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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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: 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]

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: To examine whether a fitness tracker (FT) intervention changes physical activity (PA) behavior compared to a control condition or compared to an alternative intervention.Data Source: Searches between January 01, 2010, and January 01, 2019, were conducted in PubMed, CINAHL, Cochrane CENTRAL, EMBASE, and PsycINFO.Inclusion/exclusion Criteria: Randomized clinical trials of adults using an FT to change PA behavior were included. Nonclinical trials, studies that included the delivery of structured exercise, and/or studies that only used the FT to assess PA were excluded.Data Extraction: Extracted features included characteristics of the study population, intervention components, PA outcomes, and results.Data Synthesis: Papers were pooled in a statistical meta-analysis using a fixed effects model. Where statistical pooling was not possible, standardized mean difference (SMD) and 95% confidence intervals (CI) were calculated. Findings were presented in a narrative form and tables.Results: Of 2076 articles found, 21 were included in the review. A small yet significant positive effect (SMD = 0.25, 95% CI = 0.17-0.32; P < .01; I2 = 56.9%; P = .03) was found in step count for interventions compared to control. A small yet significant negative effect (SMD = -0.11, 95% CI = -0.20 to -0.02; P = .02; I2 = 58.2%; P = 0.03) was found in moderate-to-vigorous PA for interventions compared to an alternative intervention.Conclusion: Trackers may enhance PA interventions, as a general positive effect is found in step count compared to a control. However, there is no evidence of a positive effect when interventions are compared to an alternative intervention. It is unknown whether results are due to other intervention components and/or clinical heterogeneity. [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]

Matching estimators for average treatment effects are widely used in the binary treatment setting, in which missing potential outcomes are imputed as the average of observed outcomes of all matches for each unit. With more than two treatment groups, however, estimation using matching requires additional techniques. In this paper, we propose a nearest-neighbors matching estimator for use with multiple, nominal treatments, and use simulations to show that this method is precise and has coverage levels that are close to nominal. In addition, we implement the proposed inference methods to examine the effects of different medication regimens on long-term pain for patients experiencing motor vehicle collision. [ABSTRACT FROM AUTHOR]

The problem of how to best select variables for confounding adjustment forms one of the key challenges in the evaluation of exposure or treatment effects in observational studies. Routine practice is often based on stepwise selection procedures that use hypothesis testing, change-in-estimate assessments or the lasso, which have all been criticised for - amongst other things - not giving sufficient priority to the selection of confounders. This has prompted vigorous recent activity in developing procedures that prioritise the selection of confounders, while preventing the selection of so-called instrumental variables that are associated with exposure, but not outcome (after adjustment for the exposure). A major drawback of all these procedures is that there is no finite sample size at which they are guaranteed to deliver treatment effect estimators and associated confidence intervals with adequate performance. This is the result of the estimator jumping back and forth between different selected models, and standard confidence intervals ignoring the resulting model selection uncertainty. In this paper, we will develop insight into this by evaluating the finite-sample distribution of the exposure effect estimator in linear regression, under a number of the aforementioned confounder selection procedures. We will show that by making clever use of propensity scores, a simple and generic solution is obtained in the context of generalized linear models, which overcomes this concern (under weaker conditions than competing proposals). Specifically, we propose to use separate regularized regressions for the outcome and propensity score models in order to construct a doubly robust 'g-estimator'; when these models are sufficiently sparse and correctly specified, standard confidence intervals for the g-estimator implicitly incorporate the uncertainty induced by the variable selection procedure. [ABSTRACT FROM AUTHOR]

Non-adherence to assigned treatment is a common issue in cluster randomised trials. In these settings, the efficacy estimand may also be of interest. Many methodological contributions in recent years have advocated using instrumental variables to identify and estimate the local average treatment effect. However, the clustered nature of randomisation in cluster randomised trials adds to the complexity of such analyses. In this paper, we show that the local average treatment effect can be estimated via two-stage least squares regression using cluster-level summaries of the outcome and treatment received under certain assumptions. We propose the use of baseline variables to adjust the cluster-level summaries before performing two-stage least squares in order to improve efficiency. Implementation needs to account for the reduced sample size, as well as the possible heteroscedasticity, to obtain valid inferences. Simulations are used to assess the performance of two-stage least squares of cluster-level summaries under cluster-level or individual-level non-adherence, with and without weighting and robust standard errors. The impact of adjusting for baseline covariates and of appropriate degrees of freedom correction for inference is also explored. The methods are then illustrated by re-analysing a cluster randomised trial carried out in a specific UK primary care setting. Two-stage least squares estimation using cluster-level summaries provides estimates with small to negligible bias and coverage close to nominal level, provided the appropriate small sample degrees of freedom correction and robust standard errors are used for inference. [ABSTRACT FROM AUTHOR]

In epidemiology cohort studies, exposure data are collected in sub-studies based on a primary outcome (PO) of interest, as with the extreme-value sampling design (EVSD), to investigate their correlation. Secondary outcomes (SOs) data are also readily available, enabling researchers to assess the correlations between the exposure and the SOs. However, when the EVSD is used, the data for SOs are not representative samples of a general population; thus, many commonly used statistical methods, such as the generalized linear model (GLM), are not valid. A prospective likelihood method has been developed to associate SOs with single-nucleotide polymorphisms under an extreme phenotype sequencing design. In this paper, we describe the application of the prospective likelihood method (STEVSD) to exposure-SO association analysis under an EVSD. We undertook extensive simulations to assess the performance of the STEVSD method in associating binary and continuous exposures with SOs, comparing it to the simple GLM method that ignores the EVSD. To demonstrate the cost-benefit of the STEVSD method, we also mimicked the design of two new retrospective studies, as would be done in actual practice, based on the PO of interest, which was the same as the SO in the EVSD study. We then analyzed these data by using the GLM method and compared its power to that of the STEVSD method. We demonstrated the usefulness of the STEVSD method by applying it to a benign ethnic neutropenia dataset. Our results indicate that the STEVSD method can control type I error well, whereas the GLM method cannot do so owing to its ignorance of EVSD, and that the STEVSD method is cost-effective because it has statistical power similar to that of two new retrospective studies that require collecting new exposure data for selected individuals. [ABSTRACT FROM AUTHOR]

Comparisons of survival times between screen-detected and symptomatically detected breast cancer cases are subject to lead time and length biases. Whilst the existence of these biases is well known, correction procedures for these are not always clear, as are not the interpretation of these biases. In this paper we derive, based on a recently developed continuous tumour growth model, conditional lead time distributions, using information on each individual's tumour size, screening history and percent mammographic density. We show how these distributions can be used to obtain an individual-based (conditional) procedure for correcting survival comparisons. In stratified analyses, our correction procedure works markedly better than a previously used unconditional lead time correction, based on multi-state Markov modelling. In a study of postmenopausal invasive breast cancer patients, we estimate that, in large (>12 mm) tumours, the multi-state Markov model correction over-corrects five-year survival by 2-3 percentage points. The traditional view of length bias is that tumours being present in a woman's breast for a long time, due to being slow-growing, have a greater chance of being screen-detected. This gives a survival advantage for screening cases which is not due to the earlier detection by screening. We use simulated data to share the new insight that, not only the tumour growth rate but also the symptomatic tumour size will affect the sampling procedure, and thus be a part of the length bias through any link between tumour size and survival. We explain how this has a bearing on how observable breast cancer-specific survival curves should be interpreted. We also propose an approach for correcting survival comparisons for the length bias. [ABSTRACT FROM AUTHOR]

Ordinal responses are common in clinical studies. Although the proportional odds model is a popular option for analyzing ordered-categorical data, it cannot control the type I error rate when the proportional odds assumption fails to hold. The latent Weibull model was recently shown to be a superior candidate for modeling ordinal data, with remarkably better performance than the latent normal model when the data are highly skewed. In clinical trials with ordinal responses, a balanced design is common, with equal sample allocation for each treatment. However, a more ethical approach is to adopt a response-adaptive allocation scheme in which more patients receive the better treatment. In this paper, we propose the use of the doubly adaptive biased coin design to generate treatment allocations that benefit the trial participants. The proposed treatment allocation scheme not only allows more patients to receive the better treatment, it also maintains compatible test power for the comparison of treatment efficiencies. A clinical example is used to illustrate the proposed procedure. [ABSTRACT FROM AUTHOR]

It is well known that the standard F test is severely affected by heteroskedasticity in unbalanced analysis of covariance models. Currently available potential remedies for such a scenario are based on heteroskedasticity-consistent covariance matrix estimation (HCCME). However, the HCCME approach tends to be liberal in small samples. Therefore, in the present paper, we propose a combination of HCCME and a wild bootstrap technique, with the aim of improving the small-sample performance. We precisely state a set of assumptions for the general analysis of covariance model and discuss their practical interpretation in detail, since this issue may have been somewhat neglected in applied research so far. We prove that these assumptions are sufficient to ensure the asymptotic validity of the combined HCCME-wild bootstrap analysis of covariance. The results of our simulation study indicate that our proposed test remedies the problems of the analysis of covariance F test and its heteroskedasticity-consistent alternatives in small to moderate sample size scenarios. Our test only requires very mild conditions, thus being applicable in a broad range of real-life settings, as illustrated by the detailed discussion of a dataset from preclinical research on spinal cord injury. Our proposed method is ready-to-use and allows for valid hypothesis testing in frequently encountered settings (e.g., comparing group means while adjusting for baseline measurements in a randomized controlled clinical trial). [ABSTRACT FROM AUTHOR]

Count outcomes with excessive zeros are common in behavioral and social studies, and zero-inflated count models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) can be applied when such zero-inflated count data are used as response variable. However, when the zero-inflated count data are used as predictors, ignoring the difference of structural and random zeros can result in biased estimates. In this paper, a generalized estimating equation (GEE)-type mixture model is proposed to jointly model the response of interest and the zero-inflated count predictors. Simulation studies show that the proposed method performs well for practical settings and is more robust for model misspecification than the likelihood-based approach. A case study is also provided for illustration. [ABSTRACT FROM AUTHOR]

Real phenomena often leads to challenges in data. One of these is outliers or influential values. Especially in a small sample, these values can have a major influence on the modeling process. In the beta regression framework, this issue has been addressed mainly in two ways: the assumption of a different response model and the application of a minimum density power divergence estimation (MDPDE) procedure. In this paper, however, we propose a simple hierarchical Bayesian methodology in the context of a varying dispersion beta response model that is robust to outliers, as shown through an extensive simulation study and analysis of two real data sets. To robustify Bayesian modeling, a heavy-tailed Student's t prior with uniform degrees of freedom is adopted for the regression coefficients. This proposal results in a wieldy implementation procedure which avails practical use of the approach. [ABSTRACT FROM AUTHOR]

It is often necessary to differentiate subjects from multiple categories using medical tests. We may then adopt statistical measures to characterize the performance of these tests. The three-way ROC analysis has been proposed to evaluate the diagnostic accuracy of medical tests with three categories, reflecting the correct classification probabilities across all possible decision thresholds. The geometry of the ROC surface is carefully studied, leading to numerical summary measures such as the volume under the surface. This paper generalizes the global volume under the surface of three-way ROC analysis to the weighted volume under the surface (WVUS) by introducing a weight function emphasizing particular regions of correct classification probabilities. This generalization practically allows researchers to calculate the diagnostic accuracy for a medical or clinical biomarker while satisfactorily high probabilities of correct classification for one or two classes are conditionally ensured. We provide the asymptotic properties of the proposed nonparametric and parametric estimators of WVUS, which could easily lend support to statistical inferences. Some simulations have been conducted to assess the proposed estimators and also to demonstrate the necessity of WVUS. A real data analysis about liver cancer illustrates our methodology. [ABSTRACT FROM AUTHOR]

After the diagnosis of a disease, one major objective is to predict cumulative probabilities of events such as clinical relapse or death from the individual information collected up to a prediction time, usually including biomarker repeated measurements. Several competing estimators have been proposed, mainly from two approaches: joint modelling and landmarking. These approaches differ by the information used, the model assumptions and the complexity of the computational procedures. This paper aims to review the two approaches, precisely define the derived estimators of dynamic predictions and compare their performances notably in case of misspecification. The ultimate goal is to provide key elements for the use of individual dynamic predictions in clinical practice. Prediction of two competing causes of prostate cancer progression from the history of prostate-specific antigen is used as a motivated example. We formally define the quantity to estimate and its estimators, propose techniques to assess the uncertainty around predictions and validate them. We then conduct an in-depth simulation study compare the estimators in terms of prediction error, discriminatory power, efficiency and robustness to model assumptions. We show that prediction tools should be handled with care, in particular by properly specifying models and estimators. [ABSTRACT FROM AUTHOR]

Joint modelling of longitudinal and time-to-event data has received much attention recently. Increasingly, extensions to standard joint modelling approaches are being proposed to handle complex data structures commonly encountered in applied research. In this paper, we propose a joint model for hierarchical longitudinal and time-to-event data. Our motivating application explores the association between tumor burden and progression-free survival in non-small cell lung cancer patients. We define tumor burden as a function of the sizes of target lesions clustered within a patient. Since a patient may have more than one lesion, and each lesion is tracked over time, the data have a three-level hierarchical structure: repeated measurements taken at time points (level 1) clustered within lesions (level 2) within patients (level 3). We jointly model the lesion-specific longitudinal trajectories and patient-specific risk of death or disease progression by specifying novel association structures that combine information across lower level clusters (e.g. lesions) into patient-level summaries (e.g. tumor burden). We provide user-friendly software for fitting the model under a Bayesian framework. Lastly, we discuss alternative situations in which additional clustering factor(s) occur at a level higher in the hierarchy than the patient-level, since this has implications for the model formulation. [ABSTRACT FROM AUTHOR]

In recent years, there has been a prominent discussion in the literature about the potential for overestimation of the treatment effect when a clinical trial stops at an interim analysis due to the experimental treatment showing a benefit over the control. However, there has been much less attention paid to the converse issue, namely, that sequentially monitored clinical trials which did not stop early for benefit tend to underestimate the treatment effect. In meta-analyses of many studies, these two sources of bias will tend to balance each other to produce an unbiased estimate of the treatment effect. However, for the interpretation of a single study in isolation, underestimation due to interim analysis may be an important consideration. In this paper, we discuss the nature of this underestimation, including theoretical and simulation results demonstrating that it can be substantial in some contexts. Furthermore, we show how a conditional approach to estimation, in which we condition on the study reaching its final analysis, may be used to validly inflate the observed treatment difference from a sequentially monitored clinical trial. Expressions for the conditional bias and information are derived, and these are used in supplied R code that computes the bias-adjusted estimate and an associated confidence interval. As well as simulation results demonstrating the validity of the methods, we present a data analysis example from a pivotal clinical trial in cardiovascular disease. The methods will be most useful when an unbiased treatment effect estimate is critical, such as in cost-effectiveness analysis or risk prediction. [ABSTRACT FROM AUTHOR]

The accuracy of a diagnostic test, which is often quantified by a pair of measures such as sensitivity and specificity, is critical for medical decision making. Separate studies of an investigational diagnostic test can be combined through meta-analysis; however, such an analysis can be threatened by publication bias. To the best of our knowledge, there is no existing method that accounts for publication bias in the meta-analysis of diagnostic tests involving bivariate outcomes. In this paper, we extend the Copas selection model from univariate outcomes to bivariate outcomes for the correction of publication bias when the probability of a study being published can depend on its sensitivity, specificity, and the associated standard errors. We develop an expectation-maximization algorithm for the maximum likelihood estimation under the proposed selection model. We investigate the finite sample performance of the proposed method through simulation studies and illustrate the method by assessing a meta-analysis of 17 published studies of a rapid diagnostic test for influenza. [ABSTRACT FROM AUTHOR]

Agreement studies are of paramount importance in various scientific domains. When several observers classify objects on categorical scales, agreement can be quantified through multirater kappa coefficients. In most statistical packages, the standard error of these coefficients is only available under the null hypothesis that the coefficient is equal to zero, preventing the construction of confidence intervals in the general case. The aim of this paper is triple. First, simple analytic formulae for the standard error of multirater kappa coefficients will be given in the general case. Second, these formulae will be extended to the case of multilevel data structures. The formulae are based on simple matrix algebra and are implemented in the R package "multiagree". Third, guidelines on the choice between the different mulitrater kappa coefficients will be provided. [ABSTRACT FROM AUTHOR]

Correct classification of breast cancer subtypes is of high importance as it directly affects the therapeutic options. We focus on triple-negative breast cancer which has the worst prognosis among breast cancer types. Using cutting edge methods from the field of robust statistics, we analyze Breast Invasive Carcinoma transcriptomic data publicly available from The Cancer Genome Atlas data portal. Our analysis identifies statistical outliers that may correspond to misdiagnosed patients. Furthermore, it is illustrated that classical statistical methods may fail to identify outliers due to their heavy influence, prompting the need for robust statistics. Using robust sparse logistic regression we obtain 36 relevant genes, of which ca. 60% have been previously reported as biologically relevant to triple-negative breast cancer, reinforcing the validity of the method. The remaining 14 genes identified are new potential biomarkers for triple-negative breast cancer. Out of these, JAM3, SFT2D2, and PAPSS1 were previously associated to breast tumors or other types of cancer. The relevance of these genes is confirmed by the new DetectDeviatingCells outlier detection technique. A comparison of gene networks on the selected genes showed significant differences between triple-negative breast cancer and non-triple-negative breast cancer data. The individual role of FOXA1 in triple-negative breast cancer and non-triple-negative breast cancer, and the strong FOXA1-AGR2 connection in triple-negative breast cancer stand out. The goal of our paper is to contribute to the breast cancer/triple-negative breast cancer understanding and management. At the same time it demonstrates that robust regression and outlier detection constitute key strategies to cope with high-dimensional clinical data such as omics data. [ABSTRACT FROM AUTHOR]

With rapid aging of world population, Alzheimer's disease is becoming a leading cause of death after cardiovascular disease and cancer. Nearly 10% of people who are over 65 years old are affected by Alzheimer's disease. The causes have been studied intensively, but no definitive answer has been found. Genetic predisposition, abnormal protein deposits in brain, and environmental factors are suspected to play a role in the development of this disease. In this paper, we model progression of Alzheimer's disease using a multi-state Markov model to investigate the significance of known risk factors such as age, apolipoprotein E4, and some brain structural volumetric variables from magnetic resonance imaging scans (e.g., hippocampus, etc.) while predicting transitions between different clinical diagnosis states. With the Alzheimer's Disease Neuroimaging Initiative data, we found that the model with age is not significant (p = 0.1733) according to the likelihood ratio test, but the apolipoprotein E4 is a significant risk factor, and the examination of apolipoprotein E4-by-sex interaction suggests that the apolipoprotein E4 link to Alzheimer's disease is stronger in women. Given the estimated transition probabilities, the prediction accuracy is as high as 0.7849. [ABSTRACT FROM AUTHOR]