Howard D. Bondell

Optimal Cut-point and Its Corresponding Youden Index to Discriminate Individuals Using Pooled Blood Samples

Costs can hamper the evaluation of the effectiveness of new biomarkers. Analysis of smaller numbers of pooled specimens has been shown to be a useful cost-cutting technique. The Youden index (J), a function of sensitivity (q) and specificity (p), is a commonly used measure of overall diagnostic effectiveness. More importantly, J is the maximum vertical distance or difference between the ROC curve and the diagonal or chance line; it occurs at the cut-point that optimizes the biomarkers differentiating ability when equal weight is given to sensitivity and specificity. Using the additive property of the gamma and normal distributions, we present a method to estimate the Youden index and the optimal cut-point, and extend its applications to pooled samples. We study the effect of pooling when only a fixed number of individuals are available for testing, and pooling is carried out to save on the number of assays. We measure loss of information by the change in root mean squared error of the estimates of the optimal cut-point and the Youden index, and we study the extent of this loss via a simulation study. In conclusion, pooling can result in a substantial cost reduction while preserving the effectiveness of estimators, especially when the pool size is not very large.

Flexible Bayesian quantile regression for independent and clustered data

Quantile regression has emerged as a useful supplement to ordinary mean regression. Traditional frequentist quantile regression makes very minimal assumptions on the form of the error distribution and thus is able to accommodate nonnormal errors, which are common in many applications. However, inference for these models is challenging, particularly for clustered or censored data. A Bayesian approach enables exact inference and is well suited to incorporate clustered, missing, or censored data. In this paper, we propose a flexible Bayesian quantile regression model. We assume that the error distribution is an infinite mixture of Gaussian densities subject to a stochastic constraint that enables inference on the quantile of interest. This method outperforms the traditional frequentist method under a wide array of simulated data models. We extend the proposed approach to analyze clustered data. Here, we differentiate between and develop conditional and marginal models for clustered data. We apply our methods to analyze a multipatient apnea duration data set.

Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models

It is of great practical interest to simultaneously identify the important predictors that correspond to both the fixed and random effects components in a linear mixed-effects (LME) model. Typical approaches perform selection separately on each of the fixed and random effect components. However, changing the structure of one set of effects can lead to different choices of variables for the other set of effects. We propose simultaneous selection of the fixed and random factors in an LME model using a modified Cholesky decomposition. Our method is based on a penalized joint log likelihood with an adaptive penalty for the selection and estimation of both the fixed and random effects. It performs model selection by allowing fixed effects or standard deviations of random effects to be exactly zero. A constrained expectation-maximization algorithm is then used to obtain the final estimates. It is further shown that the proposed penalized estimator enjoys the Oracle property, in that, asymptotically it performs as well as if the true model was known beforehand. We demonstrate the performance of our method based on a simulation study and a real data example.

Item generation and design testing of a questionnaire to assess degenerative joint disease-associated pain in cats

OBJECTIVE To determine the items (question topics) for a subjective instrument to assess degenerative joint disease (DJD)-associated chronic pain in cats and determine the instrument design most appropriate for use by cat owners. ANIMALS 100 randomly selected client-owned cats from 6 months to 20 years old. PROCEDURES Cats were evaluated to determine degree of radiographic DJD and signs of pain throughout the skeletal system. Two groups were identified: high DJD pain and low DJD pain. Owner-answered questions about activity and signs of pain were compared between the 2 groups to define items relating to chronic DJD pain. Interviews with 45 cat owners were performed to generate items. Fifty-three cat owners who had not been involved in any other part of the study, 19 veterinarians, and 2 statisticians assessed 6 preliminary instrument designs. RESULTS 22 cats were selected for each group; 19 important items were identified, resulting in 12 potential items for the instrument; and 3 additional items were identified from owner interviews. Owners and veterinarians selected a 5-point descriptive instrument design over 11-point or visual analogue scale formats. CONCLUSIONS AND CLINICAL RELEVANCE Behaviors relating to activity were substantially different between healthy cats and cats with signs of DJD-associated pain. Fifteen items were identified as being potentially useful, and the preferred instrument design was identified. This information could be used to construct an owner-based questionnaire to assess feline DJD-associated pain. Once validated, such a questionnaire would assist in evaluating potential analgesic treatments for these patients.

Simultaneous Factor Selection and Collapsing Levels in ANOVA

When performing an analysis of variance, the investigator often has two main goals: to determine which of the factors have a significant effect on the response, and to detect differences among the levels of the significant factors. Level comparisons are done via a post-hoc analysis based on pairwise differences. This article proposes a novel constrained regression approach to simultaneously accomplish both goals via shrinkage within a single automated procedure. The form of this shrinkage has the ability to collapse levels within a factor by setting their effects to be equal, while also achieving factor selection by zeroing out entire factors. Using this approach also leads to the identification of a structure within each factor, as levels can be automatically collapsed to form groups. In contrast to the traditional pairwise comparison methods, these groups are necessarily nonoverlapping so that the results are interpretable in terms of distinct subsets of levels. The proposed procedure is shown to have the oracle property in that asymptotically it performs as well as if the exact structure were known beforehand. A simulation and real data examples show the strong performance of the method.

Overcoming skepticism with education: interacting influences of worldview and climate change knowledge on perceived climate change risk among adolescents

Though many climate literacy efforts attempt to communicate climate change as a risk, these strategies may be ineffective because among adults, worldview rather than scientific understanding largely drives climate change risk perceptions. Further, increased science literacy may polarize worldview-driven perceptions, making some climate literacy efforts ineffective among skeptics. Because worldviews are still forming in the teenage years, adolescents may represent a more receptive audience. This study examined how worldview and climate change knowledge related to acceptance of anthropogenic global warming (AGW) and in turn, climate change risk perception among middle school students in North Carolina, USA (n = 387). We found respondents with individualistic worldviews were 16.1 percentage points less likely to accept AGW than communitarian respondents at median knowledge levels, mirroring findings in similar studies among adults. The interaction between knowledge and worldview, however, was opposite from previous studies among adults, because increased climate change knowledge was positively related to acceptance of AGW among both groups, and had a stronger positive relationship among individualists. Though individualists were 24.1 percentage points less likely to accept AGW than communitarians at low levels (bottom decile) of climate change knowledge, there was no statistical difference in acceptance levels between individualists and communitarians at high levels of knowledge (top decile). Non-White and females also demonstrated higher levels of AGW acceptance and climate change risk perception, respectively. Thus, education efforts specific to climate change may counteract divisions based on worldviews among adolescents.

Variable Selection in Bayesian Smoothing Spline ANOVA Models: Application to Deterministic Computer Codes

With many predictors, choosing an appropriate subset of the covariates is a crucial—and difficult—step in nonparametric regression. We propose a Bayesian nonparametric regression model for curve fitting and variable selection. We use the smoothing splines ANOVA framework to decompose the regression function into interpretable main effect and interaction functions, and use stochastic search variable selection through Markov chain Monte Carlo sampling to search for models that fit the data well. We also show that variable selection is highly sensitive to hyperparameter choice, and develop a technique for selecting hyperparameters that control the long-run false-positive rate. We use our method to build an emulator for a complex computer model for two-phase fluid flow.

Consistent High-Dimensional Bayesian Variable Selection via Penalized Credible Regions

For high-dimensional data, particularly when the number of predictors greatly exceeds the sample size, selection of relevant predictors for regression is a challenging problem. Methods such as sure screening, forward selection, or penalized regressions are commonly used. Bayesian variable selection methods place prior distributions on the parameters along with a prior over model space, or equivalently, a mixture prior on the parameters having mass at zero. Since exhaustive enumeration is not feasible, posterior model probabilities are often obtained via long Markov chain Monte Carlo (MCMC) runs. The chosen model can depend heavily on various choices for priors and also posterior thresholds. Alternatively, we propose a conjugate prior only on the full model parameters and use sparse solutions within posterior credible regions to perform selection. These posterior credible regions often have closed-form representations, and it is shown that these sparse solutions can be computed via existing algorithms. The approach is shown to outperform common methods in the high-dimensional setting, particularly under correlation. By searching for a sparse solution within a joint credible region, consistent model selection is established. Furthermore, it is shown that, under certain conditions, the use of marginal credible intervals can give consistent selection up to the case where the dimension grows exponentially in the sample size. The proposed approach successfully accomplishes variable selection in the high-dimensional setting, while avoiding pitfalls that plague typical Bayesian variable selection methods.

Environmental, Institutional, and Demographic Predictors of Environmental Literacy among Middle School Children

Building environmental literacy (EL) in children and adolescents is critical to meeting current and emerging environmental challenges worldwide. Although environmental education (EE) efforts have begun to address this need, empirical research holistically evaluating drivers of EL is critical. This study begins to fill this gap with an examination of school-wide EE programs among middle schools in North Carolina, including the use of published EE curricula and time outdoors while controlling for teacher education level and experience, student attributes (age, gender, and ethnicity), and school attributes (socio-economic status, student-teacher ratio, and locale). Our sample included an EE group selected from schools with registered school-wide EE programs, and a control group randomly selected from NC middle schools that were not registered as EE schools. Students were given an EL survey at the beginning and end of the spring 2012 semester. Use of published EE curricula, time outdoors, and having teachers with advanced degrees and mid-level teaching experience (between 3 and 5 years) were positively related with EL whereas minority status (Hispanic and black) was negatively related with EL. Results suggest that school-wide EE programs were not associated with improved EL, but the use of published EE curricula paired with time outdoors represents a strategy that may improve all key components of student EL. Further, investments in teacher development and efforts to maintain enthusiasm for EE among teachers with more than 5 years of experience may help to boost student EL levels. Middle school represents a pivotal time for influencing EL, as improvement was slower among older students. Differences in EL levels based on gender suggest boys and girls may possess complementary skills sets when approaching environmental issues. Our findings suggest ethnicity related disparities in EL levels may be mitigated by time spent in nature, especially among black and Hispanic students.

RADIOGRAPHIC EVALUATION OF FELINE APPENDICULAR DEGENERATIVE JOINT DISEASE VS. MACROSCOPIC APPEARANCE OF ARTICULAR CARTILAGE

Degenerative joint disease (DJD) is common in domesticated cats. Our purpose was to describe how radiographic findings thought to indicate feline DJD relate to macroscopic cartilage degeneration in appendicular joints. Thirty adult cats euthanized for reasons unrelated to this study were evaluated. Orthogonal digital radiographs of the elbow, tarsus, stifle, and coxofemoral joints were evaluated for the presence of DJD. The same joints were dissected for visual inspection of changes indicative of DJD and macroscopic cartilage damage was graded using a Total Cartilage Damage Score. When considering all joints, there was statistically significant fair correlation between cartilage damage and the presence of osteophytes and joint-associated mineralizations, and the subjective radiographic DJD score. Most correlations were statistically significant when looking at the different joints individually, but only the correlation between the presence of osteophytes and the subjective radiographic DJD score with the presence of cartilage damage in the elbow and coxofemoral joints had a value above 0.4 (moderate correlation). The joints most likely to have cartilage damage without radiographic evidence of DJD are the stifle (71% of radiographically normal joints) followed by the coxofemoral joint (57%), elbow (57%), and tarsal joint (46%). Our data support radiographic findings not relating well to cartilage degeneration, and that other modalities should be evaluated to aid in making a diagnosis of feline DJD.