We propose a Bayesian model selection approach that allows medical practitioners to select among predictor variables while taking their respective costs into account. Medical procedures almost always ...incur costs in time and/or money. These costs might exceed their usefulness for modeling the outcome of interest. We develop Bayesian model selection that uses flexible model priors to penalize costly predictors a priori and select a subset of predictors useful relative to their costs. Our approach (i) gives the practitioner control over the magnitude of cost penalization, (ii) enables the prior to scale well with sample size, and (iii) enables the creation of our proposed inclusion path visualization, which can be used to make decisions about individual candidate predictors using both probabilistic and visual tools. We demonstrate the effectiveness of our inclusion path approach and the importance of being able to adjust the magnitude of the prior's cost penalization through a dataset pertaining to heart disease diagnosis in patients at the Cleveland Clinic Foundation, where several candidate predictors with various costs were recorded for patients, and through simulated data.
We propose a Bayesian model selection approach for generalized linear mixed models (GLMMs). We consider covariance structures for the random effects that are widely used in areas such as longitudinal ...studies, genome‐wide association studies, and spatial statistics. Since the random effects cannot be integrated out of GLMMs analytically, we approximate the integrated likelihood function using a pseudo‐likelihood approach. Our Bayesian approach assumes a flat prior for the fixed effects and includes both approximate reference prior and half‐Cauchy prior choices for the variances of random effects. Since the flat prior on the fixed effects is improper, we develop a fractional Bayes factor approach to obtain posterior probabilities of the several competing models. Simulation studies with Poisson GLMMs with spatial random effects and overdispersion random effects show that our approach performs favorably when compared to widely used competing Bayesian methods including deviance information criterion and Watanabe–Akaike information criterion. We illustrate the usefulness and flexibility of our approach with three case studies including a Poisson longitudinal model, a Poisson spatial model, and a logistic mixed model. Our proposed approach is implemented in the R package GLMMselect that is available on CRAN.
•Discrimination learning was assessed during three conditions of opioid exposure.•Learning was not affected by self-administered oxycodone.•Learning was impaired during withdrawal via naltrexone and ...abrupt discontinuation.•Impairment of learning was concordant with autonomic signs of opioid withdrawal.
Prescription opioid abuse continues to be a public health concern of epidemic proportions. Notwithstanding the extensive literature regarding opioid action, there has been little systematic research regarding the effects of opioid dependence and withdrawal on aspects of cognition-related behavior in laboratory animals. The present studies examined the effects of the prescription opioid oxycodone on learning processes in nonhuman primates.
The ability of subjects to repeatedly learn novel touchscreen-based visual discriminations was examined during three conditions of opioid exposure. Discrimination learning was examined, first, during oxycodone self-administration (3-hr sessions, 0.1 mg/kg/injection) and, next, during non-contingent chronic treatment with oxycodone (10 mg/kg/day). Finally, discrimination learning was re-examined during antagonist-precipitated opioid withdrawal (0.001-0.1 mg/kg naltrexone) and, subsequently, following abrupt discontinuation of oxycodone treatment.
Although motoric behavior was disrupted by oxycodone, neither the development of discrimination learning nor steady-state performance were impaired following oxycodone self-administration or during non-contingent chronic oxycodone treatment. However, discrimination learning was substantially impaired during oxycodone withdrawal, whether elicited by naltrexone or by abrupt oxycodone discontinuation. Moreover, these learning impairments were concordant with autonomic signs of opioid withdrawal.
Taken together, the present studies indicate that impairment of learning processes can accompany the unconditioned signs of opioid withdrawal.
Background
Given the conflicting nature of reported risk factors for post-discharge venous thromboembolism (VTE) and unclear guidelines for post-discharge pharmacoprophylaxis, we sought to determine ...risk factors for 30-day post-discharge VTE after colectomy to predict which patients will benefit from post-discharge pharmacoprophylaxis.
Methods
Patients who underwent colectomy in the American College of Surgeons National Surgical Quality Improvement Project Participant Use Files from 2011 to 2015 were identified. Logistic regression modeling was used. Receiver-operating characteristic curves were used and the best cut-points were determined using Youden’s J index (sensitivity + specificity − 1). Hosmer–Lemeshow goodness-of-fit test was used to test model calibration. A random sample of 30% of the cohort was used as a validation set.
Results
Among 77,823 cases, the overall incidence of VTE after colectomy was 1.9%, with 0.7% of VTE events occurring in the post-discharge setting. Factors associated with post-discharge VTE risk including body mass index, preoperative albumin, operation time, hospital length of stay, race, smoking status, inflammatory bowel disease, return to the operating room and postoperative ileus were included in logistic regression equation model. The model demonstrated good calibration (goodness of fit
P
= 0.7137) and good discrimination (area under the curve (AUC) = 0.68; validation set, AUC = 0.70). A score of ≥−5.00 had the maxim sensitivity and specificity, resulting in 36.63% of patients being treated with prophylaxis for an overall VTE risk of 0.67%.
Conclusion
Approximately one-third of post-colectomy VTE events occurred after discharge. Patients with predicted post-discharge VTE risk of ≥−5.00 should be recommended for extended post-discharge VTE prophylaxis.
Fast algorithms are developed for Bayesian analysis of Gaussian hierarchical models with intrinsic conditional autoregressive (ICAR) spatial random effects. To achieve computational speed-ups, first ...a result is proved on the equivalence between the use of an improper CAR prior with centering on the fly and the use of a sum-zero constrained ICAR prior. This equivalence result then provides the key insight for the algorithms, which are based on rewriting the hierarchical model in the spectral domain. The two novel algorithms are the Spectral Gibbs Sampler (SGS) and the Spectral Posterior Maximizer (SPM). Both algorithms are based on one single matrix spectral decomposition computation. After this computation, the SGS and SPM algorithms scale linearly with the sample size. The SGS algorithm is preferable for smaller sample sizes, whereas the SPM algorithm is preferable for sample sizes large enough for asymptotic calculations to provide good approximations. Because the matrix spectral decomposition needs to be computed only once, the SPM algorithm has computational advantages over algorithms based on sparse matrix factorizations (which need to be computed for each value of the random effects variance parameter) in situations when many models need to be fitted. Three simulation studies are performed: the first simulation study shows improved performance in computational speed in estimation of the SGS algorithm compared to an algorithm that uses the spectral decomposition of the precision matrix; the second simulation study shows that for model selection computations with 10 regressors and sample sizes varying from 49 to 3600, when compared to the current fastest state-of-the-art algorithm implemented in the R package INLA, SPM computations are 550 to 1825 times faster; the third simulation study shows that, when compared to default INLA settings, SGS and SPM combined with reference priors provide much more adequate uncertainty quantification. Finally, the application of the novel SGS and SPM algorithms is illustrated with a spatial regression study of county-level median household income for 3108 counties in the contiguous United States in 2017.
Abstract only
Over the past decade, prescription opioid abuse has become a major public health concern. Despite increased prevalence, however, the effects of chronic opioid abuse on cognition‐related ...behavior remains poorly understood. Previous studies in our laboratory have shown that self‐administration of other abused drugs can have adverse effects on cognitive behavior. Therefore, the present studies employed similar techniques to examine the effects of daily intravenous self‐administered oxycodone. Touchscreen‐based repeated acquisition and discrimination reversal tasks were designed to assay basic features relevant to learning and cognitive flexibility, respectively. Four squirrel monkeys were initially trained to self‐administer oxycodone. A range of doses was evaluated in each subject over 1, 2, and 3‐hour sessions until stable intake was observed. A peak dose of 0.1 mg/kg/inj during 3‐hr sessions yielded maximum daily intake and was used for subsequent conditions. Following 30 self‐administration sessions, subjects were then introduced to the touchscreen tasks. Subjects were placed in a touchscreen chamber immediately following oxycodone self‐administration and learned to discriminate between two novel stimuli (acquisition) for a palatable food reward. Once discrimination acquisition was mastered, subjects then re‐learned the discrimination under conditions where the consequences were switched (reversal). Dose‐response functions revealed orderly increases in oxycodone intake as a function of dose. In addition, extending session durations increased daily intake at all doses examined. Stable levels of oxycodone intake using the peak dose of 0.1 mg/kg/inj were maintained over extended periods of self‐administration (>100 sessions). Results from touchscreen sessions indicated dramatic deleterious effects on learning and reversal in some subjects; however, in other subjects less profound effects were observed. Future studies will investigate the effects of abrupt discontinuation of opioid self‐administration (withdrawal) on discrimination learning and reversal.
Support or Funding Information
This research was supported by grant K01‐DA035974 (BDK) from the National Institute on Drug Abuse.
We developed the Athlete Fear Avoidance Questionnaire (AFAQ) to measure fear avoidance in athletes. Previous fear avoidance scales were developed for the general population and have demonstrated ...significant predictive capabilities regarding rehabilitation. No research to date has examined the association between athlete fear avoidance as measured by the AFAQ and the rehabilitation time in athletes.
Fifty-nine athletes who were injured during sport season participated in the study (40 males and 19 females). At injury onset, all participants completed self-report functional questionnaires. In addition, we measured multiple aspects of fear avoidance including athlete fear avoidance (AFAQ), kinesiophobia (TSK), and pain catastrophizing (PCS). Finally, we assessed pain severity and interference, as well as depression. Once the athletes were able to return to competition all participants answered the questionnaires again. Pearson correlations and a regression analysis were used to identify relationships between function, psychological variables, pain, and return to competition time.
The AFAQ yielded the strongest correlation with return to competition time (
=0.544,
<0.001). In addition, function at initial injury time and pain interference were also significantly correlated with return to competition time (
=0.442,
<0.001 and
=0.356,
=0.006 respectively). Athlete fear-avoidance combined with function at the time of injury explained 34% of the variance of return to competition time in the multivariate regression model (
<0.001).
Athlete fear-avoidance as measured by the AFAQ is associated with rehabilitation time and returning to competition in injured athletes. Psychosocial factors including athlete fear avoidance may explain why some athletes take longer to rehabilitate than others and should be evaluated in athletes who are taking longer than anticipated to complete their rehabilitation. Reducing athlete fear avoidance may facilitate rehabilitation in future studies.
Leaf traits are frequently measured in ecology to provide a 'common currency' for predicting how anthropogenic pressures impact ecosystem function. Here, we test whether leaf traits consistently ...respond to experimental treatments across 27 globally distributed grassland sites across 4 continents. We find that specific leaf area (leaf area per unit mass)-a commonly measured morphological trait inferring shifts between plant growth strategies-did not respond to up to four years of soil nutrient additions. Leaf nitrogen, phosphorus and potassium concentrations increased in response to the addition of each respective soil nutrient. We found few significant changes in leaf traits when vertebrate herbivores were excluded in the short-term. Leaf nitrogen and potassium concentrations were positively correlated with species turnover, suggesting that interspecific trait variation was a significant predictor of leaf nitrogen and potassium, but not of leaf phosphorus concentration. Climatic conditions and pretreatment soil nutrient levels also accounted for significant amounts of variation in the leaf traits measured. Overall, we find that leaf morphological traits, such as specific leaf area, are not appropriate indicators of plant response to anthropogenic perturbations in grasslands.
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