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At least 1,045 records · Page 58Linked to original sources
PubMed2004

What determines compulsory community treatment? A logistic regression analysis using linked mental health and offender databases.

OBJECTIVE: Western Australia has one of the highest published rates of the use of compulsory treatment orders in the English-speaking world. Differences in patient characteristics, legislation and service setting may explain variations in the reported efficacy of compulsory community treatment. Our objective is to investigate predictors of Community Treatment Orders (CTO) placement in the first year of implementation in Western Australia and see if there were any differences in the type of patients placed on these orders compared to other studies. METHOD: A population-based record linkage study of Mental Health and Offender Databases comparing 265 patients on CTOs with a consecutive control group (CCG) of equal number matched on date of discharge from inpatient care or CTO placement. RESULTS: Previous health service use, after-care placement, mental disorder history including schizophrenic history, a positive forensic history of violence to others as well as patient's marital status were the significant predictors of CTO placement. CONCLUSIONS: Studies of compulsory community treatment appear to be of similar populations. In spite of the comparatively high rate of use, psychiatrists in Western Australia do not appear to be applying community treatment orders to different types of patient compared to elsewhere. We need further research to establish the relative contribution of patient characteristics, legislation and service setting toward the use and outcome of compulsory community treatment.

Adult↗
PubMed2006

Bayesian logistic regression using a perfect phylogeny.

Haplotype data capture the genetic variation among individuals in a population and among populations. An understanding of this variation and the ancestral history of haplotypes is important in genetic association studies of complex disease. We introduce a method for detecting associations between disease and haplotypes in a candidate gene region or candidate block with little or no recombination. A perfect phylogeny demonstrates the evolutionary relationship between single-nucleotide polymorphisms (SNPs) in the haplotype blocks. Our approach extends the logic regression technique of Ruczinski and others (2003) to a Bayesian framework, and constrains the model space to that of a perfect phylogeny. Environmental factors, as well as their interactions with SNPs, may be incorporated into the regression framework. We demonstrate our method on simulated data from a coalescent model, as well as data from a candidate gene study of sarcoidosis.

Bayes Theorem↗
PubMed1999

Multiple logistic regression analysis of plasma paraquat concentrations as a predictor of outcome in 375 cases of paraquat poisoning.

Successful prediction of who may survive paraquat poisoning can prevent inappropriately aggressive treatment in those who have little hope of survival and those only minimally poisoned. We examined case records of patients admitted to one poisoning treatment unit over the last 5 years, and the English and French language literature on paraquat poisoning. Data were recorded from all patients where outcome and timed plasma paraquat concentrations were present. Of 375 patients (113 M, 62 F, 200 unknown), mean age 38.3 years (range 1-87 years), 49 had evidence of renal toxicity, and 41 received haemodialysis or charcoal haemoperfusion; 61 developed pulmonary sequelae; and 44 had lesions in the upper gastrointestinal tract. Median time from ingestion to death in the 241 deaths reported was 270 h (range 3-720 h). We plotted log(plasma paraquat concentration) against log(h since ingestion). The predicted probability of survival for any specified time and concentration was exp(logit)/[1 + exp(logit)], where logit = 0.58-2.33 x log(plasma paraquat)-1.15 x log(h since ingestion). This equation may be helpful in predicting who will survive after ingestion of paraquat up to at least 200 h after ingestion, and can now be used as a research tool for studies on efficacy of treatment of paraquat poisoning.

Adolescent↗
PubMedDate not supplied

Multinomial logistic regression.

BACKGROUND: When the dependent variable consists of several categories that are not ordinal (i.e., they have no natural ordering), the ordinary least square estimator cannot be used. Instead, a maximum likelihood estimator like multinomial logit or probit should be used. OBJECTIVES: The purpose of this article is to understand the multinomial logit model (MLM) that uses maximum likelihood estimator and its application in nursing research. METHOD: The research on "Racial differences in use of long-term care received by the elderly" (Kwak, 2001) is used to illustrate the multinomial logit model approach. This method assumes that the data satisfy a critical assumption called the "independence of irrelevant alternatives." A diagnostic developed by Hausman is used to test the independence of irrelevant alternatives assumption. Models in which the dependent variable consists of several unordered categories can be estimated with the multinomial logit model, and these models can be easily interpreted. CONCLUSIONS: This method can handle situations with several categories. There is no need to limit the analysis to pairs of categories, or to collapse the categories into two mutually exclusive groups so that the (more familiar) logit model can be used. Indeed, any strategy that eliminates observations or combines categories only leads to less efficient estimates.

Aged↗
PubMed2005

From data to knowledge: a method for modeling hospital logistic processes.

When modeling or redesigning a process, the knowledge-management perspective is seldomly used. Using the knowledge categorization developed by van Heusden and Jorna, we propose a knowledge-management perspective to provide a strategy for modeling and redesigning a business process. As an illustration of our approach, we use hospital data of multidisciplinary patients. This specific group of patients requires the involvement of different specialisms for their medical treatment that leads to more efforts regarding the coordination of care for these patients. In order to increase the care efficiency, knowledge that supports the reorganization of care for multidisciplinary patients should be provided. We use the above-mentioned knowledge-management perspective for creating new multidisciplinary units, in which different specialisms coordinate the treatment of specific groups of patients.

Artificial Intelligence↗
PubMed2002

Group sequential methods for an ordinal logistic random-effects model under misspecification.

Interim analyses in clinical trials are planned for ethical as well as economic reasons. General results have been published in the literature that allow the use of standard group sequential methodology if one uses an efficient test statistic, e.g., when Wald-type statistics are used in random-effects models for ordinal longitudinal data. These models often assume that the random effects are normally distributed. However, this is not always the case. We will show that, when the random-effects distribution is misspecified in ordinal regression models, the joint distribution of the test statistics over the different interim analyses is still a multivariate normal distribution, but a sandwich-type correction to the covariance matrix is needed in order to obtain the correct covariance matrix. The independent increment structure is also investigated. A bias in estimation will occur due to the misspecification. However, we will also show that the treatment effect estimate will be unbiased under the null hypothesis, thus maintaining the type I error. Extensive simulations based on a toenail dermatophyte onychomycosis trial are used to illustrate our results.

Biometry↗
PubMed2004

Functional mapping of quantitative trait loci underlying growth trajectories using a transform-both-sides logistic model.

The incorporation of developmental control mechanisms of growth has proven to be a powerful tool in mapping quantitative trait loci (QTL) underlying growth trajectories. A theoretical framework for implementing a QTL mapping strategy with growth laws has been established. This framework can be generalized to an arbitrary number of time points, where growth is measured, and becomes computationally more tractable, when the assumption of variance stationarity is made. In practice, however, this assumption is likely to be violated for age-specific growth traits due to a scale effect. In this article, we present a new statistical model for mapping growth QTL, which also addresses the problem of variance stationarity, by using a transform-both-sides (TBS) model advocated by Carroll and Ruppert (1984, Journal of the American Statistical Association 79, 321-328). The TBS-based model for mapping growth QTL cannot only maintain the original biological properties of a growth model, but also can increase the accuracy and precision of parameter estimation and the power to detect a QTL responsible for growth differentiation. Using the TBS-based model, we successfully map a QTL governing growth trajectories to a linkage group in an example of forest trees. The statistical and biological properties of the estimates of this growth QTL position and effect are investigated using Monte Carlo simulation studies. The implications of our model for understanding the genetic architecture of growth are discussed.

Biometry↗