Search results for "covariate"
showing 10 items of 110 documents
Including Covariates in the ETAS Model Triggered Seismicity
2020
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 to take covariates into account. The proposed approach develops the Forward Likelihood for prediction (FLP) method for estimating the ETAS model, including covariates in the model specification of the epidemic component. A simulation study is carried out for analysing the misspecification model effect under several scenarios. Also an application to the Italian catalogue is reported, together with the reference to the developed R package.
SEQUENTIALLY ADJUSTED RANDOMIZATION TO FORCE BALANCE IN CONTROLLED TRIALS WITH UNKNOWN PREVALENCE OF COVARIATES: APPLICATION TO ALCOHOLISM RESEARCH
2005
In treatment outcome studies with small to medium sample sizes (n200), the balance of groups with regard to important factors, which sometimes occur at low prevalence, is indispensable for adequate interpretation. This study tested a method for use in clinical alcoholism research, an uncomplicated procedure for satisfactory randomization of patients to different treatments, taking into account relevant background variables.An easily applicable modification of Efron's biased coin method for the randomization of treatments within strata of unknown but low prevalence was compared with the original approach and alternative methods by computer simulation (10,000 runs). An application example for…
Covariate-constrained Randomization Routine for Achieving Baseline Balance in Cluster-randomized Trials
2017
In cluster-randomized trials, groups or clusters of individuals, rather than individuals themselves, are randomly allocated to intervention or control. In this article, we describe a new command, ccrand, that implements a covariate-constrained randomization procedure for cluster-randomized trials. It can ensure balance of one or more baseline covariates between trial arms by restriction to allocations that meet specified balance criteria. We provide a brief overview of the theoretical background, describe ccrand and its options, and illustrate it using an example.
Individual participant data systematic reviews with meta-analyses of psychotherapies for borderline personality disorder
2021
IntroductionThe heterogeneity in people with borderline personality disorder (BPD) and the range of specialised psychotherapies means that people with certain BPD characteristics might benefit more or less from different types of psychotherapy. Identifying moderating characteristics of individuals is a key to refine and tailor standard treatments so they match the specificities of the individual participant. The objective of this is to improve the quality of care and the individual outcomes. We will do so by performing three systematic reviews with meta-analyses of individual participant data (IPD). The aim of these reviews is to investigate potential predictors and moderating patient chara…
Meta-analysis of time perception and temporal processing in schizophrenia: Differential effects on precision and accuracy
2016
Numerous studies have reported that time perception and temporal processing are impaired in schizophrenia. In a meta-analytical review, we differentiate between time perception (judgments of time intervals) and basic temporal processing (e.g., judgments of temporal order) as well as between effects on accuracy (deviation of estimates from the veridical value) and precision (variability of judgments). In a meta-regression approach, we also included the specific tasks and the different time interval ranges as covariates. We considered 68 publications of the past 65years, and meta-analyzed data from 957 patients with schizophrenia and 1060 healthy control participants. Independent of tasks and…
Using active learning to adapt remote sensing image classifiers
2011
The validity of training samples collected in field campaigns is crucial for the success of land use classification models. However, such samples often suffer from a sample selection bias and do not represent the variability of spectra that can be encountered in the entire image. Therefore, to maximize classification performance, one must perform adaptation of the first model to the new data distribution. In this paper, we propose to perform adaptation by sampling new training examples in unknown areas of the image. Our goal is to select these pixels in an intelligent fashion that minimizes their number and maximizes their information content. Two strategies based on uncertainty and cluster…
Towards the specification of a self-exciting point process for modelling crimes in Valencia
2023
A number of papers have dealt with the analysis of crime data using self-exciting point process theory after the analogy drawn between aftershock ETAS models and crime rate. With the aim to describe crime events that occurred in Valencia in the last decade, in this paper, we justify the need for a self-exciting point process model through spatial and temporal exploratory analysis.
Regression with Imputed Covariates: A Generalized Missing Indicator Approach
2011
A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations with the imputed values to gain precision may lead to bias. In this paper we formalize this trade-off by showing that one can augment the regression model with a set of auxiliary variables so as to obtain, under weak assumptions about the imputations, the same unbiased estimator of the parameters of interest as complete-case analysis. Given this augmented model, the bias-precision trade-off may then…
A Generalized Missing-Indicator Approach to Regression with Imputed Covariates
2011
We consider estimation of a linear regression model using data where some covariate values are missing but imputations are available to fill in the missing values. This situation generates a tradeoff between bias and precision when estimating the regression parameters of interest. Using only the subsample of complete observations does not cause bias but may imply a substantial loss of precision because the complete cases may be too few. On the other hand, filling in the missing values with imputations may cause bias. We provide the new Stata command gmi, which handles such tradeoff by using either model reduction or Bayesian model averaging techniques in the context of the generalized miss…
Explaining Method Effects Associated With Negatively Worded Items in Trait and State Global and Domain-Specific Self-Esteem Scales
2013
Several investigators have interpreted method effects associated with negatively worded items in a substantive way. This research extends those studies in different ways: (a) it establishes the presence of methods effects in further populations and particular scales, and (b) it examines the possible relations between a method factor associated with negatively worded items and several covariates. Two samples were assessed: 592 high school students from Valencia (Spain), and 285 batterers from the same city. The self-esteem scales used were Rosenberg's Self-Esteem Scale, the State Self-Esteem Scale, and Self-Esteem 17. Anxiety was also assessed with the State-Trait Anxiety Inventory, and gend…