Evaluation of Bayesian Hui-Walter and logistic regression latent class models to estimate diagnostic test characteristics with simulated data.

Estimation of the accuracy of diagnostic tests in the absence of a gold standard is an important research subject in epidemiology (Dohoo et al., 2009). One of the most used methods the last few decades is the Bayesian Hui-Walter (HW) latent class model (Hui and Walter, 1980). However, the classic HW models aggregate the observed individual test results to the population level, and as a result, potentially valuable information from the lower level(s) is not fully incorporated. An alternative approach is the Bayesian logistic regression (LR) latent class model that allows inclusion of individual level covariates (McInturff et al., 2004). In this study, we explored both classic HW and individual level LR latent class models using Bayesian methodology within a simulation context where true disease status and true test properties were predefined. Population prevalences and test characteristics that were realistic for paratuberculosis in cattle (Toft et al., 2005) were used for the simulation. Individual animals were generated to be clustered within herds in two regions. Two tests with binary outcomes were simulated with constant test characteristics across the two regions. On top of the prevalence properties and test characteristics, one animal level binary risk factor was added to the data. The main objective was to compare the performance of Bayesian HW and LR approaches in estimating test sensitivity and specificity in simulated datasets with different population characteristics. Results from various settings showed that LR models provided posterior estimates that were closer to the true values. The LR models that incorporated herd level clustering effects provided the most accurate estimates, in terms of being closest to the true values and having smaller estimation intervals. This work illustrates that individual level LR models are in many situations preferable over classic HW models for estimation of test characteristics in the absence of a gold standard.

Expert opinion as priors for random effects in Bayesian prediction models: Subclinical ketosis in dairy cows as an example.

Random effects regression models are routinely used for clustered data in etiological and intervention research. However, in prediction models, the random effects are either neglected or conventionally substituted with zero for new clusters after model development. In this study, we applied a Bayesian prediction modelling method to the subclinical ketosis data previously collected by Van der Drift et al. (2012). Using a dataset of 118 randomly selected Dutch dairy farms participating in a regular milk recording system, the authors proposed a prediction model with milk measures as well as available test-day information as predictors for the diagnosis of subclinical ketosis in dairy cows. While their original model included random effects to correct for the clustering, the random effect term was removed for their final prediction model. With the Bayesian prediction modelling approach, we first used non-informative priors for the random effects for model development as well as for prediction. This approach was evaluated by comparing it to the original frequentist model. In addition, herd level expert opinion was elicited from a bovine health specialist using three different scales of precision and incorporated in the prediction as informative priors for the random effects, resulting in three more Bayesian prediction models. Results showed that the Bayesian approach could naturally take the clustering structure of clusters into account by keeping the random effects in the prediction model. Expert opinion could be explicitly combined with individual level data for prediction. However in this dataset, when elicited expert opinion was incorporated, little improvement was seen at the individual level as well as at the herd level. When the prediction models were applied to the 118 herds, at the individual cow level, with the original frequentist approach we obtained a sensitivity of 82.4% and a specificity of 83.8% at the optimal cutoff, while with the three Bayesian models with elicited expert opinion, we obtained sensitivities ranged from 78.7% to 84.6% and specificities ranged from 75.0% to 83.6%. At the herd level, 30 out of 118 within herd prevalences were correctly predicted by the original frequentist approach, and 31 to 44 herds were correctly predicted by the three Bayesian models with elicited expert opinion. Further investigation in expert opinion and distributional assumption for the random effects was carried out and discussed.

Prediction models for clustered data with informative priors for the random effects: a simulation study.

BACKGROUND: Random effects modelling is routinely used in clustered data, but for prediction models, random effects are commonly substituted with the mean zero after model development. In this study, we proposed a novel approach of including prior knowledge through the random effects distribution and investigated to what extent this could improve the predictive performance. METHODS: Data were simulated on the basis of a random effects logistic regression model. Five prediction models were specified: a frequentist model that set the random effects to zero for all new clusters, a Bayesian model with weakly informative priors for the random effects of new clusters, Bayesian models with expert opinion incorporated into low informative, medium informative and highly informative priors for the random effects. Expert opinion at the cluster level was elicited in the form of a truncated area of the random effects distribution. The predictive performance of the five models was assessed. In addition, impact of suboptimal expert opinion that deviated from the true quantity as well as including expert opinion by means of a categorical variable in the frequentist approach were explored. The five models were further investigated in various sensitivity analyses. RESULTS: The Bayesian prediction model using weakly informative priors for the random effects showed similar results to the frequentist model. Bayesian prediction models using expert opinion as informative priors showed smaller Brier scores, better overall discrimination and calibration, as well as better within cluster calibration. Results also indicated that incorporation of more precise expert opinion led to better predictions. Predictive performance from the frequentist models with expert opinion incorporated as categorical variable showed similar patterns as the Bayesian models with informative priors. When suboptimal expert opinion was used as prior information, results indicated that prediction still improved in certain settings. CONCLUSIONS: The prediction models that incorporated cluster level information showed better performance than the models that did not. The Bayesian prediction models we proposed, with cluster specific expert opinion incorporated as priors for the random effects showed better predictive ability in new data, compared to the frequentist method that replaced random effects with zero after model development.

Judgement bias in pigs is independent of performance in a spatial holeboard task and conditional discrimination learning.

Biases in judgement of ambiguous stimuli, as measured in a judgement bias task, have been proposed as a measure of the valence of affective states in animals. We recently suggested a list of criteria for behavioural tests of emotion, one of them stating that responses on the task used to assess emotionality should not be confounded by, among others, differences in learning capacity, i.e. must not simply reflect the cognitive capacity of an animal. We performed three independent studies in which pigs acquired a spatial holeboard task, a free choice maze which simultaneously assesses working memory and reference memory. Next, pigs learned a conditional discrimination between auditory stimuli predicting a large or small reward, a prerequisite for assessment of judgement bias. Once pigs had acquired the conditional discrimination task, optimistic responses to previously unheard ambiguous stimuli were measured in the judgement bias task as choices indicating expectation of the large reward. We found that optimism in the judgement bias task was independent of all three measures of learning and memory indicating that the performance is not dependent on the pig's cognitive abilities. These results support the use of biases in judgement as proxy indicators of emotional valence in animals.

Crystalloid Coload Reduced the Incidence of Hypotension in Spinal Anesthesia for Cesarean Delivery, When Compared to Crystalloid Preload: A Meta-Analysis.

OBJECTIVE: To determine whether crystalloid infusion just after intrathecal injection (coload) would be better than infusion before anesthesia (preload) for hypotension prophylaxis in spinal anesthesia for cesarean delivery. METHODS: We searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials, and other databases for randomized controlled trials comparing coload of crystalloid with preload in parturients receiving spinal anesthesia for cesarean delivery. Primary outcome was intraoperative incidence of hypotension. Other outcomes were intraoperative need for vasopressors, hemodynamic variables, neonatal outcomes (umbilical artery pH and Apgar scores), and the incidence of maternal nausea and vomiting. We used RevMan 5.2 and STATA 12.0 for the data analyses. RESULTS: Ten studies with 824 cases were included. The incidence of hypotension was significantly higher in the preload group compared with the coload group (57.8% versus 47.1%, odds ratio [OR] = 1.62, 95% confidence interval [CI] = 1.11-2.37, and P = 0.01). More patients needed intraoperative vasopressors (OR = 1.71, 95% CI = 1.07-2.04, and P = 0.02) when receiving crystalloid preload. In addition, the incidence of nausea and vomiting was higher in the preload group (OR = 3.40, 95% CI = 1.88-6.16, and P < 0.0001). There were no differences in neonatal outcomes between the groups. CONCLUSIONS: For parturients receiving crystalloid loading in spinal anesthesia for cesarean delivery, coload strategy is superior to preload for the prevention of maternal hypotension.

Hydrothermal synthesis of graphene/nickel oxide nanocomposites used as the electrode for supercapacitors.

Graphene (GR)-based nanocomposites with different mass ratios of NiO and GR are prepared via hydrothermal method using Ni(NO3)2 as the origin of nickel and urea as the hydrolysis-controlling agent. The morphology and electrochemical performance of the GR/NiO nanocomposites are closely associated with the mass ratios of GR to NiO. The chemical composition and morphology of the composites together with the pure GR and NiO are characterized by thermogravimetric analysis (TGA), X-ray diffraction (XRD), scanning electron microscope (SEM), and transmission electron microscope (TEM). It is found that the GR sheets and NiO particles form uniform nanocomposites with the NiO particles absorbed on the GR surface. A specific capacitance of 384 F g(-1) at a current density of 0.1 A g(-1) is achieved when the coating amount of NiO is up to 74 wt%. In addition, the attenuation of the specific capacitance is less than 6% after 500 cycles, indicating such nanocomposite has excellent cycling performance.

Carbon-coated LiFePO4-porous carbon composites as cathode materials for lithium ion batteries.

This work introduces a facile strategy for the synthesis of carbon-coated LiFePO(4)-porous carbon (C-LiFePO(4)-PC) composites as a cathode material for lithium ion batteries. The LiFePO(4) particles obtained are about 200 nm in size and homogeneously dispersed in porous carbon matrix. These particles are further coated with the carbon layers pyrolyzed from sucrose. The C-LiFePO(4)-PC composites display a high initial discharge capacity of 152.3 mA h g(-1) at 0.1 C, good cycling stability, as well as excellent rate capability (112 mA h g(-1) at 5 C). The likely contributing factors to the excellent electrochemical performance of the C-LiFePO(4)-PC composites could be related to the combined effects of enhancement of conductivity by the porous carbon matrix and the carbon coating layers. It is believed that further carbon coating is a facile and effective way to improve the electrochemical performance of LiFePO(4)-PC.