10,389 research outputs found
The Association Between Rate and Severity of Exacerbations in Chronic Obstructive Pulmonary Disease: An Application of a Joint Frailty-Logistic Model.
Exacerbations are a hallmark of chronic obstructive pulmonary disease (COPD). Evidence suggests the presence of substantial between-individual variability (heterogeneity) in exacerbation rates. The question of whether individuals vary in their tendency towards experiencing severe (versus mild) exacerbations, or whether there is an association between exacerbation rate and severity, has not yet been studied. We used data from the MACRO Study, a 1-year randomized trial of the use of azithromycin for prevention of COPD exacerbations (United States and Canada, 2006-2010; n = 1,107, mean age = 65.2 years, 59.1% male). A parametric frailty model was combined with a logistic regression model, with bivariate random effects capturing heterogeneity in rate and severity. The average rate of exacerbation was 1.53 episodes/year, with 95% of subjects having a model-estimated rate of 0.47-4.22 episodes/year. The overall ratio of severe exacerbations to total exacerbations was 0.22, with 95% of subjects having a model-estimated ratio of 0.04-0.60. We did not confirm an association between exacerbation rate and severity (P = 0.099). A unified model, implemented in standard software, could estimate joint heterogeneity in COPD exacerbation rate and severity and can have applications in similar contexts where inference on event time and intensity is considered. We provide SAS code (SAS Institute, Inc., Cary, North Carolina) and a simulated data set to facilitate further uses of this method
The Five Factor Model of personality and evaluation of drug consumption risk
The problem of evaluating an individual's risk of drug consumption and misuse
is highly important. An online survey methodology was employed to collect data
including Big Five personality traits (NEO-FFI-R), impulsivity (BIS-11),
sensation seeking (ImpSS), and demographic information. The data set contained
information on the consumption of 18 central nervous system psychoactive drugs.
Correlation analysis demonstrated the existence of groups of drugs with
strongly correlated consumption patterns. Three correlation pleiades were
identified, named by the central drug in the pleiade: ecstasy, heroin, and
benzodiazepines pleiades. An exhaustive search was performed to select the most
effective subset of input features and data mining methods to classify users
and non-users for each drug and pleiad. A number of classification methods were
employed (decision tree, random forest, -nearest neighbors, linear
discriminant analysis, Gaussian mixture, probability density function
estimation, logistic regression and na{\"i}ve Bayes) and the most effective
classifier was selected for each drug. The quality of classification was
surprisingly high with sensitivity and specificity (evaluated by leave-one-out
cross-validation) being greater than 70\% for almost all classification tasks.
The best results with sensitivity and specificity being greater than 75\% were
achieved for cannabis, crack, ecstasy, legal highs, LSD, and volatile substance
abuse (VSA).Comment: Significantly extended report with 67 pages, 27 tables, 21 figure
Modelling occupants' personal characteristics for thermal comfort prediction
Based on results from a field survey campaign conducted in Switzerand, we show that occupants' variations in clothing choices, which are relatively unconstrained, are best described by the daily mean outdoor temperature and that major clothing adjustments occur rarely during the day. We then develop an ordinal logistic model of the probability distribution of discretised clothing levels, which results in a concise and informative expression of occupants' clothing choices. Results from both cross-validation and independent verification suggest that this model formulation may be used with confidence. Furthermore, the form of the model is readily generalisable, given the requisite calibration data, to environments where dress codes are more specific. We also observe that, for these building occupants, the prevailing metabolic activity levels are mostly constant for the whole range of surveyed environmental conditions, as their activities are relatively constrained by the tasks in hand. Occupants may compensate for this constraint, however, through the consumption of cold and hot drinks, with corresponding impacts on metabolic heat production. Indeed, cold drink consumption was found to be highly correlated with indoor thermal conditions, whilst hot drink consumption is best described by a seasonal variable. These variables can be used for predictive purposes using binary logistic model
Bayesian Item Response Modeling in R with brms and Stan
Item Response Theory (IRT) is widely applied in the human sciences to model
persons' responses on a set of items measuring one or more latent constructs.
While several R packages have been developed that implement IRT models, they
tend to be restricted to respective prespecified classes of models. Further,
most implementations are frequentist while the availability of Bayesian methods
remains comparably limited. We demonstrate how to use the R package brms
together with the probabilistic programming language Stan to specify and fit a
wide range of Bayesian IRT models using flexible and intuitive multilevel
formula syntax. Further, item and person parameters can be related in both a
linear or non-linear manner. Various distributions for categorical, ordinal,
and continuous responses are supported. Users may even define their own custom
response distribution for use in the presented framework. Common IRT model
classes that can be specified natively in the presented framework include 1PL
and 2PL logistic models optionally also containing guessing parameters, graded
response and partial credit ordinal models, as well as drift diffusion models
of response times coupled with binary decisions. Posterior distributions of
item and person parameters can be conveniently extracted and post-processed.
Model fit can be evaluated and compared using Bayes factors and efficient
cross-validation procedures.Comment: 54 pages, 16 figures, 3 table
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