Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy.

Determining causes of deaths (CODs) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or "domains") necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this article, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a prespecified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. The posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation dataset. The article concludes with a discussion of limitations and future directions.

The microbial biosynthesis of noncanonical terpenoids.

Terpenoids are a class of structurally complex, naturally occurring compounds found predominantly in plant, animal, and microorganism secondary metabolites. Classical terpenoids typically have carbon atoms in multiples of five and follow well-defined carbon skeletons, whereas noncanonical terpenoids deviate from these patterns. These noncanonical terpenoids often result from the methyltransferase-catalyzed methylation modification of substrate units, leading to irregular carbon skeletons. In this comprehensive review, various activities and applications of these noncanonical terpenes have been summarized. Importantly, the review delves into the biosynthetic pathways of noncanonical terpenes, including those with C6, C7, C11, C12, and C16 carbon skeletons, in bacteria and fungi host. It also covers noncanonical triterpenes synthesized from non-squalene substrates and nortriterpenes in Ganoderma lucidum, providing detailed examples to elucidate the intricate biosynthetic processes involved. Finally, the review outlines the potential future applications of noncanonical terpenoids. In conclusion, the insights gathered from this review provide a reference for understanding the biosynthesis of these noncanonical terpenes and pave the way for the discovery of additional unique and novel noncanonical terpenes. KEY POINTS: •The activities and applications of noncanonical terpenoids are introduced. •The noncanonical terpenoids with irregular carbon skeletons are presented. •The microbial biosynthesis of noncanonical terpenoids is summarized.

Using machine learning with intensive longitudinal data to predict depression and suicidal ideation among medical interns over time.

BACKGROUND: Use of intensive longitudinal methods (e.g. ecological momentary assessment, passive sensing) and machine learning (ML) models to predict risk for depression and suicide has increased in recent years. However, these studies often vary considerably in length, ML methods used, and sources of data. The present study examined predictive accuracy for depression and suicidal ideation (SI) as a function of time, comparing different combinations of ML methods and data sources. METHODS: Participants were 2459 first-year training physicians (55.1% female; 52.5% White) who were provided with Fitbit wearable devices and assessed daily for mood. Linear [elastic net regression (ENR)] and non-linear (random forest) ML algorithms were used to predict depression and SI at the first-quarter follow-up assessment, using two sets of variables (daily mood features only, daily mood features + passive-sensing features). To assess accuracy over time, models were estimated iteratively for each of the first 92 days of internship, using data available up to that point in time. RESULTS: ENRs using only the daily mood features generally had the best accuracy for predicting mental health outcomes, and predictive accuracy within 1 standard error of the full 92 day models was attained by weeks 7-8. Depression at 92 days could be predicted accurately (area under the curve >0.70) after only 14 days of data collection. CONCLUSIONS: Simpler ML methods may outperform more complex methods until passive-sensing features become better specified. For intensive longitudinal studies, there may be limited predictive value in collecting data for more than 2 months.

Integrating sample similarities into latent class analysis: a tree-structured shrinkage approach.

This paper is concerned with using multivariate binary observations to estimate the probabilities of unobserved classes with scientific meanings. We focus on the setting where additional information about sample similarities is available and represented by a rooted weighted tree. Every leaf in the given tree contains multiple samples. Shorter distances over the tree between the leaves indicate a priori higher similarity in class probability vectors. We propose a novel data integrative extension to classical latent class models with tree-structured shrinkage. The proposed approach enables (1) borrowing of information across leaves, (2) estimating data-driven leaf groups with distinct vectors of class probabilities, and (3) individual-level probabilistic class assignment given the observed multivariate binary measurements. We derive and implement a scalable posterior inference algorithm in a variational Bayes framework. Extensive simulations show more accurate estimation of class probabilities than alternatives that suboptimally use the additional sample similarity information. A zoonotic infectious disease application is used to illustrate the proposed approach. The paper concludes by a brief discussion on model limitations and extensions.

Advances in the optimization of central carbon metabolism in metabolic engineering.

Central carbon metabolism (CCM), including glycolysis, tricarboxylic acid cycle and the pentose phosphate pathway, is the most fundamental metabolic process in the activities of living organisms that maintains normal cellular growth. CCM has been widely used in microbial metabolic engineering in recent years due to its unique regulatory role in cellular metabolism. Using yeast and Escherichia coli as the representative organisms, we summarized the metabolic engineering strategies on the optimization of CCM in eukaryotic and prokaryotic microbial chassis, such as the introduction of heterologous CCM metabolic pathways and the optimization of key enzymes or regulatory factors, to lay the groundwork for the future use of CCM optimization in metabolic engineering. Furthermore, the bottlenecks in the application of CCM optimization in metabolic engineering and future application prospects are summarized.

Preferences for Sexually Transmitted Infection and Cancer Vaccines in the United States and in China.

OBJECTIVES: This study assessed preferences for hypothetical vaccines for children in 2 large vaccine markets according to how the vaccine-preventable disease is transmitted via a discrete choice experiment. METHODS: Surveys in China (N = 1350) and the United States (N = 1413) were conducted from April to May 2021. The discrete choice experiment included attributes of cost, age at vaccination, transmission mode of the vaccine-preventable disease, and whether the vaccine prevents cancer. Preference utilities were modeled in a Bayesian, multinomial logistic regression model, and respondents were grouped by vaccine preference classification through a latent class analysis. RESULTS: Individuals favored vaccines against diseases with transmission modes other than sexual transmission (vaccine for sexually transmitted infection [STI] vs airborne disease, in the United States, odds ratio 0.71; 95% credible interval 0.64-0.78; in China, odds ratio 0.76; 95% credible interval 0.69-0.84). The latent class analysis revealed 6 classes: vaccine rejecters (19% in the United States and 8% in China), careful deciders (18% and 17%), preferring cancer vaccination (20% and 19%), preferring vaccinating children at older ages (10% and 11%), preferring vaccinating older ages, but indifferent about cancer vaccines (23% and 25%), and preferring vaccinating children at younger ages (10% and 19%). Vaccine rejection was higher with age in the United States versus more vaccine rejection among those at the age of 18 to 24 and ≥ 64 years in China. CONCLUSION: The public had strong preferences against giving their child an STI vaccine, and the class preferring a cancer vaccine was less accepting of an STI vaccine. Overall, this study points to the need for more education about how some STI vaccines could also prevent cancers.

Advances in the biosynthesis and metabolic engineering of rare ginsenosides.

Rare ginsenosides are the deglycosylated secondary metabolic derivatives of major ginsenosides, and they are more readily absorbed into the bloodstream and function as active substances. The traditional preparation methods hindered the potential application of these effective components. The continuous elucidation of ginsenoside biosynthesis pathways has rendered the production of rare ginsenosides using synthetic biology techniques effective for their large-scale production. Previously, only the progress in the biosynthesis and biotechnological production of major ginsenosides was highlighted. In this review, we summarized the recent advances in the identification of key enzymes involved in the biosynthetic pathways of rare ginsenosides, especially the glycosyltransferases (GTs). Then the construction of microbial chassis for the production of rare ginsenosides, mainly in Saccharomyces cerevisiae, was presented. In the future, discovery of more GTs and improving their catalytic efficiencies are essential for the metabolic engineering of rare ginsenosides. This review will give more clues and be helpful for the characterization of the biosynthesis and metabolic engineering of rare ginsenosides. KEY POINTS: • The key enzymes involved in the biosynthetic pathways of rare ginsenosides are summarized. • The recent progress in metabolic engineering of rare ginsenosides is presented. • The discovery of glycosyltransferases is essential for the microbial production of rare ginsenosides in the future.

An Automatically Adaptive Digital Health Intervention to Decrease Opioid-Related Risk While Conserving Counselor Time: Quantitative Analysis of Treatment Decisions Based on Artificial Intelligence and Patient-Reported Risk Measures.

BACKGROUND: Some patients prescribed opioid analgesic (OA) medications for pain experience serious side effects, including dependence, sedation, and overdose. As most patients are at low risk for OA-related harms, risk reduction interventions requiring multiple counseling sessions are impractical on a large scale. OBJECTIVE: This study evaluates whether an intervention based on reinforcement learning (RL), a field of artificial intelligence, learned through experience to personalize interactions with patients with pain discharged from the emergency department (ED) and decreased self-reported OA misuse behaviors while conserving counselors' time. METHODS: We used data representing 2439 weekly interactions between a digital health intervention ("Prescription Opioid Wellness and Engagement Research in the ED" [PowerED]) and 228 patients with pain discharged from 2 EDs who reported recent opioid misuse. During each patient's 12 weeks of intervention, PowerED used RL to select from 3 treatment options: a brief motivational message delivered via an interactive voice response (IVR) call, a longer motivational IVR call, or a live call from a counselor. The algorithm selected session types for each patient each week, with the goal of minimizing OA risk, defined in terms of a dynamic score reflecting patient reports during IVR monitoring calls. When a live counseling call was predicted to have a similar impact on future risk as an IVR message, the algorithm favored IVR to conserve counselor time. We used logit models to estimate changes in the relative frequency of each session type as PowerED gained experience. Poisson regression was used to examine the changes in self-reported OA risk scores over calendar time, controlling for the ordinal session number (1st to 12th). RESULTS: Participants on average were 40 (SD 12.7) years of age; 66.7% (152/228) were women and 51.3% (117/228) were unemployed. Most participants (175/228, 76.8%) reported chronic pain, and 46.2% (104/225) had moderate to severe depressive symptoms. As PowerED gained experience through interactions over a period of 142 weeks, it delivered fewer live counseling sessions than brief IVR sessions (P=.006) and extended IVR sessions (P<.001). Live counseling sessions were selected 33.5% of the time in the first 5 weeks of interactions (95% CI 27.4%-39.7%) but only for 16.4% of sessions (95% CI 12.7%-20%) after 125 weeks. Controlling for each patient's changes during the course of treatment, this adaptation of treatment-type allocation led to progressively greater improvements in self-reported OA risk scores (P<.001) over calendar time, as measured by the number of weeks since enrollment began. Improvement in risk behaviors over time was especially pronounced among patients with the highest risk at baseline (P=.02). CONCLUSIONS: The RL-supported program learned which treatment modalities worked best to improve self-reported OA risk behaviors while conserving counselors' time. RL-supported interventions represent a scalable solution for patients with pain receiving OA prescriptions. TRIAL REGISTRATION: Clinicaltrials.gov NCT02990377; https://classic.clinicaltrials.gov/ct2/show/NCT02990377.

Vaccine Hesitancy During the COVID-19 Pandemic: A Latent Class Analysis of Middle-Aged and Older US Adults.

It is important to distinguish between apprehensions that lead to vaccine rejection and those that do not. In this study, we (1) identifed latent classes of individuals by vaccination attitudes, and (2) compared classes of individuals by sociodemographic characteristics COVID-19 vaccination, and risk reduction behaviors. The COVID-19 Coping Study is a longitudinal cohort of US adults aged ≥ 55 years (n = 2358). We categorized individuals into three classes based on the adult Vaccine Hesitancy Scale using latent class analysis (LCA). The associations between class membership and sociodemographic characteristics, COVID-19 vaccination, and other behaviors were assessed using chi-square tests. In total, 88.9% were Vaccine Acceptors, 8.6% were Vaccine Ambivalent, and 2.5% Vaccine Rejectors. At the end, 90.7% of Acceptors, 62.4% of the Ambivalent, and 30.7% of the Rejectors had been vaccinated. The Ambivalent were more likely to be Black or Hispanic, and adopted social distancing and mask wearing behaviors intermediate to that of the Acceptors and Rejectors. Targeting the Vaccine Ambivalent may be an efficient way of increasing vaccination coverage. Controlling the spread of disease during a pandemic requires tailoring vaccine messaging to their concerns, e.g., through working with trusted community leaders, while promoting other risk reduction behaviors.

Association of Polygenic Risk Scores for Multiple Cancers in a Phenome-wide Study: Results from The Michigan Genomics Initiative.

Health systems are stewards of patient electronic health record (EHR) data with extraordinarily rich depth and breadth, reflecting thousands of diagnoses and exposures. Measures of genomic variation integrated with EHRs offer a potential strategy to accurately stratify patients for risk profiling and discover new relationships between diagnoses and genomes. The objective of this study was to evaluate whether polygenic risk scores (PRS) for common cancers are associated with multiple phenotypes in a phenome-wide association study (PheWAS) conducted in 28,260 unrelated, genotyped patients of recent European ancestry who consented to participate in the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine. PRS for 12 cancer traits were calculated using summary statistics from the NHGRI-EBI catalog. A total of 1,711 synthetic case-control studies was used for PheWAS analyses. There were 13,490 (47.7%) patients with at least one cancer diagnosis in this study sample. PRS exhibited strong association for several cancer traits they were designed for, including female breast cancer, prostate cancer, melanoma, basal cell carcinoma, squamous cell carcinoma, and thyroid cancer. Phenome-wide significant associations were observed between PRS and many non-cancer diagnoses. To differentiate PRS associations driven by the primary trait from associations arising through shared genetic risk profiles, the idea of "exclusion PRS PheWAS" was introduced. Further analysis of temporal order of the diagnoses improved our understanding of these secondary associations. This comprehensive PheWAS used PRS instead of a single variant.

A Bayesian approach to restricted latent class models for scientifically structured clustering of multivariate binary outcomes.

This paper presents a model-based method for clustering multivariate binary observations that incorporates constraints consistent with the scientific context. The approach is motivated by the precision medicine problem of identifying autoimmune disease patient subsets or classes who may require different treatments. We start with a family of restricted latent class models or RLCMs. However, in the motivating example and many others like it, the unknown number of classes and the definition of classes using binary states are among the targets of inference. We use a Bayesian approach to RLCMs in order to use informative prior assumptions on the number and definitions of latent classes to be consistent with scientific knowledge so that the posterior distribution tends to concentrate on smaller numbers of clusters and sparser binary patterns. The paper derives a posterior sampling algorithm based on Markov chain Monte Carlo with split-merge updates to efficiently explore the space of clustering allocations. Through simulations under the assumed model and realistic deviations from it, we demonstrate greater interpretability of results and superior finite-sample clustering performance for our method compared to common alternatives. The methods are illustrated with an analysis of protein data to detect clusters representing autoantibody classes among scleroderma patients.

Probabilistic cause-of-disease assignment using case-control diagnostic tests: A latent variable regression approach.

Optimal prevention and treatment strategies for a disease of multiple causes, such as pneumonia, must be informed by the population distribution of causes among cases, or cause-specific case fractions (CSCFs). CSCFs may further depend on additional explanatory variables. Existing methodological literature in disease etiology research does not fully address the regression problem, particularly under a case-control design. Based on multivariate binary non-gold-standard diagnostic data and additional covariate information, this article proposes a novel and unified regression modeling framework for estimating covariate-dependent CSCF functions in case-control disease etiology studies. The model leverages critical control data for valid probabilistic cause assignment for cases. We derive an efficient Markov chain Monte Carlo algorithm for flexible posterior inference. We illustrate the inference of CSCF functions using extensive simulations and show that the proposed model produces less biased estimates and more valid inference of the overall CSCFs than analyses that omit covariates. A regression analysis of pediatric pneumonia data reveals the dependence of CSCFs upon season, age, human immunodeficiency virus status and disease severity. The article concludes with a brief discussion on model extensions that may further enhance the utility of the regression model in broader disease etiology research.

Clinical and sociodemographic factors associated with anticoagulant use for cancer associated venous thromboembolism.

Cancer associated thrombosis (CAT) is a leading cause of death among patients with cancer. It is not clear if non-clinical factors are associated with anticoagulation receipt. We conducted a retrospective cohort study of Optum's de-identified Clinformatics® Database of adults with cancer diagnosed between 2009 and 2016 who developed CAT, treated with an outpatient anticoagulant (warfarin, low molecular weight heparin (LMWH), or a direct oral anticoagulant (DOAC)). Of 12,622 patients, three months after an episode of CAT, 1,485 (12%) were on LMWH, 1,546 (12%) on DOACs, and 9,591 (76%) were on warfarin. When controlling for other factors, anticoagulant use was significantly associated with socioeconomic factors, region, co-morbidities, type of thrombosis, and cancer subtype. Patients with a bachelor's degree or greater level of education were less likely to receive warfarin (OR: 0.77; 95% CI: [0.59, 0.99]; p < 0.046) or DOACs (OR: 0.67; 95% CI: [0.55, 0.82]; p < 0.001) compared to LMWH. Patients with higher income levels were more likely to receive LMWH or DOACs compared to warfarin, while patients across all income levels were equally likely to receive LMWH or DOACs. Non-clinical factors including income, education, and region, are associated with anticoagulation receipt three months after an episode of CAT. Sociodemographic factors may result in some patients receiving suboptimal care and contribute to non-guideline concordant care for CAT.

A hierarchical integrative group least absolute shrinkage and selection operator for analyzing environmental mixtures.

Environmental health studies are increasingly measuring multiple pollutants to characterize the joint health effects attributable to exposure mixtures. However, the underlying dose-response relationship between toxicants and health outcomes of interest may be highly nonlinear, with possible nonlinear interaction effects. Existing penalized regression methods that account for exposure interactions either cannot accommodate nonlinear interactions while maintaining strong heredity or are computationally unstable in applications with limited sample size. In this paper, we propose a general shrinkage and selection framework to identify noteworthy nonlinear main and interaction effects among a set of exposures. We design hierarchical integrative group least absolute shrinkage and selection operator (HiGLASSO) to (a) impose strong heredity constraints on two-way interaction effects (hierarchical), (b) incorporate adaptive weights without necessitating initial coefficient estimates (integrative), and (c) induce sparsity for variable selection while respecting group structure (group LASSO). We prove sparsistency of the proposed method and apply HiGLASSO to an environmental toxicants dataset from the LIFECODES birth cohort, where the investigators are interested in understanding the joint effects of 21 urinary toxicant biomarkers on urinary 8-isoprostane, a measure of oxidative stress. An implementation of HiGLASSO is available in the higlasso R package, accessible through the Comprehensive R Archive Network.

Estimating autoantibody signatures to detect autoimmune disease patient subsets.

Autoimmune diseases are characterized by highly specific immune responses against molecules in self-tissues. Different autoimmune diseases are characterized by distinct immune responses, making autoantibodies useful for diagnosis and prediction. In many diseases, the targets of autoantibodies are incompletely defined. Although the technologies for autoantibody discovery have advanced dramatically over the past decade, each of these techniques generates hundreds of possibilities, which are onerous and expensive to validate. We set out to establish a method to greatly simplify autoantibody discovery, using a pre-filtering step to define subgroups with similar specificities based on migration of radiolabeled, immunoprecipitated proteins on sodium dodecyl sulfate (SDS) gels and autoradiography [Gel Electrophoresis and band detection on Autoradiograms (GEA)]. Human recognition of patterns is not optimal when the patterns are complex or scattered across many samples. Multiple sources of errors-including irrelevant intensity differences and warping of gels-have challenged automation of pattern discovery from autoradiograms.In this article, we address these limitations using a Bayesian hierarchical model with shrinkage priors for pattern alignment and spatial dewarping. The Bayesian model combines information from multiple gel sets and corrects spatial warping for coherent estimation of autoantibody signatures defined by presence or absence of a grid of landmark proteins. We show the pre-processing creates more clearly separated clusters and improves the accuracy of autoantibody subset detection via hierarchical clustering. Finally, we demonstrate the utility of the proposed methods with GEA data from scleroderma patients.

Assessing Real-Time Moderation for Developing Adaptive Mobile Health Interventions for Medical Interns: Micro-Randomized Trial.

BACKGROUND: Individuals in stressful work environments often experience mental health issues, such as depression. Reducing depression rates is difficult because of persistently stressful work environments and inadequate time or resources to access traditional mental health care services. Mobile health (mHealth) interventions provide an opportunity to deliver real-time interventions in the real world. In addition, the delivery times of interventions can be based on real-time data collected with a mobile device. To date, data and analyses informing the timing of delivery of mHealth interventions are generally lacking. OBJECTIVE: This study aimed to investigate when to provide mHealth interventions to individuals in stressful work environments to improve their behavior and mental health. The mHealth interventions targeted 3 categories of behavior: mood, activity, and sleep. The interventions aimed to improve 3 different outcomes: weekly mood (assessed through a daily survey), weekly step count, and weekly sleep time. We explored when these interventions were most effective, based on previous mood, step, and sleep scores. METHODS: We conducted a 6-month micro-randomized trial on 1565 medical interns. Medical internship, during the first year of physician residency training, is highly stressful, resulting in depression rates several folds higher than those of the general population. Every week, interns were randomly assigned to receive push notifications related to a particular category (mood, activity, sleep, or no notifications). Every day, we collected interns' daily mood valence, sleep, and step data. We assessed the causal effect moderation by the previous week's mood, steps, and sleep. Specifically, we examined changes in the effect of notifications containing mood, activity, and sleep messages based on the previous week's mood, step, and sleep scores. Moderation was assessed with a weighted and centered least-squares estimator. RESULTS: We found that the previous week's mood negatively moderated the effect of notifications on the current week's mood with an estimated moderation of -0.052 (P=.001). That is, notifications had a better impact on mood when the studied interns had a low mood in the previous week. Similarly, we found that the previous week's step count negatively moderated the effect of activity notifications on the current week's step count, with an estimated moderation of -0.039 (P=.01) and that the previous week's sleep negatively moderated the effect of sleep notifications on the current week's sleep with an estimated moderation of -0.075 (P<.001). For all three of these moderators, we estimated that the treatment effect was positive (beneficial) when the moderator was low, and negative (harmful) when the moderator was high. CONCLUSIONS: These findings suggest that an individual's current state meaningfully influences their receptivity to mHealth interventions for mental health. Timing interventions to match an individual's state may be critical to maximizing the efficacy of interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03972293; http://clinicaltrials.gov/ct2/show/NCT03972293.

Nested partially latent class models for dependent binary data; estimating disease etiology.

The Pneumonia Etiology Research for Child Health (PERCH) study seeks to use modern measurement technology to infer the causes of pneumonia for which gold-standard evidence is unavailable. Based on case-control data, the article describes a latent variable model designed to infer the etiology distribution for the population of cases, and for an individual case given her measurements. We assume each observation is drawn from a mixture model for which each component represents one disease class. The model conisidered here addresses a major limitation of the traditional latent class approach by taking account of residual dependence among multivariate binary outcomes given disease class, hence reducing estimation bias, retaining efficiency and offering more valid inference. Such "local dependence" on each subject is induced in the model by nesting latent subclasses within each disease class. Measurement precision and covariation can be estimated using the control sample for whom the class is known. In a Bayesian framework, we use stick-breaking priors on the subclass indicators for model-averaged inference across different numbers of subclasses. Assessment of model fit and individual diagnosis are done using posterior samples drawn by Gibbs sampling. We demonstrate the utility of the method on simulated and on the motivating PERCH data.

Bayesian Estimation of Pneumonia Etiology: Epidemiologic Considerations and Applications to the Pneumonia Etiology Research for Child Health Study.

In pneumonia, specimens are rarely obtained directly from the infection site, the lung, so the pathogen causing infection is determined indirectly from multiple tests on peripheral clinical specimens, which may have imperfect and uncertain sensitivity and specificity, so inference about the cause is complex. Analytic approaches have included expert review of case-only results, case-control logistic regression, latent class analysis, and attributable fraction, but each has serious limitations and none naturally integrate multiple test results. The Pneumonia Etiology Research for Child Health (PERCH) study required an analytic solution appropriate for a case-control design that could incorporate evidence from multiple specimens from cases and controls and that accounted for measurement error. We describe a Bayesian integrated approach we developed that combined and extended elements of attributable fraction and latent class analyses to meet some of these challenges and illustrate the advantage it confers regarding the challenges identified for other methods.

Deductive derivation and turing-computerization of semiparametric efficient estimation.

Researchers often seek robust inference for a parameter through semiparametric estimation. Efficient semiparametric estimation currently requires theoretical derivation of the efficient influence function (EIF), which can be a challenging and time-consuming task. If this task can be computerized, it can save dramatic human effort, which can be transferred, for example, to the design of new studies. Although the EIF is, in principle, a derivative, simple numerical differentiation to calculate the EIF by a computer masks the EIF's functional dependence on the parameter of interest. For this reason, the standard approach to obtaining the EIF relies on the theoretical construction of the space of scores under all possible parametric submodels. This process currently depends on the correctness of conjectures about these spaces, and the correct verification of such conjectures. The correct guessing of such conjectures, though successful in some problems, is a nondeductive process, i.e., is not guaranteed to succeed (e.g., is not computerizable), and the verification of conjectures is generally susceptible to mistakes. We propose a method that can deductively produce semiparametric locally efficient estimators. The proposed method is computerizable, meaning that it does not need either conjecturing, or otherwise theoretically deriving the functional form of the EIF, and is guaranteed to produce the desired estimates even for complex parameters. The method is demonstrated through an example.

Estimation of treatment effects in matched-pair cluster randomized trials by calibrating covariate imbalance between clusters.

We address estimation of intervention effects in experimental designs in which (a) interventions are assigned at the cluster level; (b) clusters are selected to form pairs, matched on observed characteristics; and (c) intervention is assigned to one cluster at random within each pair. One goal of policy interest is to estimate the average outcome if all clusters in all pairs are assigned control versus if all clusters in all pairs are assigned to intervention. In such designs, inference that ignores individual level covariates can be imprecise because cluster-level assignment can leave substantial imbalance in the covariate distribution between experimental arms within each pair. However, most existing methods that adjust for covariates have estimands that are not of policy interest. We propose a methodology that explicitly balances the observed covariates among clusters in a pair, and retains the original estimand of interest. We demonstrate our approach through the evaluation of the Guided Care program.