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Background Study: Technology‐supported learning environments, act as significant observational and enabling indicators for evaluating and encouraging the digital revolution of education, are of vital importance in current educational research. Keeping track of the dynamics of technology‐supported learning environment research allows for the enrichment of theoretical studies and a prompt innovation of talent‐training environments. Objectives: The present study carried out a bibliometric analysis of the core collection of WoS database over the last two decades. Method: This study is a bibliometric research. We applied CiteSpace and VOSviewer for co‐occurrence and evolution analysis. In addition, we extracted the fundamental ideas and thoughts through reading and analysis. Results: First, the study found that technology‐supported learning environment research is in a growth phase, with core journals such as Interactive Learning Environments or Computers & Education. The findings show that a core research team comprised of such scholars as Hwang Gwo‐Jen, Lester James C. and Wong Lung‐Hsiang. Key nations of publishing and research strength are from China, the United States, the United Kingdom and the Netherlands. Second, the hot topics in research on technology‐supported learning environments were virtual learning environments, technology‐enabled learning environments and interactive learning environments. Nine study specifics were derived from these three topics such as moulding function, generating scenario, stimulating sensation, effect verification, design idea, research reflection, inquiry and research, virtual community, and mixed environment. Lastly, the frontier evolution offers a pattern of development from enhancing performance to changing mode to incorporating experience. Conclusion: Looking ahead, our research recommendations for the field of technology‐supported learning environments include a multifaceted approach. We should aim to enhance research designs and methodologies, develop contemporary guiding theories and strive for a balanced representation across various educational domains, expanding our scope to encompass all academic disciplines. In terms of practical application, it is imperative to focus on the design and implementation of technology‐supported learning environments from three critical perspectives: "change‐demand," "student‐learning" and "online‐offline." These approaches will collectively ensure that technology‐supported learning environments are both innovative and responsive to the diverse needs of the educational landscape. Lay Description: What is already known about this topic: Technology‐supported learning environments are currently a focal and hot research topic in the field of education.Although numerous studies have been conducted on the learning environment of technical support, there has yet to be a systematic and comprehensive review of the related research. What this paper adds: This study provides a comprehensive review of research on the technology‐supported learning environments over the past two decades.This study primarily presents an overview of research on the technology‐supported learning environments over the past two decades, focusing on main literature, research strength, hot topics and frontier evolution. Implications for practice and/or policy: Researchers need to further advance research on technology‐supported learning environments by enriching research designs and methods, constructing up‐to‐date guiding theories and achieving a balance across different educational domains while extending the reach to all disciplines.Educational practitioners need to pay attention to the design and implementation of technology‐supported learning environments from the three viewpoints of "change‐demand," "student‐learning" and "online‐offline." [ABSTRACT FROM AUTHOR]

Background and Aims: The focus of this paper is on the improvement of substance use disorder (SUD) screening and measurement. Using a multi-dimensional item response theory model, the bifactor model, we provide a psychometric harmonization between SUD, depression, anxiety, trauma, social isolation, functional impairment and risk-taking behavior symptom domains, providing a more balanced view of SUD. The aims are to (1) develop the item-bank, (2) calibrate the item-bank using a bifactor model that includes a primary dimension and symptom-specific subdomains, (3) administer using computerized adaptive testing (CAT) and (4) validate the CAT-SUD in Spanish and English in the United States and Spain., Design: Item bank construction, item calibration phase, CAT-SUD validation phase., Setting: Primary care, community clinics, emergency departments and patient-to-patient referrals in Spain (Barcelona and Madrid) and the United States (Boston and Los Angeles)., Participants/cases: Calibration phase: the CAT-SUD was developed via simulation from complete item responses in 513 participants. Validation phase: 297 participants received the Composite International Diagnostic Interview (CIDI) and the CAT-SUD., Measurements: A total of 252 items from five subdomains: (1) SUD, (2) psychological disorders, (3) risky behavior, (4) functional impairment and (5) social support. CAT-SUD scale scores and CIDI SUD diagnosis., Findings: Calibration: the bifactor model provided excellent fit to the multi-dimensional item bank; 168 items had high loadings (> 0.4 with the majority > 0.6) on the primary SUD dimension. Using an average of 11 items (four to 26), which represents a 94% reduction in respondent burden (average administration time of approximately 2 minutes), we found a correlation of 0.91 with the 168-item scale (precision of 5 points on a 100-point scale)., Validation: strong agreement was found between the primary CAT-SUD dimension estimate and the results of a structured clinical interview. There was a 20-fold increase in the likelihood of a CIDI SUD diagnosis across the range of the CAT-SUD (AUC = 0.85)., Conclusions: We have developed a new approach for the screening and measurement of SUD and related severity based on multi-dimensional item response theory. The bifactor model harmonized information from mental health, trauma, social support and traditional SUD items to provide a more complete characterization of SUD. The CAT-SUD is highly predictive of a current SUD diagnosis based on a structured clinical interview, and may be predictive of the development of SUD in the future., (© 2020 Society for the Study of Addiction.)

Drug-drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDA's adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair-wise drug interactions, and methods to detect high-dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug-count response models for detecting high-dimensional drug combinations that induce myopathy. The "count" indicates the number of drugs in a combination. One model is called fixed probability mixture drug-count response model with a maximum risk threshold (FMDRM-MRT). The other model is called count-dependent probability mixture drug-count response model with a maximum risk threshold (CMDRM-MRT), in which the mixture probability is count dependent. Compared with the previous mixture drug-count response model (MDRM) developed by our group, these 2 new models show a better likelihood in detecting high-dimensional drug combinatory effects on myopathy. CMDRM-MRT identified and validated (54; 374; 637; 442; 131) 2-way to 6-way drug interactions, respectively, which induce myopathy in both EMR and FAERS databases. We further demonstrate FAERS data capture much higher maximum myopathy risk than EMR data do. The consistency of 2 mixture models' parameters and local false discovery rate estimates are evaluated through statistical simulation studies. [ABSTRACT FROM AUTHOR]

Land transformation from grassland to cropland in the Northern Great Plains (NGP) has become a growing concern among many stakeholders. A growing body of work has sought to determine the amount and rate of land use change with less emphasis on the systemic structures or feedback processes of land use decisions. This paper presents the development of a system dynamics simulation model to integrate ecological, economic, and social components influencing land use decisions, including cattle ranching, cropland production, rural communities, land quality, and public policies. Evaluation indicated that the model satisfactorily predicted historical land, agricultural commodity, and rural community data from the model structure. Reference modes for key variables, including the farmland area, were characterized by a bias correction of 0.999, root mean squared error of prediction of 0.053, R2 of 0.921, and concordance correlation coefficient of 0.0959. The model was robust under extreme and varying sensitivity tests, as well as adequately predicting land use under changing system context. The model's major contributions were the inclusion of decision-making feedbacks from economic and social signals with connectivity to land quality and elasticity values that drive land transformation. Limitations include lack of spatial input and output capabilities useful for visual interfacing. [ABSTRACT FROM AUTHOR]

Mental models are a vital part of complex engineered system design and operations and contribute to either success or failure in both system development and operations. This paper examines the Systems Modeling Language (SysML) viewpoints and views to determine whether they can illustrate elements of mental models and serve to increase communication and foster a common system understanding between individual stakeholders holding different perspectives. Two models were created in SysML, the first comparing SysML with established conceptual graphs of mental models and the second was the Lynx X‐ray Observatory, a project in concept development at National Aeronautics and Space Administration. This research revealed that shared SysML viewpoints and views can serve as conceptual graphs of shared mental models. It was also found that illustrating Lynx viewpoints and views was a valuable aid in eliciting concerns and desires of stakeholders, leading to a project more in line with desires of the stakeholders. [ABSTRACT FROM AUTHOR]

Summary: Background: Non‐alcoholic fatty liver conditions in adolescence are associated with premature mortality in adulthood. Effective screening could impact the population burden of this disease. Objectives: We sought to determine which adolescents should be screened for non‐alcoholic fatty liver using vibration‐controlled transient elastography. Methods: We simulated a non‐alcoholic fatty liver screening program of 938 adolescents from the National Health and Nutritional Examination Survey of 2017/2018. We stratified subjects by body mass index and metabolic parameters and analyzed our data using standard diagnostic statistical measures. Results: The weighted prevalence of non‐alcoholic fatty liver and non‐alcoholic fatty liver disease was 24.4%, and 3.8%, respectively. For all subjects with obesity (21.8% of the population), screening identified 61.8% of the non‐alcoholic fatty liver cases. In a category of all subjects with obesity and overweight subjects with metabolic abnormalities (26.7% of the population), screening identified 71.2% of non‐alcoholic fatty liver cases. Conclusions: The two groups most likely to benefit by transient elastography screening are adolescents with obesity and overweight adolescents with one metabolic abnormality. These criteria reduce the number of individuals to be tested by approximately 80% (from an approximate 32 million adolescents to 6–7.5 million adolescents), while retaining a diagnostic accuracy of 84%–85%. [ABSTRACT FROM AUTHOR]

Background: New research has attributed increased significance to the causal link between ultraviolet A (UVA) radiation and immunosuppression and carcinogenesis. In the United States, sunscreens are labeled with only their sun protection factor (SPF) and an imprecise term "broad‐spectrum protection." Sunscreen marketing and efficacy evaluations continue to be based primarily on skin redness (sunburn) or erythema. We sought to evaluate the ultraviolet (UV) protection offered by common sunscreen products on the US market using laboratory‐measured UV‐absorption testing and comparing with computer‐modeled protection and the labeled SPF values. This approach enables an investigation of the relationship between the labeled SPF and measured UVA protection, a factor that is ignored in current regulations. Methods: Fifty‐one sunscreen products for sale in the United States with SPF values from 15 to 110 and labeled as providing broad‐spectrum protection were tested using a commercial laboratory. All products were evaluated using the ISO 24443:2012 method for sunscreen effectiveness. The final absorbance spectra were used for analysis of in vitro UV protection. Results: In vitro SPF values from laboratory‐measured UV absorption and computer modeling were on average just 59 and 42 percent of the labeled SPF. The majority of products provided significantly lower UVA protection with the average unweighted UVA protection factor just 24 percent of the labeled SPF. Conclusion: Regulations and marketplace forces promote sunscreens that reduce sunburn instead of products that provide better, more broad‐spectrum UV protection. The production and use of products with broad spectrum UV protection should be incentivized, removing the emphasis on sunburn protection and ending testing on people. [ABSTRACT FROM AUTHOR]

The joinpoint regression model (JRM) is used to describe trend changes in many applications and relies on the detection of joinpoints (changepoints). However, the existing joinpoint detection methods, namely, the grid search (GS)-based methods, are computationally demanding, and hence, the maximum number of computable joinpoints is limited. Herein, we developed a genetic algorithm-based joinpoint (GAJP) model in which an explicitly decoupled computing procedure for optimization and regression is used to embed a binary genetic algorithm into the JRM for optimal joinpoint detection. The combinations of joinpoints were represented as binary chromosomes, and genetic operations were performed to determine the optimum solution by minimizing the fitness function, the Bayesian information criterion (BIC) and BIC3 . The accuracy and computational performance of the GAJP model were evaluated via intensive simulation studies and compared with those of the GS-based methods using BIC, BIC3 , and permutation test. The proposed method showed an outstanding computational efficiency in detecting multiple joinpoints. Finally, the suitability of the GAJP model for the analysis of cancer incidence trends was demonstrated by applying this model to data on the incidence of colorectal cancer in the United States from 1975 to 2016 from the National Cancer Institute's Surveillance, Epidemiology, and End Results program. Thus, the GAJP model was concluded to be practically feasible to detect multiple joinpoints up to the number of grids without requirement to preassign the number of joinpoints and be easily extendable to cancer trend analysis utilizing large datasets. [ABSTRACT FROM AUTHOR]

Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large‐scale numerical models can be resource intensive. Using a novel automated approach, a set of 115 inexpensive general simulation models (GSMs) was used to create RTD metrics (fraction of young groundwater, defined as <65 years old; mean travel time of young fraction; median travel time of old fraction; and mean path length). GSMs captured the general trends in measured tritium concentrations in 431 wells. Boosted Regression Tree metamodels were trained to predict these RTD metrics using available wall‐to‐wall hydrogeographic digital sets as explanatory features. The metamodels produced a three‐dimensional distribution of predictions throughout the glacial system that generally matched with the numerical model RTD metrics. In addition to the expected importance of aquifer thickness and recharge rate in predicting RTD metrics, two new data sets, Multi‐Order Hydrologic Position (MOHP) and hydrogeologic terrane were important predictors. These variables by themselves produced metamodels with Nash‐Sutcliffe efficiency close to the full metamodel. Metamodel predictions showed that the volume of young groundwater stored in the glaciated United States is about 6,000 km3, or about 0.5% of globally stored young groundwater. Plain Language Summary: Groundwater is an important source of water for people and ecosystems. The quality of groundwater depends partly on how long it has resided underground, but this is difficult to know. There are several methods to determine groundwater residence time, but they mostly do not apply over large regional aquifers. In this study, we used a novel method coupling statistical learning and numerical modeling to calculate groundwater residence time throughout the glaciated areas of the United States. The results reveal that most of the groundwater in the area is less than 65 years old and usually travels distances ranging from less than 1 km to about 30 km before discharging to streams, rivers, and lakes. Key Points: A novel method coupling statistical learning and numerical models is presentedThree‐dimensional continuous digital data sets of groundwater age metrics are presentedTwo novel data sets, Multi‐Order Hydrologic Position and hydrogeologic terrane, contain information about groundwater residence time [ABSTRACT FROM AUTHOR]

We present multilayer observations and numerical simulations of gravity waves (GWs) generated by a series of Mesoscale Convective Systems over the midwestern United States. Strong semiconcentric GWs were observed and modeled, which couple from their tropospheric sources to the thermosphere, displaying strong nonlinearity indicated by instability, breaking, and formation of turbulent vortices. GWs in the stratosphere display a large range of horizontal scales from 34–400 km; however, the smaller wavelength waves break rapidly in the mesosphere and lower thermosphere. Larger‐scale (≥150 km) waves dominate in the thermosphere and display northwestward propagation at 200–300 km altitude, opposing the mean winds. Despite strong molecular viscosity and thermal conductivity in the thermosphere, steepened wave fronts, which may indicate nonlinearity, is identified in 630 nm airglow imagers. The agreement between model and data suggests new opportunities for data‐constrained simulations that span multilayer observables, including mesosphere and lower thermosphere‐region airglow not captured for this event. Key Points: Coherent gravity waves were observed above thunderstorms, in the stratosphere by AIRS and thermosphere by MANGOSimulations of waves generated by convective sources defined by radar data closely match the observations in scaleResults indicate that gravity waves may evolve nonlinearly well into the thermosphere and ionosphere [ABSTRACT FROM AUTHOR]

Biosurveillance for rapid detection of epidemics of diseases is a challenging area of endeavor in many respects. Hence, this area is in need of development of methodology and opens to novel methods of detection. In this study, a new simple statistical early outbreak detection approach is proposed to detect outbreaks of diseases in real time. The new approach is called LWMAT since it is based on linearly weighted moving average. Furthermore, it does not require a long baseline and partly takes into account of likely features of biosurveillance data such as nonstationary and overdispersion to some extent. Moreover, this newly proposed method is easily adapted to automated use in public health surveillance systems to monitor simultaneously large number time series of indicators associated with the relevant diseases. To compare the performance of the new method with those of some well-known outbreak detection methods, semisynthetic data with outbreaks of various magnitudes and durations are simulated by considering the weekly number of outpatient visits for influenza-like illness for the influenza seasons 2014-2015 through 2017-2018 at Centers for Disease Control and Prevention (CDC) in the United States. Under the conditions of the simulation studies, Serfling regression and Farrington flexible seem to be preferable methods for monitoring the weekly influenza data at CDC in terms of early identification of influenza outbreaks with a high probability. In addition, the newly proposed LWMAT-type methods appear to be promising and useful methods in the case of small magnitude outbreaks with a short duration. [ABSTRACT FROM AUTHOR]

Background: Outpatients receive observation services to determine the need for inpatient admission. These services are usually provided without the use of condition‐specific protocols and in an unstructured manner, scattered throughout a hospital in areas typically designated for inpatient care. Emergency department observation units (EDOUs) use protocolized care to offer an efficient alternative with shorter lengths of stay, lower costs, and higher patient satisfaction. EDOU growth is limited by existing policy barriers that prevent a "two‐service" model of separate professional billing for both emergency and observation services. The majority of EDOUs use the "one‐service" model, where a single composite professional fee is billed for both emergency and observation services. The financial implications of these models are not well understood. Methods: We created a Monte Carlo simulation by building a model that reflects current clinical practice in the United States and uses inputs gathered from the most recently available peer‐reviewed literature, national survey, and payer data. Using this simulation, we modeled annual staffing costs and payments for professional services under two common models of care in an EDOU. We also modeled cash flows over a continuous range of daily EDOU patient encounters to illustrate the dynamic relationship between costs and revenue over various staffing levels. Results: We estimate the mean (±SD) annual net cash flow to be a net loss of $315,382 (±$89,635) in the one‐service model and a net profit of $37,569 (±$359,583) in the two‐service model. The two‐service model is financially sustainable at daily billable encounters above 20, while in the one‐service model, costs exceed revenue regardless of encounter count. Physician cost per hour and daily patient encounters had the most significant impact on model estimates. Conclusions: In the one‐service model, EDOU staffing costs exceed payments at all levels of patient encounters, making a hospital subsidy necessary to create a financially sustainable practice. Professional groups seeking to staff and bill for both emergency and observation services are seldom able to do so due to EDOU size limitations and the regulatory hurdles that require setting up a separate professional group for each service. Policymakers and health care leaders should encourage universal adoption of EDOUs by removing restrictions and allowing the two‐service model to be the standard billing option. These findings may inform planning and policy regarding observation services. [ABSTRACT FROM AUTHOR]

Objective: To provide an alternative to propensity scoring (PS) for the common situation where there are interacting covariates.Setting: We used 1.3 million assessments of residents of the United States Veterans Affairs nursing homes, collected from January 1, 2000, through October 9, 2012.Design: In stratified covariate balancing (SCB), data are divided into naturally occurring strata, where each stratum is an observed combination of the covariates. Within each stratum, cases with, and controls without, the target event are counted; controls are weighted to be as frequent as cases. This weighting procedure guarantees that covariates, or combination of covariates, are balanced, meaning they occur at the same rate among cases and controls. Finally, impact of the target event is calculated in the weighted data. We compare the performance of SCB, logistic regression (LR), and propensity scoring (PS) in simulated and real data. We examined the calibration of SCB and PS in predicting 6-month mortality from inability to eat, controlling for age, gender, and nine other disabilities for 296,051 residents in Veterans Affairs nursing homes. We also performed a simulation study, where outcomes were randomly generated from treatment, 10 covariates, and increasing number of covariate interactions. The accuracy of SCB, PS, and LR in recovering the simulated treatment effect was reported.Findings: In simulated environment, as the number of interactions among the covariates increased, SCB and properly specified LR remained accurate but pairwise LR and pairwise PS, the most common applications of these tools, performed poorly. In real data, application of SCB was practical. SCB was better calibrated than linear PS, the most common method of PS.Conclusions: In environments where covariates interact, SCB is practical and more accurate than common methods of applying LR and PS. [ABSTRACT FROM AUTHOR]

Objective: (1) To evaluate the operational efficiency of various sampling methods for patient exit interviews; (2) to discuss under what circumstances each method yields an unbiased sample; and (3) to propose a new, operationally efficient, and unbiased sampling method.Study Design: Literature review, mathematical derivation, and Monte Carlo simulations.Principal Findings: Our simulations show that in patient exit interviews it is most operationally efficient if the interviewer, after completing an interview, selects the next patient exiting the clinical consultation. We demonstrate mathematically that this method yields a biased sample: patients who spend a longer time with the clinician are overrepresented. This bias can be removed by selecting the next patient who enters, rather than exits, the consultation room. We show that this sampling method is operationally more efficient than alternative methods (systematic and simple random sampling) in most primary health care settings.Conclusion: Under the assumption that the order in which patients enter the consultation room is unrelated to the length of time spent with the clinician and the interviewer, selecting the next patient entering the consultation room tends to be the operationally most efficient unbiased sampling method for patient exit interviews. [ABSTRACT FROM AUTHOR]

Abstract: Background and aims: The drinking environment is a complex system consisting of a number of heterogeneous, evolving and interacting components, which exhibit circular causality and emergent properties. These characteristics reduce the efficacy of commonly used research approaches, which typically do not account for the underlying dynamic complexity of alcohol consumption and the interdependent nature of diverse factors influencing misuse over time. We use alcohol misuse among college students in the United States as an example for framing our argument for a complex systems paradigm. Methods: A complex systems paradigm, grounded in socio‐ecological and complex systems theories and computational modeling and simulation, is introduced. Theoretical, conceptual, methodological and analytical underpinnings of this paradigm are described in the context of college drinking prevention research. Results: The proposed complex systems paradigm can transcend limitations of traditional approaches, thereby fostering new directions in alcohol prevention research. By conceptualizing student alcohol misuse as a complex adaptive system, computational modeling and simulation methodologies and analytical techniques can be used. Moreover, use of participatory model‐building approaches to generate simulation models can further increase stakeholder buy‐in, understanding and policymaking. Conclusions: A complex systems paradigm for research into alcohol misuse can provide a holistic understanding of the underlying drinking environment and its long‐term trajectory, which can elucidate high‐leverage preventive interventions. [ABSTRACT FROM AUTHOR]

Abstract: Background and aims: Given the complexity of factors contributing to alcohol misuse, appropriate epistemologies and methodologies are needed to understand and intervene meaningfully. We aimed to (1) provide an overview of computational modeling methodologies, with an emphasis on system dynamics modeling; (2) explain how community‐based system dynamics modeling can forge new directions in alcohol prevention research; and (3) present a primer on how to build alcohol misuse simulation models using system dynamics modeling, with an emphasis on stakeholder involvement, data sources and model validation. Throughout, we use alcohol misuse among college students in the United States as a heuristic example for demonstrating these methodologies. Methods: System dynamics modeling employs a top–down aggregate approach to understanding dynamically complex problems. Its three foundational properties—stocks, flows and feedbacks—capture non‐linearity, time‐delayed effects and other system characteristics. As a methodological choice, system dynamics modeling is amenable to participatory approaches; in particular, community‐based system dynamics modeling has been used to build impactful models for addressing dynamically complex problems. Results: The process of community‐based system dynamics modeling consists of numerous stages: (1) creating model boundary charts, behavior‐over‐time‐graphs and preliminary system dynamics models using group model‐building techniques; (2) model formulation; (3) model calibration; (4) model testing and validation; and (5) model simulation using learning‐laboratory techniques. Conclusions: Community‐based system dynamics modeling can provide powerful tools for policy and intervention decisions that can result ultimately in sustainable changes in research and action in alcohol misuse prevention. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, a monthly water-balance model is used to simulate monthly runoff for 2109 hydrologic units ( HUs) in the conterminous United States ( CONUS) for water-years 1901 through 2014. The monthly runoff time series for each HU were smoothed with a 3-month moving average, and then the 3-month moving-average runoff values were converted to percentiles. For each HU, a drought was considered to occur when the HU runoff percentile dropped to the 20th percentile or lower. A drought was considered to end when the HU runoff percentile exceeded the 20th percentile. After identifying drought events for each HU, the frequency and length of drought events were examined. Results indicated that (1) the longest mean drought lengths occur in the eastern CONUS and parts of the Rocky Mountain region and the northwestern CONUS, (2) the frequency of drought is highest in the southwestern and central CONUS, and lowest in the eastern CONUS, the Rocky Mountain region, and the northwestern CONUS, (3) droughts have occurred during all months of the year and there does not appear to be a seasonal pattern to drought occurrence, (4) the variability of precipitation appears to have been the principal climatic factor determining drought, and (5) for most of the CONUS, drought frequency appears to have decreased during the 1901 through 2014 period. [ABSTRACT FROM AUTHOR]

Recent studies found that infection-related hospitalization was associated with increased risk of cardiovascular (CV) events, such as myocardial infarction and stroke in the dialysis population. In this work, we develop time-varying effects modeling tools in order to examine the CV outcome risk trajectories during the time periods before and after an initial infection-related hospitalization. For this, we propose partly conditional and fully conditional partially linear generalized varying coefficient models (PL-GVCMs) for modeling time-varying effects in longitudinal data with substantial follow-up truncation by death. Unconditional models that implicitly target an immortal population is not a relevant target of inference in applications involving a population with high mortality, like the dialysis population. A partly conditional model characterizes the outcome trajectory for the dynamic cohort of survivors, where each point in the longitudinal trajectory represents a snapshot of the population relationships among subjects who are alive at that time point. In contrast, a fully conditional approach models the time-varying effects of the population stratified by the actual time of death, where the mean response characterizes individual trends in each cohort stratum. We compare and contrast partly and fully conditional PL-GVCMs in our aforementioned application using hospitalization data from the United States Renal Data System. For inference, we develop generalized likelihood ratio tests. Simulation studies examine the efficacy of estimation and inference procedures. [ABSTRACT FROM AUTHOR]

Chronic nonhealing wounds have a prevalence of 2% in the United States, and cost an estimated $50 billion annually. Accurate stratification of wounds for risk of slow healing may help guide treatment and referral decisions. We have applied modern machine learning methods and feature engineering to develop a predictive model for delayed wound healing that uses information collected during routine care in outpatient wound care centers. Patient and wound data was collected at 68 outpatient wound care centers operated by Healogics Inc. in 26 states between 2009 and 2013. The dataset included basic demographic information on 59,953 patients, as well as both quantitative and categorical information on 180,696 wounds. Wounds were split into training and test sets by randomly assigning patients to training and test sets. Wounds were considered delayed with respect to healing time if they took more than 15 weeks to heal after presentation at a wound care center. Eleven percent of wounds in this dataset met this criterion. Prognostic models were developed on training data available in the first week of care to predict delayed healing wounds. A held out subset of the training set was used for model selection, and the final model was evaluated on the test set to evaluate discriminative power and calibration. The model achieved an area under the curve of 0.842 (95% confidence interval 0.834-0.847) for the delayed healing outcome and a Brier reliability score of 0.00018. Early, accurate prediction of delayed healing wounds can improve patient care by allowing clinicians to increase the aggressiveness of intervention in patients most at risk. [ABSTRACT FROM AUTHOR]

By '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. Lastly, we investigate the approach's operating characteristics in a simple illustrative setting through a series of simulations, showing that it can have desirable properties under both null and alternative hypotheses. [ABSTRACT FROM AUTHOR]

The dimensions that define a food product have expanded rapidly to include characteristics of the production process, marketing arrangements, and implications that production and consumption of the product have for the environment. Some market intermediaries have responded by requiring that their suppliers abide by restrictive production practices. We examine the economic effects of such restrictions and apply this analysis to limitations on the use of antibiotics in U.S. pork production. Results from conceptual and simulation analyses show that, in the absence of demand growth, less pork is sold due to higher costs in the restricted segment, and both pork consumers (on average) and producers are harmed. Demand growth of between 6-11% from adding new consumers who will consume the restricted (antibiotic-free) product but not the conventional product is needed to return consumer surplus to the level in the base case, and between 2-4% demand growth was required to return producer surplus to base. When restricted and conventional products are modeled using a vertical differentiation framework, results depend importantly on the ease with which consumers can switch to a seller who offers their desired product type. Significant distributional impacts among consumers are present when switching costs are prohibitive. [ABSTRACT FROM AUTHOR]

The current U.S. Social Security Disability Insurance program is an all-or-nothing system that has been criticized for creating strong work disincentives. In an empirically grounded and calibrated life-cycle model, I simulate behavioral responses to a partial disability benefit system, a policy alternative to the current program, which allows individuals to claim partial disability and combine earnings with disability benefits. Simulation results show financial savings for the program as well as welfare improvements for individuals with disabilities. [ABSTRACT FROM AUTHOR]