probability of receiving treatment (vs. placebo). Subjects are then told their value of π and randomized to either treatment or placebo with probabilities π and 1-π, respectively. Analysis of the treatment effect includes statistical controls for π (necessary for causal inference) and typically a π-by-treatment interaction. Random assignment of subjects to π and disclosure of its value to subjects manipulates subject expectations about receiving the treatment without deception. This method offers a better treatment effect estimate under actual conditions of use than does a conventional RCT. Design properties, guidelines for power analyses, and limitations of the approach are discussed. We illustrate the design by implementing an RCT of caffeine effects on mood and vigilance and show that some of the actual effects of caffeine differ by the expectation that one is receiving the active drug. (PsycINFO Database Record

Launching Effectiveness Research to Guide Practice in Neurosurgery: A National Institute Neurological Disorders and Stroke Workshop Report.

This 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. The workshop considered possible solutions, such as (1) statistical methods for demonstrating causality using observational data; (2) characteristics required of a registry supporting effectiveness research; (3) trial designs combining advantages of observational studies and RCTs; and (4) equipoise, an identified challenge for RCTs. In the future, advances in information technology potentially could lead to creation of a massive database where clinical data from all neurosurgeons are integrated and analyzed, ending the separation of clinical research and practice and leading to a new "science of practice."

Drug Repurposing: New Treatments for Zika Virus Infection?

To 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.

Fisher, Neyman, and Bayes at FDA.

The 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. Because two brilliant statisticians, Ronald Fisher and Jerzy Neyman, are often credited with instilling the traditional frequentist approach in current practice, I argue that both men were actually seeking very Bayesian answers, and neither would have endorsed the rigid application of their ideas.

Systems Biology-Based Investigation of Cellular Antiviral Drug Targets Identified by Gene-Trap Insertional Mutagenesis.

Viruses 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. In this study, we used libraries of randomly mutagenized cells to discover cellular genes that are essential for the replication of 10 distinct cytotoxic mammalian viruses, 1 gram-negative bacterium, and 5 toxins. We herein reported 712 candidate cellular genes, characterizing distinct topological network and evolutionary signatures, and occupying central hubs in the human interactome. Cell cycle phase-specific network analysis showed that host cell cycle programs played critical roles during viral replication (e.g. MYC and TAF4 regulating G0/1 phase). Moreover, the viral perturbation of host cellular networks reflected disease etiology in that host genes (e.g. CTCF, RHOA, and CDKN1B) identified were frequently essential and significantly associated with Mendelian and orphan diseases, or somatic mutations in cancer. Computational drug repositioning framework via incorporating drug-gene signatures from the Connectivity Map into the virus-host interactome identified 110 putative druggable antiviral targets and prioritized several existing drugs (e.g. ajmaline) that may be potential for antiviral indication (e.g. anti-Ebola). In summary, this work provides a powerful methodology with a tight integration of gene-trap insertional mutagenesis testing and systems biology to identify new antiviral targets and drugs for the development of broadly acting and targeted clinical antiviral therapeutics.

Linguistic Stereotyping in Older Adults' Perceptions of Health Care Aides.

The cultural and linguistic diversity of the U.S. health care provider workforce is expanding. Diversity among health care personnel such as paraprofessional health care assistants (HCAs)-many of whom are immigrants-means that intimate, high-stakes cross-cultural and cross-linguistic contact characterizes many health interactions. In particular, nonmainstream HCAs may face negative patient expectations because of patients' language stereotypes. In other contexts, reverse linguistic stereotyping has been shown to result in negative speaker evaluations and even reduced listening comprehension quite independently of the actual language performance of the speaker. The present study extends the language and attitude paradigm to older adults' perceptions of HCAs. Listeners heard the identical speaker of Standard American English as they watched interactions between an HCA and an older patient. Ethnolinguistic identities-either an Anglo native speaker of English or a Mexican nonnative speaker-were ascribed to HCAs by means of fabricated personnel files. Dependent variables included measures of perceived HCA language proficiency, personal characteristics, and professional competence, as well as listeners' comprehension of a health message delivered by the putative HCA. For most of these outcomes, moderate effect sizes were found such that the HCA with an ascribed Anglo identity-relative to the Mexican guise-was judged more proficient in English, socially superior, interpersonally more attractive, more dynamic, and a more satisfactory home health aide. No difference in listening comprehension emerged, but the Anglo guise tended to engender a more compliant listening mind set. Results of this study can inform both provider-directed and patient-directed efforts to improve health care services for members of all linguistic and cultural groups.

Health literacy-listening skill and patient questions following cancer prevention and screening discussions.

OBJECTIVE: 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 literacy-listening was assessed using the Cancer Message Literacy Test-Listening (CMLT-Listening). Two authors developed a coding scheme, which was applied to all questions. Analyses examined whether participants scoring above or below the median on the CMLT-Listening asked a similar variety of questions. RESULTS: Questions were coded into six major function categories: risks/benefits, procedure details, personalizing information, additional information, decision making and credibility. Participants who scored higher on the CMLT-Listening asked a greater variety of risks/benefits questions; those who scored lower asked a greater variety of questions seeking to personalize information. This difference persisted after adjusting for education. CONCLUSION: Patients' health literacy-listening is associated with distinctive patterns of question utilization following cancer screening and prevention counselling. Providers should not only be responsive to the question functions the patient favours, but also seek to ensure that the patient is exposed to the full range of information needed for shared decision making.

Improving Interactive Health Literacy Skills of Older Adults: Lessons Learned From Formative Organizational Research With Community Partners.

BACKGROUND: 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. LESSONS LEARNED: The observed brevity of volunteer-client interaction led program planners to incorporate substantial emphasis on communicating with older adults into the health literacy coach training curriculum. Ethnographic observations made clear that program materials had to be portable and fit it in with the mobile nature of MOW delivery. CONCLUSIONS: Formative organizational research can greatly increase the chance of successful implementation of public health interventions when those interventions will be implemented in partnerships with community-based organizations in diverse settings and with varying practices.