Search results for "Covariate"
showing 10 items of 110 documents
The Happy-Productive Worker Model and Beyond: Patterns of Wellbeing and Performance at Work
2019
According to the happy-productive worker thesis (HPWT), &ldquo
Effects of Visually Induced Motion Sickness on Emergency Braking Reaction Times in a Driving Simulator
2019
Objective: The study explores associations of visually induced motion sickness (VIMS) with emergency braking reaction times (RTs) in driving simulator studies. It examines the effects over the progression of multiple simulated drives. Background: Driving simulator usage has many advantages for RT studies; however, if it induces VIMS, the observed driving behavior might deviate from real-world driving, potentially masking or skewing results. Possible effects of VIMS on RT have long been entertained, but the progression of VIMS across simulated drives has so far not been sufficiently considered. Method: Twenty-eight adults completed six drives on 2 days in a fixed-base driving simulator. At f…
Method effects associated with negatively and positively worded items on the 12-item General Health Questionnaire (GHQ-12): results from a cross-sect…
2019
This study focused on the examination of the latent structure underlying the responses to the GHQ-12, considering the role of method effects associated with both, PW and NW items, and using two alternative parameterisations of the CFA measurement models. What should first be noted is that the studies that have included method effects in the measurement model of the GHQ-12 have been more the exception than the rule in previous research into the factor structure of this questionnaire. According to the results of the present study, we conclude that the GHQ-12 factor structure is best characterised by introducing latent method factors that capture both the method effects associated with NW and …
Optimal selection of individuals for repeated covariate measurements in follow-up studies
2016
Repeated covariate measurements bring important information on the time-varying risk factors in long epidemiological follow-up studies. However, due to budget limitations, it may be possible to carry out the repeated measurements only for a subset of the cohort. We study cost-efficient alternatives for the simple random sampling in the selection of the individuals to be remeasured. The proposed selection criteria are based on forms of the D-optimality. The selection methods are compared with the simulation studies and illustrated with the data from the East–West study carried out in Finland from 1959 to 1999. The results indicate that cost savings can be achieved if the selection is focuse…
A practical approach to improve the statistical performance of surface water monitoring networks
2019
The representativeness of aquatic ecosystem monitoring and the precision of the assessment results are of high importance when implementing the EU’s Water Framework Directive that aims to secure a good status of waterbodies in Europe. However, adapting monitoring designs to answer the objectives and allocating the sampling resources effectively are seldom practiced. Here, we present a practical solution how the sampling effort could be re-allocated without decreasing the precision and confidence of status class assignment. For demonstrating this, we used a large data set of 272 intensively monitored Finnish lake, coastal, and river waterbodies utilizing an existing framework for quantifying…
Adaptive trial design: a general methodology for censored time to event data.
2008
Adaptive designs allow a clinical trial design to be changed according to interim findings without inflating type I error. The Inverse Normal method can be considered as an adaptive generalization of classical group sequential designs. The use of the Inverse Normal method for censored survival data was demonstrated only for the logrank statistic. However, the logrank statistic is inefficient in the presence of nuisance covariates affecting survival. We demonstrate, how the Inverse Normal method can be applied to Cox regression analysis. The required independence between test statistics of the different stages of the trial can be obtained by two different approaches. One is using the indepen…
Regularized Regression Incorporating Network Information: Simultaneous Estimation of Covariate Coefficients and Connection Signs
2014
We develop an algorithm that incorporates network information into regression settings. It simultaneously estimates the covariate coefficients and the signs of the network connections (i.e. whether the connections are of an activating or of a repressing type). For the coefficient estimation steps an additional penalty is set on top of the lasso penalty, similarly to Li and Li (2008). We develop a fast implementation for the new method based on coordinate descent. Furthermore, we show how the new methods can be applied to time-to-event data. The new method yields good results in simulation studies concerning sensitivity and specificity of non-zero covariate coefficients, estimation of networ…
Does Sedentary Behavior Predict Academic Performance in Adolescents or the Other Way Round? A Longitudinal Path Analysis.
2016
This study examined whether adolescents’ time spent on sedentary behaviors (academic, technological-based and social-based activities) was a better predictor of academic performance than the reverse. A cohort of 755 adolescents participated in a three-year period study. Structural Equation Modeling techniques were used to test plausible causal hypotheses. Four competing models were analyzed to determine which model best fitted the data. The Best Model was separately tested by gender. The Best Model showed that academic performance was a better predictor of sedentary behaviors than the other way round. It also indicated that students who obtained excellent academic results were more likely t…
A Bayesian unified framework for risk estimation and cluster identification in small area health data analysis.
2020
Many statistical models have been proposed to analyse small area disease data with the aim of describing spatial variation in disease risk. In this paper, we propose a Bayesian hierarchical model that simultaneously allows for risk estimation and cluster identification. Our model formulation assumes that there is an unknown number of risk classes and small areas are assigned to a risk class by means of independent allocation variables. Therefore, areas within each cluster are assumed to share a common risk but they may be geographically separated. The posterior distribution of the parameter representing the number of risk classes is estimated using a novel procedure that combines its prior …
Statistical Methods for the Geographical Analysis of Rare Diseases
2010
In this chapter we provide a summary of different methods for the detection of disease clusters. First of all, we give a summary of methods for computing estimates of the relative risk. These estimates provide smoothed values of the relative risks that can account for its spatial variation. Some methods for assessing spatial autocorrelation and general clustering are also discussed to test for significant spatial variation of the risk. In order to find the actual location of the clusters, scan methods are introduced. The spatial scan statistic is discussed as well as its extension by means of Generalised Linear Models that allows for the inclusion of covariates and cluster effects. In this …