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Log Likelihood (log + likelihood)

Distribution by Scientific Domains

Medical Sciences	40%

Selected Abstracts

Clinical value of 12 occlusal features for the prediction of disc displacement with reduction (RDC/TMD Axis I group IIa)

JOURNAL OF ORAL REHABILITATION, Issue 5 2009
G. CHIAPPE
Summary, The purpose of this study is to quantify the clinical value of 12 occlusal variables for the prediction of disc displacement with reduction diagnosed according to research diagnostic criteria (RDC)/temporomandibular disorder (TMD). Twelve occlusal features were clinically assessed by the same three operators. The sample consisted of 165 TMD patients (65 males, 100 females; mean age: 32·55 ± 11·685years) with only disc displacement with reduction (RDC/TMD Axis I group IIa) and a control sample of 145 healthy subjects (65 males, 80 females; mean age: 31·24 ± 12·436 years) diagnosed with RDC/TMD Axis I group 0. A stepwise multiple logistic regression model was used to identify the significant correlation between occlusal features and disease. The odds ratio for disc displacement was 2·84 for absence of canine guidance, 2·14 for mediotrusive interference and 1·75 for retruded contact position (RCP)/maximum intercuspation (MI) slide ,2 mm. Other occlusal variables did not reveal to be statistically significant. The percentage of the total log likelihood for disc displacement explained by the significant occlusal factors was acceptable with a Nagelkerke's R2 = 0·124. The final model including the significant occlusal features revealed an optimal discriminant capacity to predict patients with disc displacement with a sensitivity of 63·6% or with a specificity of 64·8% for healthy subjects and an accuracy of 64·2%. Occlusal features showed a low predictive value for detecting disc displacement. Multifactorial complex pathologies such as TMD should be investigated using a multivariate statistical analysis; moreover, the future of aetiopathogenic research in this matter requires a multifactorial approach. [source]

Forecasting Models of Emergency Department Crowding

ACADEMIC EMERGENCY MEDICINE, Issue 4 2009
Lisa M. Schweigler MD
Abstract Objectives:, The authors investigated whether models using time series methods can generate accurate short-term forecasts of emergency department (ED) bed occupancy, using traditional historical averages models as comparison. Methods:, From July 2005 through June 2006, retrospective hourly ED bed occupancy values were collected from three tertiary care hospitals. Three models of ED bed occupancy were developed for each site: 1) hourly historical average, 2) seasonal autoregressive integrated moving average (ARIMA), and 3) sinusoidal with an autoregression (AR)-structured error term. Goodness of fits were compared using log likelihood and Akaike's Information Criterion (AIC). The accuracies of 4- and 12-hour forecasts were evaluated by comparing model forecasts to actual observed bed occupancy with root mean square (RMS) error. Sensitivity of prediction errors to model training time was evaluated, as well. Results:, The seasonal ARIMA outperformed the historical average in complexity adjusted goodness of fit (AIC). Both AR-based models had significantly better forecast accuracy for the 4- and the 12-hour forecasts of ED bed occupancy (analysis of variance [ANOVA] p < 0.01), compared to the historical average. The AR-based models did not differ significantly from each other in their performance. Model prediction errors did not show appreciable sensitivity to model training times greater than 7 days. Conclusions:, Both a sinusoidal model with AR-structured error term and a seasonal ARIMA model were found to robustly forecast ED bed occupancy 4 and 12 hours in advance at three different EDs, without needing data input beyond bed occupancy in the preceding hours. [source]

A Semiparametric Joint Model for Longitudinal and Survival Data with Application to Hemodialysis Study

BIOMETRICS, Issue 3 2009
Liang Li
Summary In many longitudinal clinical studies, the level and progression rate of repeatedly measured biomarkers on each subject quantify the severity of the disease and that subject's susceptibility to progression of the disease. It is of scientific and clinical interest to relate such quantities to a later time-to-event clinical endpoint such as patient survival. This is usually done with a shared parameter model. In such models, the longitudinal biomarker data and the survival outcome of each subject are assumed to be conditionally independent given subject-level severity or susceptibility (also called frailty in statistical terms). In this article, we study the case where the conditional distribution of longitudinal data is modeled by a linear mixed-effect model, and the conditional distribution of the survival data is given by a Cox proportional hazard model. We allow unknown regression coefficients and time-dependent covariates in both models. The proposed estimators are maximizers of an exact correction to the joint log likelihood with the frailties eliminated as nuisance parameters, an idea that originated from correction of covariate measurement error in measurement error models. The corrected joint log likelihood is shown to be asymptotically concave and leads to consistent and asymptotically normal estimators. Unlike most published methods for joint modeling, the proposed estimation procedure does not rely on distributional assumptions of the frailties. The proposed method was studied in simulations and applied to a data set from the Hemodialysis Study. [source]

A Multiple-Record Systems Estimation Method that Takes Observed and Unobserved Heterogeneity into Account

BIOMETRICS, Issue 2 2004
Elena Stanghellini
Summary. We present a model to estimate the size of an unknown population from a number of lists that applies when the assumptions of (a) homogeneity of capture probabilities of individuals and (b) marginal independence of lists are violated. This situation typically occurs in epidemiological studies, where the heterogeneity of individuals is severe and researchers cannot control the independence between sources of ascertainment. We discuss the situation when categorical covariates are available and the interest is not only in the total undercount, but also in the undercount within each stratum resulting from the cross-classification of the covariates. We also present several techniques for determining confidence intervals of the undercount within each stratum using the profile log likelihood, thereby extending the work of Cormack (1992, Biometrics48, 567,576). [source]

Estimation and Inference for a Spline-Enhanced Population Pharmacokinetic Model

BIOMETRICS, Issue 3 2002
Lang Li
Summary. This article is motivated by an application where subjects were dosed three times with the same drug and the drug concentration profiles appeared to be the lowest after the third dose. One possible explanation is that the pharmacokinetic (PK) parameters vary over time. Therefore, we consider population PK models with time-varying PK parameters. These time-varying PK parameters are modeled by natural cubic spline functions in the ordinary differential equations. Mean parameters, variance components, and smoothing parameters are jointly estimated by maximizing the double penalized log likelihood. Mean functions and their derivatives are obtained by the numerical solution of ordinary differential equations. The interpretation of PK parameters in the model and its flexibility are discussed. The proposed methods are illustrated by application to the data that motivated this article. The model's performance is evaluated through simulation. [source]