Search

The introduction of an electronic medical record (EMR) has been rapidly accelerating in South Korea. The EMR was expected to improve quality of care, readability, availability, and the quality of data. However, the reluctance of healthcare providers to use the EMR may have caused a reduction of information recorded in EMRs. The purpose of this study was to identify whether there was any loss of information following the introduction of a narrative text-based EMR in the recording of chief complaint and present illness in inpatient medical records. Inpatient medical records of a university hospital were retrospectively evaluated for one month before and one month after the introduction of the EMR in June 2006. The volume of information for chief complaint and present illness was measured by number of words in Korean and normalised bytes. Change in volume of information was measured by two-way ANOVA and multiple regression analyses, controlling for doctors' gender, age, and grade/year of residents, patients' readmission status, reasons for admission and service department to assess any effect of the introduction of an EMR. Total numbers of paper-based medical records (PMRs) and EMRs for analysis were 1,159 and 1,122, respectively. Forty-three doctors participated in the study. Thirty-one (72%) doctors were less than 30 years of age. Number of words proved a better outcome measure (R2=.22 for CC, R2=.36 for PI) than normalised bytes (R2=.18 for CC, R2=.35 for PI) for measuring volume of information. Results showed that the volume of information in the chief complaint and present illness was not decreased after the introduction of the EMR, except when the dependent variable was measured by number of words in the present illness. The study showed that the introduction of the EMR did not reduce the volume of information documented for chief complaint and present illness in inpatient medical records. However, further studies are needed to identify how to control the probable loss of information as showed in present illness measured by number of words. [ABSTRACT FROM AUTHOR]

Aim: The aim of this article was to develop and apply an Internet-based headache diary (i-diary) for adolescents and compare it with a paper-diary (p-diary) regarding adherence, user acceptability and recorded headache activity.Methods: In a cross-sectional school-based study, a representative sample of 488 adolescents aged 12-18 years were randomly allocated by cluster sampling to record for three weeks in i-diaries or p-diaries their headache intensity, disability, and use of acute medication.Results: A significantly (p = 0.008) higher proportion of adolescents in the i-diary group used the diary at least once during the 21-day period (86% vs 76% for the p-diary). However, the p-diary group completed a significantly (p < 0.001) higher number of diary days (20.8 vs 15.0 days for the i-diary). The response rate for the i-diary-group was largely evenly distributed over the study period; conversely, approximately two-thirds of the adolescents using the p-diary responded on all 21 days, whereas one-fourth did not respond at all. The two diary types were rated as equal in easiness to remember (p = 0.25), but the i-diaries were more bothersome to use (p = 0.029).Conclusion: Although p-diary users completed a higher proportion of diary days, i-diaries provided more reliable and credible estimates of headache parameters because of better real-time assessment. [ABSTRACT FROM AUTHOR]

The study was conducted to assess whether the introduction of an electronic patient records-based system affected hepatitis B vaccination completion rates and post-vaccination return rates, when compared to a paper-based system. Data were gathered for three groups of patients: those commencing vaccination (a) when paper records were in use (paper records group), (b) after electronic patient records were introduced (basic electronic patient records group) and (c) after electronic patient records were enhanced with recall (enhanced electronic patient records group). Compared to the paper records group, the third dose completion rates for patients managed using electronic patient records did not differ significantly: 74/119 (62.2%) paper vs. 58/98 (59.2%) basic electronic patient records, p = 0.652 and 89/130 (68.5%) enhanced electronic patient records, p = 0.298. On sub-group analysis, completion rates in patients of black ethnicity in the enhanced electronic patient records group were significantly higher than those in the paper records group: 16/19 (84.2%) enhanced electronic patient records vs. 11/23 (47.8%) paper, p = 0.014. Patients in the enhanced electronic patient records group were more likely than those in the paper records group to attend for measurement of hepatitis B surface antibody levels: 61/130 (46.9%) vs. 39/119 (32.8%), p = 0.023. [ABSTRACT FROM AUTHOR]

Background Until now, no valid alternative exists for predicting central venous pressure (CVP) with lower invasiveness than central venous catheter. Purpose To explore inferior vena cava diameter (IVCD) measurement accuracy by ultrasonography as a surrogate variable for determination of central venous pressure (CVP). Material and Methods A systematic review and meta-analysis of all published studies in PubMed, Scopus, Web of Knowledge, and Google Scholar were conducted from inception to July 2013. We used the STROBE checklist for quality assessment and meta-regression. Results Thirty-seven papers with 2843 cases were identified. The correlation coefficients between each one of IVCD, inspiratory IVC (iIVC), IVC collapsibility index (IVCCI), and expiratory IVC (eIVC) with CVP, were 0.68, 0.60, 0.54, and 0.44, respectively. There was no evidence of publication bias ( P = 0.28). Based on meta-regression, male gender was an important source of heterogeneity (OR = 1.01; 95% confidence interval, 1-1.03), which resulted in a higher correlation between IVCD and CVP. The present study showed a higher strength of association with CVP pertaining to IVCD, iIVC, IVCCI, and eIVC, respectively, and they were higher in men. Conclusion This study does not support the measurement of IVCD by ultrasonography as an acceptable surrogate variable to determine CVP among critical patients. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Psychiatry is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Little evidence is available about nurses’ use of electronic tools during handoffs. This qualitative study explored information management and use of electronic tools during nursing handoffs. The sample included 93 handoffs by 26 nurses on 5 medical/surgical units in 2 western hospitals with a robust electronic health record (EHR). Data collection included audiotaping handoffs, semi-structured interviews, observations, and fieldnotes. The dataset was inductively coded into 33 categories and 5 themes: good nurse expectations for handoffs, paper forms are best, information at a glance, only pertinent information please, and information tools that work. Two-thirds of the nurses abandoned use of the leadership-endorsed electronic handoff form, preferring personal paper forms. The findings suggest effective electronic solutions will require extensive contextually-based information, information integrated across EHR modules and portable, electronic support throughout shifts. This is a call to action for leaders and informaticists as they select and design future electronic tools. [ABSTRACT FROM PUBLISHER]

This paper analyzes a UK-based psychoeducational intervention for users of online child sexual exploitation material (CSEM). It is founded on 17 months of anthropological research in group programs with 81 participants and 15 staff. The article argues that group exercises help participants reframe knowledge about their offending, and ultimately reinforce the theoretical concept of discipline (Foucault) toward internal and external surveillance, normalization, and decreased risk. The paper first discusses factors participants believed contributed to offending. It then analyzes the program and participants' experiences, focusing on exercises about the mind (fantasy), Internet usage (disclosure and relationships), needs met by offending (Good Lives and true needs), and planning for the future (relapse prevention). Critical is that participants are encouraged to reengage offline lives and enact discipline on and to the online world. Thus, the article ends with an anthropologically-minded discussion about digital norms, online morality, and implications for Internet offender psychoeducational practice. [ABSTRACT FROM AUTHOR]

Starting in 2002, the UK Labour Government of 1997–2010 introduced a series of changes to the National Health Service (NHS) in England designed to increase patients’ choices of the place of elective hospital care and encourage competition among public and private providers of elective hospital services for NHS-funded patients. In 2006, the Department of Health initiated the Health Reform Evaluation Programme (HREP) to assess the impact of the changes. In June 2008, the White Paper, High quality care for all, was published. It represented the government’s desire to focus the next phase of health care system reform in England as much on the quality of care as on improving its responsiveness and efficiency.The 2008 White Paper led to the commissioning of a further wave of evaluative research under the auspices of HREP, as follows:an evaluation of the implementation and outcomes of care planning for people with long-term conditions;an evaluation of the personal health budget pilots;an evaluation of the implementation and outcomes of the Commissioning for Quality and Innovation (CQUIN) framework; andan evaluation of cultural and behavioural change in the NHS focused on ensuring high quality care for all.This Supplement includes papers from each project.The evaluations present a mixed picture of the impact and success of the 2008 reforms. All the studies identify some limitations of the policies in the White Paper. The introduction of personal health budgets appears to have been the least problematic and, depending on assumptions, likely to be cost-effective for the sorts of patients involved in the pilot. For the rest of the changes, impacts ranged from little or none (CQUIN and care planning for people with chronic conditions) to patchy and highly variable (instilling a culture of quality in acute hospitals) in the three years following the publication of the White Paper. On the other hand, each of the studies identifies important insights relevant to modifying and improving the policies. These findings have continuing relevance since both the 2008 White Paper’s policies, and the issues they were focused on remedying, remain central to the current Coalition Government’s reform agenda. [ABSTRACT FROM PUBLISHER]

Hospital-based violence intervention programs (HVIPs) are multidisciplinary programs that use a health/public health approach to violence intervention by intervening with victims of violence at the time of injury and leveraging hospital- and community-based resources to address the underlying risk factors for violence. Much of the evidence for the impacts of HVIPs comes from qualitative research, yet there are few reviews of the efficacy of these approaches, and none that focus specifically on the HVIP context. This paper fills this gap by reviewing the common qualitative elements of published HVIP evaluations, and discusses their strengths, challenges, and relative applicability for researchers and practitioners alike. It then sets forth a research agenda, making a case for further (and more varied) qualitative HVIP evaluation research. [ABSTRACT FROM AUTHOR]

Objective: In this review, we determine if there is evidence to demonstrate a relationship between occupational driving posture and low back pain.Background: The burden of low back pain is increasing. An understanding of this relationship is required to enable the development of recommendations for clinicians and policy-makers for the driving industry.Method: Five databases were searched up to March 12, 2018. Study quality was assessed using the National Heart, Lung, and Blood Institute's Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies, followed by a GRADE analysis to consider the evidence as a whole. A narrative, critical synthesis was completed that considered the methods by which driving posture and low back pain were measured and analyzed.Results: There were 653 articles identified, with seven eligible for review. Four articles identified an association between occupational driving posture and low back pain, yet this was based on the use of measurement tools lacking validity. Although a relationship may exist, the specific driving postures associated with low back pain and the strength of this relationship have not been confirmed.Conclusion: Future research needs to employ validated and reliable, real-time qualitative methods for measuring occupational driving posture to advance our understanding of this relationship.Application: Clinical and policy recommendations regarding driving posture and low back pain should be used with caution, as they are guided by evidence incorporating bias. Future studies are required to confirm the specific postures assumed while occupational driving and their relationship with low back pain, before recommendations can be made. [ABSTRACT FROM AUTHOR]

Newell and Card (1985) warned the human factors community that the way to deal with scientists, engineers, and designers was not through the use of platitudes or by advocating the empirical testing of an infinity of design alternatives but, rather, through the use of predictive and reliable quantitative techniques. As the scope and scale of the issues that the human factors community was asked to consider expanded, the tool chest of quantitative methods seemed to diminish. That situation appears to be changing. As the papers in this special section show, the science base and techniques available for applying that science through use of quantitative formalisms have progressed. The pendulum is swinging back, and human factors engineers are in the ascendance. Engineering quantitative formal models of human performance is the wave of the present and represents an important part of the future of our profession. [ABSTRACT FROM AUTHOR]

Propensity score methods are commonly used in statistical analyses of observational data to reduce the impact of confounding bias in estimations of average treatment effect. While the propensity score is defined as the conditional probability of a subject being in the treatment group given that subject's covariates, the most precise estimation of average treatment effect results from specifying the propensity score as a function of true confounders and predictors only. This property has been demonstrated via simulation in multiple prior research articles. However, we have seen no theoretical explanation as to why this should be so. This paper provides that theoretical proof. Furthermore, this paper presents a method for performing the necessary variable selection by means of elastic net regression, and then estimating the propensity scores so as to obtain optimal estimates of average treatment effect. The proposed method is compared against two other recently introduced methods, outcome-adaptive lasso and covariate balancing propensity score. Extensive simulation analyses are employed to determine the circumstances under which each method appears most effective. We applied the proposed methods to examine the effect of pre-cardiac surgery coagulation indicator on mortality based on a linked dataset from a retrospective review of 1390 patient medical records at Jewish Hospital (Louisville, KY) with the Society of Thoracic Surgeons database. [ABSTRACT FROM AUTHOR]

Estimating the medical costs from disease diagnosis to a terminal event is of immense interest to researchers. However, most of existing literature on such research focused on the estimation of cumulative mean function (CMF) for history process. In this paper, the combined scheme of both inverse probability of censoring weighting (IPCW) technique and longitudinal quantile regression model is used to develop a novel procedure to the estimation of cumulative quantile function (CQF) based on history process with time-dependent covariates and right censored time-to-event variable. The consistency of proposed estimator is derived. The extensive simulation study is conducted to investigate the performance of the estimator given in this paper. A medical cost data from a multicenter automatic defibrillator implantation trial (MADIT) is analyzed to illustrate the application of developed method. [ABSTRACT FROM AUTHOR]

Late-onset toxicities often occur in phase I trials investigating novel immunotherapy and molecular targeted therapies. For trials with cohort based designs (such as modified toxicity probability interval, Bayesian optimal interval, and i3+3), patients are often turned away since the current cohort are still being followed without definite dose-limiting toxicities, which results in prolonged trial duration and waste of patient resources. In this paper, we incorporate a probability-of-decision framework into the i3+3 design and allow real-time dosing inference when the next patient becomes available. Both follow-up time for the pending patients and time to dose-limiting toxicities for the observed patients are used in calculating the posterior probability of each possible dosing decision. An intensive simulation study is conducted to evaluate the operating characteristics of the newly proposed probability-of-decision-i3+3 design under various dosing scenarios and patient accrual settings. Results show that the probability-of-decision-i3+3 design achieves comparable safety and reliability performances but much shorter trial duration compared to the complete designs. [ABSTRACT FROM AUTHOR]

Causal mediation analysis seeks to decompose the effect of a treatment or exposure among multiple possible paths and provide casually interpretable path-specific effect estimates. Recent advances have extended causal mediation analysis to situations with a sequence of mediators or multiple contemporaneous mediators. However, available methods still have limitations, and computational and other challenges remain. The present paper provides an extended causal mediation and path analysis methodology. The new method, implemented in the new R package, gmediation (described in a companion paper), accommodates both a sequence (two stages) of mediators and multiple mediators at each stage, and allows for multiple types of outcomes following generalized linear models. The methodology can also handle unsaturated models and clustered data. Addressing other practical issues, we provide new guidelines for the choice of a decomposition, and for the choice of a reference group multiplier for the reduction of Monte Carlo error in mediation formula computations. The new method is applied to data from a cohort study to illuminate the contribution of alternative biological and behavioral paths in the effect of socioeconomic status on dental caries in adolescence. [ABSTRACT FROM AUTHOR]

Objective: To systematically review the literature with respect to treatment-seeking rates for depression and associated mediating factors. This review focuses on adolescents and adults of all ages.Methods: A structured literature review using Medline and PsychInfo databases revealed 38 relevant papers. Two trained reviewers independently and blindly assessed each study according to 4 inclusion criteria. A total of 17 papers met all 4 criteria.Results: Between 17.0% and 77.8% of individuals with depressive episodes or disorders sought treatment in these studies. We could explain the range in rates by diverse measures of depression, mediating factors that influence treatment-seeking, varied years in which the studies were done, and different time periods over which treatment-seeking was assessed.Conclusions: Treatment-seeking rates for major depression appear to have increased over the years. Age, race, social supports, and clinical and psychiatric factors seem to influence treatment-seeking rates most. Public health initiatives can use this information to facilitate service access and delivery. [ABSTRACT FROM AUTHOR]

Dried blood spots (DBS) on filter paper have been used as a practical method of sample collection in sero-surveillance studies of numerous diseases. DBS may be particularly useful for HIV screening in remote areas, in which unrefrigerated transfer time to a laboratory may take a number of days. In this study, we evaluate the ability to detect human immunodeficiency virus (HIV) type-1 antibodies from DBS that have been subjected to a tropical climate in southern India for 6 days. DBS were prepared from blood samples of 59 known HIV-positive individuals and 30 known HIV-negative individuals. ELISA and Western blot results from DBS that were subjected to a mean temperature of 35.8 degrees C and humidity of 73% for 6 days had a sensitivity of 100% and 92%, respectively, and a specificity of 100% and 100%, respectively. Based on these findings, we conclude that DBS sampling could serve as a cost-effective and convenient tool for widespread HIV sero-surveillance in remote areas within tropical countries. [ABSTRACT FROM AUTHOR]

Objective: An incorrect false positive diagnosis of melancholia can lead to inappropriate treatment and illness prolongation. This paper therefore seeks to introduce the concept of 'pseudo-melancholia' to capture such instances and provide clinical examples of contributing at-risk scenarios.Methods: The author draws on clinical experience to provide exemplars of circumstances most risking a false positive diagnosis of melancholia.Results: Pseudo-melancholia can result from invalid measures of melancholia and from several functional and organic conditions presenting with suggested melancholic features.Conclusions: Recognising high-risk pseudo-melancholia scenarios has the potential to advance a change in diagnostic formulation, provide a more diagnosis-specific intervention and so avert a secondary diagnosis of 'treatment resistant depression'. [ABSTRACT FROM AUTHOR]

Bland and Altman described approximate methods in 1986 and 1999 for calculating confidence limits for their 95% limits of agreement, approximations which assume large subject numbers. In this paper, these approximations are compared with exact confidence intervals calculated using two-sided tolerance intervals for a normal distribution. The approximations are compared in terms of the tolerance factors themselves but also in terms of the exact confidence limits and the exact limits of agreement coverage corresponding to the approximate confidence interval methods. Using similar methods the 50th percentile of the tolerance interval are compared with the k values of 1.96 and 2, which Bland and Altman used to define limits of agreements (i.e. [Formula: see text]+/- 1.96Sd and [Formula: see text]+/- 2Sd). For limits of agreement outer confidence intervals, Bland and Altman's approximations are too permissive for sample sizes <40 (1999 approximation) and <76 (1986 approximation). For inner confidence limits the approximations are poorer, being permissive for sample sizes of <490 (1986 approximation) and all practical sample sizes (1999 approximation). Exact confidence intervals for 95% limits of agreements, based on two-sided tolerance factors, can be calculated easily based on tables and should be used in preference to the approximate methods, especially for small sample sizes. [ABSTRACT FROM AUTHOR]

Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study. [ABSTRACT FROM AUTHOR]

The quantile regression model has increasingly become a useful approach for analyzing survival data due to its easy interpretation and flexibility in exploring the dynamic relationship between a time-to-event outcome and the covariates. In this paper, we consider the quantile regression model for survival data with missing censoring indicators. Based on the augmented inverse probability weighting technique, two weighted estimating equations are developed and corresponding easily implemented algorithms are suggested to solve the estimating equations. Asymptotic properties of the resultant estimators and the resampling-based inference procedures are established. Finally, the finite sample performances of the proposed approaches are investigated in simulation studies and a real data application. [ABSTRACT FROM AUTHOR]

Bivariate responses with mixed continuous and binary variables arise commonly in applications such as clinical trials and genetic studies. Statistical methods based on jointly modeling continuous and binary variables have been available. However, such methods ignore the effects of response mismeasurement, a ubiquitous feature in applications. It has been well studied that in many settings, ignorance of mismeasurement in variables usually results in biased estimation. In this paper, we consider the setting with a bivariate outcome vector which contains a continuous component and a binary component both subject to mismeasurement. We propose estimating equation approaches to handle measurement error in the continuous response and misclassification in the binary response simultaneously. The proposed estimators are consistent and robust to certain model misspecification, provided regularity conditions. Extensive simulation studies confirm that the proposed methods successfully correct the biases resulting from the error-in-variables under various settings. The proposed methods are applied to analyze the outbred Carworth Farms White mice data arising from a genome-wide association study. [ABSTRACT FROM AUTHOR]

When developing prediction models for application in clinical practice, health practitioners usually categorise clinical variables that are continuous in nature. Although categorisation is not regarded as advisable from a statistical point of view, due to loss of information and power, it is a common practice in medical research. Consequently, providing researchers with a useful and valid categorisation method could be a relevant issue when developing prediction models. Without recommending categorisation of continuous predictors, our aim is to propose a valid way to do it whenever it is considered necessary by clinical researchers. This paper focuses on categorising a continuous predictor within a logistic regression model, in such a way that the best discriminative ability is obtained in terms of the highest area under the receiver operating characteristic curve (AUC). The proposed methodology is validated when the optimal cut points' location is known in theory or in practice. In addition, the proposed method is applied to a real data-set of patients with an exacerbation of chronic obstructive pulmonary disease, in the context of the IRYSS-COPD study where a clinical prediction rule for severe evolution was being developed. The clinical variable PCO2 was categorised in a univariable and a multivariable setting. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this paper is to describe the development and technical specifications of a framework and national key performance indicators (KPIs) for Australian mental health Court Liaison Services (CLSs) by the National Mental Health Court Liaison Performance Working Group (Working Group).Methods: Representatives from each Australian State and Territory were invited to form a Working Group. Through a series of national workshops and meetings, a framework and set of performance indicators were developed using a review of literature and expert opinion.Results: A total of six KPIs for CLSs have been identified and a set of technical specifications have been formed.Conclusions: This paper describes the process and outcomes of a national collaboration to develop a framework and KPIs. The measures have been developed to support future benchmarking activities and to assist services to identify best practice in this area of mental health service delivery. [ABSTRACT FROM AUTHOR]

Planning a research strategy and formulating the right research questions at various stages of developing a telehealth intervention are essential for producing scientific evidence. The aim of research at each stage should correspond to the maturity of the intervention and will require a variety of study designs. Although there are several published evaluation frameworks for telemedicine or telehealth as a subset of broader eHealth domain, there is currently no simple model to guide research planning. In this paper we propose a five-stage model as a framework for planning a comprehensive telehealth research program for a new intervention or service system. The stages are: (1) Concept development, (2) Service design, (3) Pre-implementation, (4) Implementation, (5) Post-implementation, and at each stage a number of studies are considered. Robust evaluation is important for the widespread acceptance and implementation of telehealth. We hope this framework enables researchers, service administrators and clinicians to conceptualise, undertake and appraise telehealth research from the point of view of being able to assess how applicable and valid the research is for their particular circumstances. [ABSTRACT FROM AUTHOR]

Objective: To examine whether males and females with ADHD differ in their preferences for delayed rewards, since there is some evidence that suggests a sex difference with typically developing (TD) samples. Method: We used meta-analyses to examine sex differences on delay of gratification and temporal discounting tasks in both TD and ADHD samples. We identified 28 papers with 52 effect sizes for children and adults, and calculated the average effect size for sex comparisons within TD and ADHD samples. Results: The estimated mean difference between TD males and TD females was negligible, but males with ADHD were more likely to choose the larger delayed rewards than females with ADHD. Meta-regressions indicated that task type, age, and reward type did not significantly predict sex differences. Conclusion: These findings suggest that females referred for ADHD may make less adaptive choices by preferring smaller immediate rewards over larger delayed rewards more often than males with ADHD. Implications of our findings are discussed. [ABSTRACT FROM AUTHOR]

Objective: The aim of this paper is to provide a comprehensive and original review of the theoretical development of the individual operational cognitive readiness (OCR) theory.Background: Cognitive readiness (CR) is a concept that has the potential to predict the performance of human individuals and teams prior to engaging in complex, dynamic, and resource-limited task environments. However, the current state of the literature is confusing and laborious, with heterogeneous views regarding the theoretical frameworks among leading researchers.Method: This review (1) undertakes a systematic approach toward categorizing published CR literature into theoretical and measurement contributions across the different levels of CR, (2) carries a critical evaluation of the CR and OCR theoretical frameworks, and (3) provides directions for future research guided by gaps identified during the review process and other published literatures.Results: Results from the categorization of published CR literature provide a new, valuable, synthesized CR library for researchers to consult to streamline their CR literature review process. Critical examination of the CR and OCR theoretical frameworks leads to positing that new components should be explored for OCR.Conclusion: There are many possible directions for future research including evaluating domain-independent components of OCR and evaluating the relationship between biofeedback measures and performance in CR models.Application: The Defense domain continues to be the focal application of CR. However, CR could be used by other application domains, such as sports and emergency services, that require their working personnel to engage in complex, uncertain, and dynamic task environments. [ABSTRACT FROM AUTHOR]

Objective: This paper examines drivers' allocation of attention using response time to a tactile detection response task (TDRT) while interacting with an in-vehicle information system (IVIS) over time.Background: Longer TDRT response time is associated with higher cognitive workload. However, it is not clear what role is assumed by the human and system in response to varying in-vehicle environments over time.Method: A driving simulator study with 24 participants was conducted with a restaurant selection task of two difficulty levels (easy and hard) presented in three modalities (audio only, visual only, hybrid). A linear mixed-effects model was applied to identify factors that affect TDRT response time. A nonparametric time-series model was also used to explore the visual attention allocation under the hybrid mode over time.Results: The visual-only mode significantly increased participants' response time compared with the audio-only mode. Females took longer to respond to the TDRT when engaged with an IVIS. The study showed that participants tend to use the visual component more toward the end of the easy tasks, whereas the visual mode was used more at the beginning of the harder tasks.Conclusion: The visual-only mode of the IVIS increased drivers' cognitive workload when compared with the auditory-only mode. Drivers showed different visual attention allocation during the easy and hard restaurant selection tasks in the hybrid mode.Application: The findings can help guide the design of automotive user interfaces and help manage cognitive workload. [ABSTRACT FROM AUTHOR]

Introduction: Pain, depression, and anxiety are prominent symptoms that frequently co-occur, causing significant debilitation and frequent primary care visits. This paper examines the acceptability of telecare and self-management modules in managing these conditions in a randomized trial.Methods: The Comprehensive Management of Mood and Physical Symptoms (CAMMPS) trial compared an automated symptom management (ASM) plus self-management intervention with a comprehensive symptom management (CSM) intervention that added telecare facilitation of enhanced services. Data from the CAMMPS trial were analysed to compare the acceptability of these two interventions as indicated by utilization and patient satisfaction surveys.Results: The mean number of automated reports completed was similar between the CSM and ASM groups (14.5 vs 14.0). Responses designated with clinically relevant "red alerts" (i.e. patient reports warranting an expedited nurse contact) were more frequent in the CSM group (10.2 vs 8.3). The CSM and ASM groups completed a similar number of the nine self-management modules (6.3 vs 5.8). The mean helpfulness score across all modules was higher in the CSM group than in the ASM group (1.8 vs 1.5; p = .003). The most common feedback suggestion from the ASM group was to have more personal interaction, while participants from both groups commonly suggested technical improvements or requests for more flexible timing of calls.Discussion: Participants generally found both interventions satisfactory, with a trend in satisfaction data suggesting that patients tended to find the CSM intervention more helpful. [ABSTRACT FROM AUTHOR]

Clinical trials often evaluate multiple outcome variables to form a comprehensive picture of the effects of a new treatment. The resulting multidimensional insight contributes to clinically relevant and efficient decision-making about treatment superiority. Common statistical procedures to make these superiority decisions with multiple outcomes have two important shortcomings, however: (1) Outcome variables are often modeled individually, and consequently fail to consider the relation between outcomes; and (2) superiority is often defined as a relevant difference on a single, on any, or on all outcome(s); and lacks a compensatory mechanism that allows large positive effects on one or multiple outcome(s) to outweigh small negative effects on other outcomes. To address these shortcomings, this paper proposes (1) a Bayesian model for the analysis of correlated binary outcomes based on the multivariate Bernoulli distribution; and (2) a flexible decision criterion with a compensatory mechanism that captures the relative importance of the outcomes. A simulation study demonstrates that efficient and unbiased decisions can be made while Type I error rates are properly controlled. The performance of the framework is illustrated for (1) fixed, group sequential, and adaptive designs; and (2) non-informative and informative prior distributions. [ABSTRACT FROM AUTHOR]

This paper presents a new model-based generalized functional clustering method for discrete longitudinal data, such as multivariate binomial and Poisson distributed data. For this purpose, we propose a multivariate functional principal component analysis (MFPCA)-based clustering procedure for a latent multivariate Gaussian process instead of the original functional data directly. The main contribution of this study is two-fold: modeling of discrete longitudinal data with the latent multivariate Gaussian process and developing of a clustering algorithm based on MFPCA coupled with the latent multivariate Gaussian process. Numerical experiments, including real data analysis and a simulation study, demonstrate the promising empirical properties of the proposed approach. [ABSTRACT FROM AUTHOR]

There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and evaluate the use of Bayesian additive regression trees in such settings. First, we compare Bayesian additive regression trees to several approaches that have been proposed for continuous outcomes, including inverse probability of treatment weighting, targeted maximum likelihood estimator, vector matching, and regression adjustment. Results suggest that under conditions of non-linearity and non-additivity of both the treatment assignment and outcome generating mechanisms, Bayesian additive regression trees, targeted maximum likelihood estimator, and inverse probability of treatment weighting using generalized boosted models provide better bias reduction and smaller root mean squared error. Bayesian additive regression trees and targeted maximum likelihood estimator provide more consistent 95% confidence interval coverage and better large-sample convergence property. Second, we supply Bayesian additive regression trees with a strategy to identify a common support region for retaining inferential units and for avoiding extrapolating over areas of the covariate space where common support does not exist. Bayesian additive regression trees retain more inferential units than the generalized propensity score-based strategy, and shows lower bias, compared to targeted maximum likelihood estimator or generalized boosted model, in a variety of scenarios differing by the degree of covariate overlap. A case study examining the effects of three surgical approaches for non-small cell lung cancer demonstrates the methods. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to describe and test novel computational driver models, predicting drivers' brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC).Background: Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving.Method: Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers' arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study.Results: The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study.Conclusion: Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data.Application: Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving. [ABSTRACT FROM AUTHOR]

In clustering problems, to model the intrinsic structure of unlabeled data, the latent variable models are frequently used. These model-based clustering methods often provide a clustering rule minimizing the total false assignment error. However, in many clustering applications, it is desirable to treat false assignment errors for a certain cluster differently. In this paper, we introduce the false assignment rate for clustering and estimate it by using the extended likelihood approach. We propose VRclust, a novel clustering rule that controls various errors differently across clusters. Real data examples illustrate the usage of estimation of false assignment rate and a simulation study shows that error controls are consistent as the sample size increases. [ABSTRACT FROM AUTHOR]

The recent upsurge of syphilis infections among men who have sex with men (MSM) is one of the major challenges facing China. However, the overall burden is still not clear. This study aims to summarize the incidence of syphilis among MSM in China by using meta-analysis. We comprehensively searched PubMed-MEDLINE, China National Knowledge Infrastructure and Chinese Wanfang databases. Articles published between December 2009 and March 2015 that met the inclusion criteria were considerably involved in this meta-analysis. Two reviewers performed a quality assessment of the studies and extracted data for estimating the overall syphilis incidence. STATA 12.0 was used to summarize the overall incidence of syphilis. In all, 14 studies from 13 papers were included in this study. Follow-up duration of these studies ranged from six to 36 months, while drop-out rates ranged from 11.9% to 83.6%. The individual incidence rates of the included studies varied from 3.1/100 person-years (95% CI, 0.8-5.3/100 person-years) to 38.5/100 person-years (95% CI, 28.9-48.1/100 person-years), with a pooled incidence of 9.6/100 person-years (95% CI, 7.0-12.2/100 person-years). The subgroup meta-analysis revealed that incidence estimates were 38.5/100 person-years (95% CI, 28.9-48.1/100 person-years), 12.1/100 person-years (95% CI, 7.0-17.2/100 person-years), 11.2/100 person-years (95% CI, 0.7-23.1/100 person-years), 8.9/100 person-years (95% CI, 6.5-11.2/100 person-years), 5.7/100 person-years (95% CI, 3.4-8.0/100 person-years) and 3.1/100 person-years (95% CI, 0.8-5.3/100 person-years) in Northeast, North, Southwest, East, South and Northwest China, respectively. Syphilis incidence among Chinese MSM is high, and this may increase the spread of other sexually transmitted infections, including human immunodeficiency virus. It is essential to integrate syphilis control programs with HIV control programs. This can be achieved by establishing public health response systems to monitor and control the epidemic of syphilis and HIV together in China. [ABSTRACT FROM AUTHOR]

Controversy over the validity of significance tests in the analysis of contingency tables is motivated by the disagreement between asymptotic and exact p values and its dependence on the magnitude of expected frequencies. Variants of Pearson's X2 statistic and their asymptotic distributions were proposed to overcome the difficulties, but several approaches also exist to conduct exact tests. This paper shows that discrepant asymptotic and exact results may or may not occur whether expected frequencies are large or small: Eventual inaccuracy of asymptotic p values is instead caused by idiosyncrasies of the discrete distribution of X2. More importantly, discrepancies are also artificially created by the hypergeometric sampling model used to perform exact tests. Exact computations under the alternative full-multinomial or product-multinomial models require eliminating nuisance parameters and we propose a novel method that integrates them out. The resultant exact distributions are very accurately approximated by the asymptotic distribution, which eliminates concerns about the accuracy of the latter. We also discuss that the two-stage approach that tests for significance of residuals conditional on a significant X2 test is inadvisable and that an alternative single-stage test preserves Type-I error rates and further eliminates concerns about asymptotic accuracy. [ABSTRACT FROM AUTHOR]

Constructing causal inference methods to handle longitudinal data in observational studies is of high interest. In an observational setting, treatment assignment at each clinical visit follows a decision strategy where the treating clinician selects treatment based on current and past clinical measurements as well as treatment histories. These time-dependent structures, coupled with inherent correlations between and within each visit, add on to the data complexity. Despite recent interest in Bayesian causal methods, only a limited literature has explored approaches to handle longitudinal data and no method handles repeatedly measured outcomes. In this paper, we extended two Bayesian approaches: Bayesian estimation of marginal structural models and two-stage Bayesian propensity score analysis to handle a repeatedly measured outcome. Our proposed methods permit causal estimation of treatment effects at each visit. Time-dependent inverse probability of treatment weights are obtained from the Markov chain Monte Carlo samples of the posterior treatment assignment model for each follow-up visit. We use a simulation study to validate and compare the proposed methods and illustrate our approaches through a study of intravenous immunoglobulin therapy in treating newly diagnosed juvenile dermatomyositis. [ABSTRACT FROM AUTHOR]

When establishing an effective treatment with binary data in a two-stage design, one-sided tests for a proportion p are employed. Researchers use the parameter configuration at the boundary of the null hypothesis space to determine a rejection region and an optimal design. However, it is unclear whether the (maximum) Type I error rate is achieved at the boundary especially when the sample size in stage 2 varies. In this paper, we first prove that this is true for a large family of tests in adaptive two-stage designs by showing that any test in the family has a nondecreasing power in p and then derive optimal designs. Second, similar results are established for m-stage designs with m > 2. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

For the analysis of competing risks data, three different types of hazard functions have been considered in the literature, namely the cause-specific hazard, the sub-distribution hazard, and the marginal hazard function. Accordingly, medical researchers can fit three different types of the Cox model to estimate the effect of covariates on each of the hazard function. While the relationship between the cause-specific hazard and the sub-distribution hazard has been extensively studied, the relationship to the marginal hazard function has not yet been analyzed due to the difficulties related to non-identifiability. In this paper, we adopt an assumed copula model to deal with the model identifiability issue, making it possible to establish a relationship between the sub-distribution hazard and the marginal hazard function. We then compare the two methods of fitting the Cox model to competing risks data. We also extend our comparative analysis to clustered competing risks data that are frequently used in medical studies. To facilitate the numerical comparison, we implement the computing algorithm for marginal Cox regression with clustered competing risks data in the R joint.Cox package and check its performance via simulations. For illustration, we analyze two survival datasets from lung cancer and bladder cancer patients. [ABSTRACT FROM AUTHOR]

QTL mapping is an important tool for identifying regions in chromosomes which are relevant to explain a response of interest. It is a special case of the regression model where an unknown number of missing (non-observable) covariates is involved leading to a complex variable selection procedure. Although several methods have been proposed to identify QTLs and to estimate parameters in the associated model, minimum attention has been devoted to the estimated model adequacy. In this paper, we present an overview of a few methods for residual and diagnostic analysis in the context of Bayesian regression modeling and adapt them to work with QTL mapping. The motivation of this study is to identify QTLs associated with the blood pressure of F2 rats and check the fitted model adequacy. [ABSTRACT FROM AUTHOR]

Objective: The majority of people with gambling problems contact helplines when they are in crisis, hampering their capacity to explore suitable treatment options. To date, there has been limited research identifying the best way to support individuals to reduce distress and maximise further treatment-seeking. In this paper, we describe the development and piloting of the resulting six-step brief intervention.Method: A six-step brief intervention was developed based on a literature review of existing interventions for crisis management, semi-structured interviews with 19 participants comprising gambling and crisis support counsellors and consumers, as well as experts in the addiction field.Results: The resulting six-step brief-intervention focusses on (1) acknowledging and measuring distress; (2) normalising and reducing distress; (3) optimising motivation for change; (4) providing a sense of hope; (5) re-measuring distress and, if reduced; (6) exploring options for treatment and support.Conclusion: Whilst developed primarily for helpline counsellors, the intervention has potential application for health practitioners working across telephone, online and face-to-face services. Further research is required to determine its effectiveness in improving treatment engagement amongst people with gambling problems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a generalization of the power series cure rate model for the number of competing causes related to the occurrence of the event of interest. The model includes distributions not yet used in the cure rate models context, such as the Borel, Haight and Restricted Generalized Poisson distributions. The model is conveniently parameterized in terms of the cure rate. Maximum likelihood estimation based on the Expectation Maximization algorithm is discussed. A simulation study designed to assess some properties of the estimators is carried out, showing the good performance of the proposed estimation procedure in finite samples. Finally, an application to a bone marrow transplant data set is presented. [ABSTRACT FROM AUTHOR]

Drug combination trials are often motivated by the fact that individual drugs target the same disease but via different routes. A combination of such drugs may then have an overall better effect than the individual treatments which has to be verified by clinical trials. Several statistical methods have been explored that discuss the problem of comparing a fixed-dose combination therapy to each of its components. But an extension of these approaches to multiple dose combinations can be difficult and is not yet fully investigated. In this paper, we propose two approaches by which one can provide confirmatory assurance with familywise error rate control, that the combination of two drugs at differing doses is more effective than either component doses alone. These approaches involve multiple comparisons in multilevel factorial designs where the type 1 error can be controlled first, by bootstrapping tests, and second, by considering the least favorable null configurations for a family of union intersection tests. The main advantage of the new approaches is that their implementation is simple. The implementation of these new approaches is illustrated with a real data example from a blood pressure reduction trial. Extensive simulations are also conducted to evaluate the new approaches and benchmark them with existing ones. We also present an illustration of the relationship between the different approaches. We observed that the bootstrap provided some power advantages over the other approaches with the disadvantage that there may be some error rate inflation for small sample sizes. [ABSTRACT FROM AUTHOR]

This paper presents two new model-based goodness-of-fit tests for the ordered stereotype model applied to an ordinal response variable. The proposed tests are based on the Lipsitz test, which partitions the subjects into G groups following the popular Hosmer-Lemeshow test for binary data. The tests construct an alternative model where group effects are added into the null model. If the model fits the data well then the null model is correct, and there should be no group effects. One of the main advantages of the ordered stereotype model is that it allows us to determine a new uneven spacing of the ordinal response categories, dictated by the data. The two proposed tests use this new adjusted spacing. One test uses the form of the original ordered stereotype model, and the other uses an ordinary linear model. We demonstrate the performance of both tests under a variety of scenarios. Finally, the results of the application in three examples are presented. [ABSTRACT FROM AUTHOR]

Objective: This paper investigates driver engagement with vehicle automation and the transition to manual control in the context of a phenomenon that we have termed vicarious steering-drivers steering when the vehicle is under automated control.Background: Automated vehicles introduce many challenges, including disengagement from the driving task and out-of-the-loop performance decrement. We examine drivers' steering behavior when the automation is engaged, and steering input has no effect on the vehicle state. Such vicarious steering is a potential indicator of engagement for evaluating automated vehicles.Method: A total of 32 female and 32 male drivers between 25 and 55 years of age participated in this experiment. A 2 × 2 between-subject design combined control algorithms and instructed responsibility. The control algorithms (lane centering and adaptive) were intended to convey the capability of the automation. The adaptive algorithm drifted across the lane center when latent hazards were present. The instructed levels of responsibility (driver primarily responsible and automation primarily responsible) were intended to replicate the admonitions of owners' manuals.Results: The adaptive algorithm increased vicarious steering (p < .001), but instructed responsibility did not (p = .67), and there was no interaction between the algorithm and the responsibility (p = .75). Vicarious steering was associated with an increase in transitions to manual control and glances to the road but was negatively associated with driving performance immediately after the transition to manual control.Conclusion: Vicarious steering is a promising indicator of driver engagement when the vehicle is under automated control and automation algorithms can promote engagement. [ABSTRACT FROM AUTHOR]

Objective: The aim is to provide a high-level synthesis of human factors research that contributed to the development of detect-and-avoid display requirements for unmanned aircraft systems (UAS).Background: The integration of UAS into the U.S. National Airspace System is a priority under the Federal Aviation Administration's Modernization and Reform Act. For UAS to have routine access to the National Airspace System, UAS must have detect-and-avoid capabilities. One human factors challenge is to determine how to display information effectively to remote pilots for performing detect-and-avoid tasks.Method: A high-level review of research informing the display requirements for UAS detect-and-avoid is provided. In addition, description of the contributions of human factors researchers in the writing of the requirements is highlighted.Results: Findings from human-in-the-loop simulations are used to illustrate how evidence-based guidelines and requirements were established for the display of information to assist pilots in performing detect-and-avoid. Implications for human factors are discussed.Conclusion: Human factors researchers and engineers made many contributions to generate the data used to justify the detect-and-avoid display requirements. Human factors researchers must continue to be involved in the development of standards to ensure that requirements are evidence-based and take into account human operator performance and human factors principles and guidelines.Application: The research presented in this paper is relevant to the design of UAS, the writing of standards and requirements, and the work in human-systems integration. [ABSTRACT FROM AUTHOR]

Background: Recent literature, largely from Africa, shows mixed effects of own-production on diet diversity. However, the role of own-production, relative to markets, in influencing food consumption becomes more pronounced as market integration increases.Objective: This paper investigates the relative importance of two factors - production diversity and household market integration - for the intake of a nutritious diet by women and households in rural India.Methods: Data analysis is based on primary data from an extensive agriculture-nutrition survey of 3600 Indian households that was collected in 2017. Dietary diversity scores are constructed for women and households is based on 24-hour and 7-day recall periods. Household market integration is measured as monthly household expenditure on key non-staple food groups. We measure production diversity in two ways - field-level and on-farm production diversity - in order to account for the cereal centric rice-wheat cropping system found in our study locations. The analysis is based on Ordinary Least Squares regressions where we control for a variety of village, household, and individual level covariates that affect food consumption, and village fixed effects. Robustness checks are done by way of using a Poisson regression specifications and 7-day recall period.Results: Conventional measures of field-level production diversity, like the number of crops or food groups grown, have no significant association with diet diversity. In contrast, it is on-farm production diversity (the field-level cultivation of pulses and on-farm livestock management, and kitchen gardens in the longer run) that is significantly associated with improved dietary diversity scores, thus suggesting the importance of non-staples in improving both individual and household dietary diversity. Furthermore, market purchases of non-staples like pulses and dairy products are associated with a significantly higher dietary diversity. Other significant determinants of dietary diversity include women's literacy and awareness of nutrition. These results mostly remain robust to changes in the recall period of the diet diversity measure and the nature of the empirical specification.Conclusions: This study contributes to the scarce empirical evidence related to diets in India. Additionally, our results indicate some key intervention areas - promoting livestock rearing, strengthening households' market integration (for purchase of non-staples) and increasing women's awareness about nutrition. These are more impactful than raising production diversity. [ABSTRACT FROM AUTHOR]

The use of genetic variants as instrumental variables - an approach known as Mendelian randomization - is a popular epidemiological method for estimating the causal effect of an exposure (phenotype, biomarker, risk factor) on a disease or health-related outcome from observational data. Instrumental variables must satisfy strong, often untestable assumptions, which means that finding good genetic instruments among a large list of potential candidates is challenging. This difficulty is compounded by the fact that many genetic variants influence more than one phenotype through different causal pathways, a phenomenon called horizontal pleiotropy. This leads to errors not only in estimating the magnitude of the causal effect but also in inferring the direction of the putative causal link. In this paper, we propose a Bayesian approach called BayesMR that is a generalization of the Mendelian randomization technique in which we allow for pleiotropic effects and, crucially, for the possibility of reverse causation. The output of the method is a posterior distribution over the target causal effect, which provides an immediate and easily interpretable measure of the uncertainty in the estimation. More importantly, we use Bayesian model averaging to determine how much more likely the inferred direction is relative to the reverse direction. [ABSTRACT FROM AUTHOR]