Search results for "covariate"

Goodness-of-fit tests for parametric excess hazard rate models with covariates

2017

In this paper we propose a general methodology for testing the null hypothesis that an excess hazard rate model, with or without covariates, belongs to a parametric family. Estimating the excess hazard rate function parametrically through the maximum likelihood method and non-parametrically (or semi-parametrically) we build a discrepancy process which is shown to be asymptotically Gaussian under the null hypothesis. Based on this result we are able to build some statistical tests in order to decide wether or not the null hypothesis is acceptable. We illustrate our results by the construction of chi-square tests which the behavior is studied through a Monte-Carlo study. Then the testing proc…

Examining the association between exposome score for schizophrenia and functioning in schizophrenia, siblings, and healthy controls: results from the…

2021

European Community's Seventh Framework Program, European Commission [HEALTH-F2-2009-241909]; Scientific and Technological Research Council of Turkey, 2219 International Postdoctoral Research Fellowship Program; Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, and PI19/024)...

SART and Individual Trial Mistake Thresholds: Predictive Model for Mobility Decline

2021

The Sustained Attention to Response Task (SART) has been used to measure neurocognitive functions in older adults. However, simplified average features of this complex dataset may result in loss of primary information and fail to express associations between test performance and clinically meaningful outcomes. Here, we describe a new method to visualise individual trial (raw) information obtained from the SART test, vis-à-vis age, and groups based on mobility status in a large population-based study of ageing in Ireland. A thresholding method, based on the individual trial number of mistakes, was employed to better visualise poorer SART performances, and was statistically validated with bin…

A space-time branching process with covariates

2018

The paper proposes a stochastic process that improves the assessment of seismic events in space and time, considering a contagion model (branching process) within a regression-like framework. The proposed approach develops the Forward Likelihood for prediction (FLP) method including covariates in the epidemic component.

Predicting the age at natural menopause in middle-aged women

2021

Objective To predict the age at natural menopause (ANM). Methods Cox models with time-dependent covariates were utilized for ANM prediction using longitudinal data from 47 to 55-year-old women (n = 279) participating in the Estrogenic Regulation of Muscle Apoptosis study. The ANM was assessed retrospectively for 105 women using bleeding diaries. The predictors were chosen from the set of 32 covariates by using the lasso regression (model 1). Another easy-to-access model (model 2) was created by using a subset of 16 self-reported covariates. The predictive performance was quantified with c-indices and by studying the means and standard deviations of absolute errors (MAE ± SD) between the pre…

Efficient design and modeling strategies for follow-up studies with time-varying covariates

2015

Epidemiological studies can often be designed in several ways, some of which may be more optimal than others. Possible designs may differ in the required resources or the ability to provide reliable answers to the questions under study. In addition, once the data are collected, the selected modeling approach may affect how efficiently the data are utilized. The purpose of this dissertation is to investigate efficient designs and analysis meth ods in follow-up studies with longitudinal measurements. A key question is how to select optimally a subcohort for a new longitudinal covariate measurement if we cannot afford to measure the entire cohort. Another key question we consider is how to determine …

Multilevel Latent Profile Analysis With Covariates : Identifying Job Characteristics Profiles in Hierarchical Data as an Example

2018

Latent profile analysis (LPA) is a person-centered method commonly used in organizational research to identify homogeneous subpopulations of employees within a heterogeneous population. However, in the case of nested data structures, such as employees nested in work departments, multilevel techniques are needed. Multilevel LPA (MLPA) enables adequate modeling of subpopulations in hierarchical data sets. MLPA enables investigation of variability in the proportions of Level 1 profiles across Level 2 units, and of Level 2 latent classes based on the proportions of Level 1 latent profiles and Level 1 ratings, and the extent to which covariates drawn from the different hierarchical levels of th…

Integreating geological and seismological data in point process models for seismical analysis

2017

Nowadays in the seismic and geological fields, large and complex data sets are available. This information is a valuable source that can be used for improving the seismic hazard assessment of a given region. In particular, the integration of geologic variables into point process models to study seismic pattern is an open research field that has not been fully explored. In this work, we present several open-access datasets (the catalogue of the earthquakes, geological information such as faults, plate boundary and the presence of volcanoes) that are properly treated to describe the seismicity of events occurred in Greece between 2005 and 2014. We use these datasets to fit an advanced spatial…

Models and methods for space and space-time interactions in complex point processes with applications on earthquakes

How many longitudinal covariate measurements are needed for risk prediction?

2014

Abstract Objective In epidemiologic follow-up studies, many key covariates, such as smoking, use of medication, blood pressure, and cholesterol, are time varying. Because of practical and financial limitations, time-varying covariates cannot be measured continuously, but only at certain prespecified time points. We study how the number of these longitudinal measurements can be chosen cost-efficiently by evaluating the usefulness of the measurements for risk prediction. Study Design and Setting The usefulness is addressed by measuring the improvement in model discrimination between models using different amounts of longitudinal information. We use simulated follow-up data and the data from t…