Win statistics (win ratio, win odds, and net benefit) can complement one another to show the strength of the treatment effect on time-to-event outcomes.

Conventional analyses of a composite of multiple time-to-event outcomes use the time to the first event. However, the first event may not be the most important outcome. To address this limitation, generalized pairwise comparisons and win statistics (win ratio, win odds, and net benefit) have become popular and have been applied to clinical trial practice. However, win ratio, win odds, and net benefit have typically been used separately. In this article, we examine the use of these three win statistics jointly for time-to-event outcomes. First, we explain the relation of point estimates and variances among the three win statistics, and the relation between the net benefit and the Mann-Whitney U statistic. Then we explain that the three win statistics are based on the same win proportions, and they test the same null hypothesis of equal win probabilities in two groups. We show theoretically that the Z-values of the corresponding statistical tests are approximately equal; therefore, the three win statistics provide very similar p-values and statistical powers. Finally, using simulation studies and data from a clinical trial, we demonstrate that, when there is no (or little) censoring, the three win statistics can complement one another to show the strength of the treatment effect. However, when the amount of censoring is not small, and without adjustment for censoring, the win odds and the net benefit may have an advantage for interpreting the treatment effect; with adjustment (e.g., IPCW adjustment) for censoring, the three win statistics can complement one another to show the strength of the treatment effect. For calculations we use the R package WINS, available on the CRAN (Comprehensive R Archive Network).

Design of paediatric trials with benefit-risk endpoints using a composite score of adverse events of interest (AEI) and win-statistics.

A fundamental problem in the regulatory evaluation of a therapy is assessing whether the benefit outweighs the associated risks. This work proposes designing a trial that assesses a composite endpoint consisting of benefit and risk, hence, making the core of the design of the study, to assess benefit and risk. The proposed benefit risk measure consists of efficacy measure(s) and a risk measure that is based on a composite score obtained from pre-defined adverse events of interest (AEI). This composite score incorporates full aspects of adverse events of interest (i.e. the incidence, severity, and duration of the events). We call this newly proposed score the AEI composite score. After specifying the priorities between the components of the composite endpoint, a win-statistic (i.e. win ratio, win odds, or net benefit) is used to assess the difference between treatments in this composite endpoint. The power and sample size requirements of such a trial design are explored via simulation. Finally, using Dupixent published adult study results, we show how we can design a paediatric trial where the primary outcome is a composite of prioritized outcomes consisting of efficacy endpoints and the AEI composite score endpoint. The resulting trial design can potentially substantially reduce sample size compared to a trial designed to assess the co-primary efficacy endpoints, therefore it may address the challenge of slow enrollment and patient availability for paediatric studies.

Relation of post-traumatic stress disorder symptom severity to the efficacy of an animal-assisted intervention for stress reduction after military aeromedical evacuation.

Animal-assisted interventions (AAIs) have been found to decrease stress in some settings, but it is not known if AAI is feasible in an aeromedical staging facility or effective in reducing stress following aeromedical evacuation (AE) of military personnel. An experimental design was used to evaluate the efficacy of AAI at reducing stress in AE military patients (N = 120). Patients participated in a 20-min AAI (n = 60) or 20-min informational session about assistance dogs as an attention-control group (n = 60). Demographics, post-traumatic stress symptom severity (PTSSS), and stress biomarkers (cortisol, alpha-amylase, and immunoglobulin A) were collected regular intervals. AAI was found feasible and efficacious at reducing stress. Cortisol decreased significantly (p < .05) in the AAI group compared with the attention-control group. PTSSS moderated the immunoglobulin A responses to AAI as demonstrated by the interaction of PTSD Checklist-Military Version score, group, and time, F(1, 111.23) = 4.15 p = .044; effect size: d = 0.31. This research supports AAI as a stress-reducing modality in AE patients, particularly those who report higher PTSSS. Implications for future research are discussed.

Penalized likelihood and multiple testing.

The classical multiple testing model remains an important practical area of statistics with new approaches still being developed. In this paper we develop a new multiple testing procedure inspired by a method sometimes used in a problem with a different focus. Namely, the inference after model selection problem. We note that solutions to that problem are often accomplished by making use of a penalized likelihood function. A classic example is the Bayesian information criterion (BIC) method. In this paper we construct a generalized BIC method and evaluate its properties as a multiple testing procedure. The procedure is applicable to a wide variety of statistical models including regression, contrasts, treatment versus control, change point, and others. Numerical work indicates that, in particular, for sparse models the new generalized BIC would be preferred over existing multiple testing procedures.

Examining the Effects of a Service-Trained Facility Dog on Stress in Children Undergoing Forensic Interview for Allegations of Child Sexual Abuse.

Disclosure of child sexual abuse can be a stressful experience for the child. Gaining a better understanding of how best to serve the child, while preserving the quality of their disclosure, is an ever-evolving process. The data to answer this question come from 51 children aged 4-16 (M = 9.1, SD = 3.5), who were referred to a child advocacy center in Virginia for a forensic interview (FI) following allegations of sexual abuse. A repeated measures design was conducted to examine how the presence of a service-trained facility dog (e.g. animal-assisted intervention (AAI) may serve as a mode of lowering stress levels in children during their FIs. Children were randomized to one of the two FI conditions: experimental condition (service-trained facility dog present-AAI) or control condition (service-trained facility dog not present- standard forensic interview). Stress biomarkers salivary cortisol, alpha-amylase, immunoglobulin A (IgA), heart rate, and blood pressure, and Immunoglobulin A were collected before and after the FI. Self-report data were also collected. Results supported a significant decrease in heart rate for those in the experimental condition (p = .0086) vs the control condition (p = .4986). Regression models revealed a significant decrease in systolic and diastolic blood pressure in the experimental condition (p = .03285) and (p = .04381), respectively. Statistically significant changes in alpha-amylase and IgA were also found in relation to disclosure and type of offense. The results of this study support the stress reducing effects of a service-trained facility dog for children undergoing FI for allegations of child sexual abuse.

Effects of VA Facility Dog on Hospitalized Veterans Seen by a Palliative Care Psychologist: An Innovative Approach to Impacting Stress Indicators.

The United States is home to 23 million veterans. In many instances, veterans with serious illness who seek healthcare at the VA receive care from a palliative care service. Animal-assisted intervention (AAI) is gaining attention as a therapeutic stress reducing modality; however, its effects have not been well studied in veterans receiving palliative care in an acute care setting. A crossover repeated-measures study was conducted to examine the effects of an animal-assisted intervention (AAI) in the form of a therapy dog on stress indicators in 25 veterans on the palliative care service at the VA Eastern Colorado Healthcare System in Denver, CO. Veterans had a visit from a therapy dog and the dog's handler, a clinical psychologist (experimental condition) and an unstructured visit with the clinical psychologist alone (control condition). Blood pressure, heart rate, and the salivary biomarkers cortisol, alpha-amylase, and immunoglobulin A were collected before, after, and 30-minutes after both the experimental and control conditions. Significant decreases in cortisol were found when the before time period was compared to the 30-minutes after time period for both the experimental ( p = 0.007) and control condition ( p = 0.036). A significant decrease in HR was also found when the before time period was compared to the 30-minutes after time period for both the experimental ( p = 0.0046) and control ( p = 0.0119) condition. Results of this study supported that a VA facility dog paired with a palliative care psychologist had a measurable impact on salivary cortisol levels and HR in veterans.

Pet Therapy: Enhancing Social and Cardiovascular Wellness in Community Dwelling Older Adults.

Pet therapy can be therapeutic for older adults living in the community. A crossover design was used to examine changes in blood pressure and heart rate before and after a pet therapy visit versus a volunteer-only visit in 28 community dwelling older adults. Relationships among stress, pet attitude, social support, and health status were also examined. Study findings supported that pet therapy significantly decreased blood pressure and heart rate. Ultimately, the findings supported the notion that community health nurses should consider developing and implementing pet therapy programs in the communities they serve. Further implications for community health nurses are discussed.

Evaluation of Identified Stressors in Children and Adolescents After Super Storm Sandy.

Super Storm Sandy, one of the largest storms endured by the East Coast of theUnited States, devastated New Jersey and the eastern seaboard. Although naturaldisasters affect individuals of all ages, children are particularly vulnerable becausetheir sense of normalcy is altered. The purpose of this study was to explore theeffects that exposure to Super Storm Sandy had on children who resided in NewJersey. This was a non-experimental, quantitative, cross-sectional research study.Study participants were recruited via printed flyers at disaster resource sites and ona dedicated research team's Facebook site. Each participant completed theHurricane Stressors Assessment Tool for Children and Adolescents as a webbasedsurvey related to their experiences with the hurricane. One hundred andforty-one (141) children participated in this study. Age groups (preschool, child, andadolescent) had varied results based upon developmental level. Age was positivelyassociated with finding it harder to concentrate and pay attention (r = 0.18, p =0.04); feeling sad, down, or depressed (r = 0.17, p < 0.05); being quiet and withdrawn (r = 0.16, p = 0.05); feeling irritable and grouchy (r = 0.26, p < 0.05); and findingit harder to complete schoolwork (r = 0.32, p < 0.001). Certain parental perceptionsof their child's behavior were negatively associated with the age of the child.Children had varying degrees of experiences after Sandy. Adolescents were shownto be more aware and affected by the storm than younger children. Observationscan be used for intervention initiatives in the post-natural disaster period, encouraginghealthcare providers to acknowledge family and community healing to provideadequate mental health referrals in the post-disaster period.

Nonparametric multivariate inference on shift parameters.

RATIONALE AND OBJECTIVES: Consider a study evaluating the prognostic value of prostate-specific antigen (PSA), in the presence of Gleason score, in differentiating between early and advanced prostate cancer. This data set features subjects divided into two groups (early versus advanced cancer), with one manifest variable (PSA), one covariate (Gleason score), and one stratification variable (hospital, taking three values). We present a nonparametric method for estimating a shift in median PSA score between the two groups, after adjusting for differences in Gleason score and stratifying on hospital. This method can also be extended to cases involving multivariate manifest variable. MATERIALS AND METHODS: Our method uses estimating equations derived from an existing rank-based estimator of the area under the receiver operating characteristic curve (AUC). This existing AUC estimator is adjusted for stratification and for covariates. We use the adjusted AUC estimator to construct a family of tests by shifting manifest variables in one of the treatment groups by an arbitrary constant. The null hypothesis for these tests is that the AUC is half. We report the set of shift values for which the null hypothesis is not rejected as the resulting confidence region. RESULTS: Simulated data show performance consistent with the distributional approximations used by the proposed methodology. This methodology is applied to two examples. In the first example, the mean difference in PSA levels between advanced and nonadvanced prostate cancer patients is estimated, controlling for Gleason score. In the second example, to assess the degree to which age and baseline tumor size are prognostic factors for breast cancer recurrence, differences in age and tumor size between subjects with recurrent and nonrecurrent breast cancer, stratified on Tamoxifen treatment and adjusting for tumor grade, are estimated. CONCLUSIONS: The proposed methodology provides a nonparametric method for a statistic measuring adjusted AUC to be used to estimate shift between two manifest variables.

Faculty mentors' and students' perceptions of students' research self-efficacy.

Mentoring in nursing is an important process for socializing nurse researchers, developing a body of professional knowledge, and influencing career choices of students. Self-efficacy (Bandura, 1997) is concerned with one's perceived ability to perform tasks within a specific domain. The purpose of this study was to compare undergraduate and graduate student's perceptions of their abilities to pursue research (research self-efficacy) with their mentors' perceptions. A cross-sectional design was used to study mentors in any academic discipline who received external funding and worked with an undergraduate or graduate student on the research study. Recruitment and data collection were completed using the Internet and included 21 faculty mentors and student dyads. The Clinical Research Appraisal Inventory was used to measure research self-efficacy. Differences between the faculty mentor's perception of the student's confidence in research and students' perception were significant at p=<0.001. Misjudgment of efficacy appraisals can result in opportunities forsaken and careers not pursued. Assisting mentors to guide students' skill perfection may increase students' choice of research careers, promote the effectiveness of mentorship, aid in the development of a body of professional knowledge and benefit careers of both mentors and students.

Supplier selection based on a neural network model using genetic algorithm.

In this paper, a decision-making model was developed to select suppliers using neural networks (NNs). This model used historical supplier performance data for selection of vendor suppliers. Input and output were designed in a unique manner for training purposes. The managers' judgments about suppliers were simulated by using a pairwise comparisons matrix for output estimation in the NN. To obtain the benefit of a search technique for model structure and training, genetic algorithm (GA) was applied for the initial weights and architecture of the network. The suppliers' database information (input) can be updated over time to change the suppliers' score estimation based on their performance. The case study illustrated shows how the model can be applied for suppliers' selection.

Patient complexity in quality comparisons for glycemic control: an observational study.

BACKGROUND: Patient complexity is not incorporated into quality of care comparisons for glycemic control. We developed a method to adjust hemoglobin A1c levels for patient characteristics that reflect complexity, and examined the effect of using adjusted A1c values on quality comparisons. METHODS: This cross-sectional observational study used 1999 national VA (US Department of Veterans Affairs) pharmacy, inpatient and outpatient utilization, and laboratory data on diabetic veterans. We adjusted individual A1c levels for available domains of complexity: age, social support (marital status), comorbid illnesses, and severity of disease (insulin use). We used adjusted A1c values to generate VA medical center level performance measures, and compared medical center ranks using adjusted versus unadjusted A1c levels across several thresholds of A1c (8.0%, 8.5%, 9.0%, and 9.5%). RESULTS: The adjustment model had R2 = 8.3% with stable parameter estimates on thirty random 50% resamples. Adjustment for patient complexity resulted in the greatest rank differences in the best and worst performing deciles, with similar patterns across all tested thresholds. CONCLUSION: Adjustment for complexity resulted in large differences in identified best and worst performers at all tested thresholds. Current performance measures of glycemic control may not be reliably identifying quality problems, and tying reimbursements to such measures may compromise the care of complex patients.

Classification tree modeling to identify severe and moderate vehicular injuries in young and middle-aged adults.

OBJECTIVES: Motor vehicle crashes are a leading cause of mortality and morbidity worldwide. Even though trauma centers provide the gold standard of care for motor vehicle crash patients with life- or limb-threatening injuries, many whose lives might be saved by trauma center care are treated instead at non-trauma center hospitals. Triage algorithms, designed to identify patients with life- or limb-threatening injuries who should be transported to a trauma center, lack appropriate sensitivity to many of these injuries. The challenge to the trauma community is differentiating patients with life- or limb-threatening injuries from those with less severe injuries at the crash scene so that the patients can be transported to the most appropriate level of care. The purpose of this study was to use crash scene data available to emergency responders to classify adults with moderate and severe injuries. These classifiers might be useful to guide triage decision making. METHODS AND MATERIAL: Records of 74,626 adults, age 18-64 years, from the National Automotive Sampling System Crashworthiness Data Systems database were analyzed using classification and regression trees (CART) analysis. Both CART models (moderate injury and severe injury) included 13 predictor variables. The response variables were the targeted injury severity score cut points for moderate and severe injury. Two final classification trees were developed: one that classified occupants based on moderate injury and the other on severe injury. Misclassification costs were manipulated to achieve the best model fit for each tree. RESULTS: The moderate injury classification tree had three splitters: police-estimated injury severity, restraint use, and number of persons injured. The severe injury classification tree had four splitters: police-estimated injury severity, manner of collision, number of persons injured in the crash, and age. Sensitivity and specificity of the classification trees were 93.70%, 77.53% (moderate) and 99.18%, 73.96% (severe), respectively. CONCLUSIONS: CART analysis can be used to classify injury severity using crash scene information that is available to emergency responders. This procedure offers an opportunity to examine alternative methods of identifying injury severity that might assist emergency responders to differentiate more accurately persons who should receive trauma center care from those who can be treated safely at a non-trauma center hospital.

Quality of life of men treated for localized prostate cancer: outcomes at 6 and 12 months.

GOALS OF WORK: Treatments for early-stage prostate cancer (PCa) are highly effective; therefore, research studies that explore quality of life (QOL) issues associated with different treatments are important. The purposes of this study were to (a) examine differences among treatment groups of men treated with either radiation therapies or radical prostatectomy for PCa and (b) examine quality of life outcomes over time. PATIENTS AND METHODS: We report outcomes 6 and 12 months after 159 men began treatment for PCa with either one of two types of radiation treatment (intensity-modulated radiation therapy plus high dose rate or intensity-modulated radiation therapy plus seed implantation) or radical prostatectomy. MAIN RESULTS: Significant differences among groups are described. Significant predictors of QOL at 6 months included urinary, bowel, and sexual symptoms, anxiety, depression, problem-focused coping, and physiological self-efficacy. Significant predictors of QOL at 12 months were urinary and bowel symptoms, stress, depression, problem-focused coping, and physiological self-efficacy. Demographic variables, race, and living status were significant predictors of quality of life at 12 months. CONCLUSIONS: Physiological symptoms and psychological symptoms were both significant predictors of QOL. The psychological factors that predicted quality of life in this study have potential for intervention and point to the next stage of the research.

Evaluation of injury databases as a preliminary step to developing a triage decision rule.

PURPOSE: To evaluate accessibility and appropriateness of the Crash Outcomes Data Evaluation Systems (CODES) databases for prehospital trauma triage decision-rule development for people age 65 years and older. DESIGN AND METHODS: This informatics feasibility study included five steps for evaluating the accessibility of CODES databases. Eight criteria were used to evaluate the appropriateness of these databases for older person prehospital trauma triage decision-rule development. FINDINGS: Only 4 of the 33 states funded for CODES database development released their data to the study team during the 13-month data-acquisition period. Potential predictor variables (of life-threatening injury) and outcome variables (need for trauma center care) were identified for each database. Several databases had large amounts of missing data. Codebooks were available but descriptions of data validation procedures were unavailable. CONCLUSIONS: At this time, limited access to and development of CODES databases and missing data preclude the usefulness of these databases for older person triage decision rule development. Although adequate funding must be appropriated for continued CODES development, and commitment from individual states is essential, these databases offer great promise as a mechanism for decision-rule development to guide triage decision-making. Investigators should systematically evaluate large databases before using them in secondary analyses for clinical decision rule development. CLINICAL RELEVANCE: Nurses participate in the planning, development, and implementation of health information systems in various settings. They also assume important roles in prehospital care as direct care providers, EMS administrators, participants of local and state EMS councils where emergency care problems are discussed and policies are formulated, and through use at state and federal levels.

Using crash scene variables to predict the need for trauma center care in older persons.

Current trauma triage protocols lack sensitivity to occult injuries in older persons, resulting in unacceptable undertriage rates. We identified crash scene information that could be used by emergency personnel to identify the need for trauma center care in older persons injured in motor vehicle crashes. Crash records of 7,883 persons 65 years and older were explored using classification and regression trees (CART) analysis. CART analysis of 26 crash scene variables resulted in two classification trees from which triage decision rules were stated for persons with severe and moderate injuries. Sensitivity and specificity of the rules were 95.15% and 76.47% for severe injury and 83.1% and 81.5% for moderate injury.

Exploratory factor analysis: strategies used by patients to promote health.

Strategies used by patients to promote health (SUPPH) was used to measure self-care self-efficacy in patients with cancer. The objectives of this study were (1) to determine the extent to which self-efficacy theory explained the factor structure of the SUPPH and (2) to determine the relationship of demographic data with factors of the SUPPH. Subjects were diagnosed with prostate cancer (PCa) and treated with either: (a) radical prostatectomy, (b) intensity modulated radiation therapy (IMRT) + radioactive seed implantation, or (c) IMRT + high dose rate. Subjects completed a demographic questionnaire and the SUPPH. Exploratory factor analysis of the SUPPH was performed using a varimax rotation. Subjects (n = 265) were predominately white and averaged 68 years of age. The model explained 81.3% of the total sum of eigenvalues. Two factors of the SUPPH were identified: physiological efficacy information and performance efficacy information. Younger subjects who were fully employed and earning more money had significantly higher performance self-efficacy than older subjects who were working part time and earning less money. Results are congruent with Bandura's (1997) description of self-efficacy. Use of the SUPPH may facilitate research validating Bandura's (1997) assertion that an individual's self-efficacy is related to quality of life (QOL) during chronic illness. Additional research focusing on self-efficacy and PCa patients' QOL may lead to efficacy enhancing interventions that will improve QOL of patients with PCa.

Diabetes healthcare quality report cards: how accurate are the grades?

OBJECTIVE: To evaluate the accuracy and precision of random sampling in identifying healthcare system outliers in diabetes performance measures. STUDY DESIGN: Cross-sectional analysis of 79 Veterans Health Administration facilities serving 250 317 patients with diabetes mellitus between October 1, 1999, and September 30, 2000. METHODS: Primary outcome measures were poor glycosylated hemoglobin (A1C) control and good low-density lipoprotein cholesterol (LDL-C) and blood pressure (BP) control. Facility performance for each measure was calculated using 150 separate random samples and was compared with results using the bootstrap method as the criterion standard for determining outlier status (defined as a >/=5% difference from the mean, within the 10th or 90th percentile, or >/=2 SDs from the mean). RESULTS: The study population was largely male (97.4%), with 54.0% of subjects being 65 years or older. The facility-level mean performances were 22.8% for poor A1C control, 53.1% for good LDL-C control, and 55.3% for good BP control. Comparing the random sampling method with the bootstrap method, the sensitivity ranged between 0.64 and 0.83 for the 3 outcome measures, positive predictive values ranged between 0.55 and 0.88, and specificity and negative predictive values ranged between 0.88 and 0.99. CONCLUSIONS: The specificity and negative predictive value of the random sampling method in identifying nonoutliers in performance were generally high, while its sensitivity and positive predictive value were moderate. The use of random sampling to determine performance for individual outcome measures may be most appropriate for internal quality improvement rather than for public reporting.

Assessing quality of diabetes care by measuring longitudinal changes in hemoglobin A1c in the Veterans Health Administration.

CONTEXT: A1c levels are widely used to assess quality of diabetes care provided by health care systems. Currently, cross-sectional measures are commonly used for such assessments. OBJECTIVE: To study within-patient longitudinal changes in A1c levels at Veterans Health Administration (VHA) facilities as an alternative to cross-sectional measures of quality of diabetes care. DESIGN: Longitudinal study using institutional data on individual patient A1c level over time (October 1, 1998-September 30, 2000) with time variant and invariant covariates. SETTING: One hundred and twenty-five VHA facilities nationwide, October 1, 1998-September 30, 2000. PATIENTS: Diabetic veteran users with A1c measurement performed using National Glycosylated Hemoglobin Standardization Project certified A1c lab assay methods. EXPOSURES: Characteristics unlikely to reflect quality of care, but known to influence A1c levels, demographics, and baseline illness severity. MAIN OUTCOME MEASURE: Monthly change in A1c for average patient cared for at each facility. RESULTS: The preponderance of facilities showed monthly declines in within-patient A1c over the study period (mean change of -0.0148 A1c units per month, range -0.074 to 0.042). Individual facilities varied in their monthly change, with 105 facilities showing monthly declines (70 significant at .05 level) and 20 showing monthly increases (5 significant at .05 level). Case-mix adjustment resulted in modest changes (mean change of -0.0131 case-mix adjusted A1c units per month, range -0.079 to 0.043). Facilities were ranked from worst to best, with attached 90 percent confidence intervals. Among the bottom 10 ranked facilities, four remained within the bottom decile with 90 percent confidence. CONCLUSIONS: There is substantial variation in facility-level longitudinal changes in A1c levels. We propose that evaluation of change in A1c levels over time can be used as a new measure to reflect quality of care provided to populations of individuals with chronic disease.

Seasonal patterns in monthly hemoglobin A1c values.

The purpose of this study was to investigate seasonal variations in population monthly hemoglobin A(1c) (A1c) values over 2 years (from October 1998 to September 2000) among US diabetic veterans. The study cohort included 285,705 veterans with 856,181 A1c tests. The authors calculated the monthly average A1c values for the overall population and for subpopulations defined by age, sex, race, insulin use, and climate regions. A1c values were higher in winter and lower in summer with a difference of 0.22. The proportion of A1c values greater than 9.0% followed a similar seasonal pattern that varied from 17.3% to 25.3%. Seasonal autoregressive models including trigonometric function terms were fit to the monthly average A1c values. There were significant seasonal effects; the seasonal variation was consistent across different subpopulations. Regions with colder winter temperatures had larger winter-summer contrasts than did those with warmer winter temperatures. The seasonal patterns followed trends similar to those of many physiologic markers, cardiovascular and other diabetes outcomes, and mortality. These findings have implications for health-care service research in quality-of-care assessment, epidemiologic studies investigating population trends and risk factors, and clinical trials or program evaluations of treatments or interventions.