1,721,049 research outputs found
Hierarchical multinomial processing tree models for meta-analysis of diagnostic accuracy studies
Full text linkMeta-analysis represents a widely accepted approach for evaluating the
accuracy of diagnostic tools in clinical and psychological investigations. This
paper investigates the applicability of multinomial tree models recently
suggested in the literature under a fixed-effects formulation for assessing the
accuracy of binary classification tools. The model proposed in this paper
extends previous results to a hierarchical structure accounting for the
variability between the studies included in the meta-analysis. Interestingly,
the resulting hierarchical multinomial tree model resembles the well-known
bivariate random-effects model under an exact within-study distribution for the
number of true positives and true negatives subjects, with the additional
advantage of providing an estimate of the prevalences of disease from each
study. The proposal is in line with a latent-trait approach, where inference is
performed according to a frequentist point of view. The applicability of the
proposed model and its performance with respect to the approximate bivariate
random-effects model based on normality assumptions commonly used in the
literature is evaluated in a series of simulation studies. Methods are applied
to a real meta-analysis about the accuracy of the confusion assessment method
as delirium screening tool
Robust Techniques for Measurement Error Correction: A Review
No full textMeasurement error affecting the independent variables in regression models is a common problem in many scientific areas. It is well known that the implications of ignoring measurement errors in inferential procedures may be substantial, often turning out in unreliable results. Many different measurement error correction techniques have been suggested in literature since the 80's. Most of them require many assumptions oil the involved variables to be satisfied. However, it may be usually very hard to check whether these assumptions are satisfied, mainly because of the lack of information about the unobservable and mismeasured phenomenon. Thus, alternatives based oil weaker assumptions oil the variables may be preferable, in that they offer a gain in robustness of results. In this paper, we provide a review of robust techniques to correct for Measurement errors affecting the covariates. Attention is paid to methods which share properties of robustness against misspecifications of relationships between variables. Techniques are grouped according to the kind of the underlying modeling assumptions and the inferential methods. Details about file techniques are given and their applicability is discussed. The basic framework is the epidemiological setting, where literature about the measurement error phenomenon is very substantial
Higher-order likelihood inference in meta-analysis and meta-regression
No full textThis paper investigates the use of likelihood methods for meta-analysis, within the random-effects models framework. We show that likelihood inference relying on first-order approximations, while improving common meta-analysis techniques, can be prone to misleading results. This drawback is very evident in the case of small sample sizes, which are typical in meta-analysis. We alleviate the problem by exploiting the theory of higher-order asymptotics. In particular, we focus on a second-order adjustment to the log-likelihood ratio statistic. Simulation studies in meta-analysis and meta-regression show that higher-order likelihood inference provides much more accurate results than its first-order counterpart, while being of a computationally feasible form. We illustrate the application of the proposed approach on a real example
Measurement error correction techniques in case-control studies.
No full textMeasurement error affecting covariates is a common problem in many scientific areas. In this paper, we focus on the application of likelihood-based techniques to correct for measurement errors in case-control studies. The likelihood approach has received less attention in literature with respect to alternative correction techniques, mainly because of the computational complexity and the difficulties in specifying the relationships among variables which are required. The performance of the likelihood approach is evaluated here through simulation studies, under a multiplicative error structure. Moreover, an extension of the likelihood-based method is proposed, which has the aim of robustifying the analysis with respect to misspecifications of the unobservable and mismeasured phenomenon
Measurement Error Correction in Exploiting Gene-Environment Independence in Family-Based Case-Control Studies.
Full text linkFamily-based case-control designs are commonly used in epidemiological studies for evaluating the role of genetic susceptibility and environmental exposure to risk factors in the etiology of rare diseases. Within this framework, it is often reasonable to assume genetic susceptibility and environmental exposure being conditionally independent of each other within families in the source population. We focus on this setting to consider the common situation of measurement error aecting the assessment of the environmental exposure. We propose to correct for measurement error through a likelihood-based method, by exploiting the conditional likelihood of Chatterjee, Kalaylioglu and Carroll (2005) to relate the probability of disease to the genetic and the mismeasured environmental risk factors. Simulation studies show that this approach provides less biased and more ecient results than that based on traditional logistic regression. The likelihood approach for measurement error correction is also compared to regression calibration, the last resulting in severely biased estimators of the parameters of interest
Pseudo-likelihood inference for measurement error models with emphasis on misclassification problems
No full textInference in meta-regression models with log event rates information
No full textThis work focuses on meta-analysis of treatment-control studies including the log event rate as a measure of the baseline risk for controls in order to explain between-study heterogeneity. Inference accounting for errors affecting the baseline risk measure is carried out using likelihood approaches and a simulation-based solution (SIMEX)
Robust Techniques for Measurement Error Correction in Case-Control Studies: A Review
Full text linkMeasurement error affecting the independent variables in regression models is a common problem in many scientific areas. It is well known that the implications of ignoring measurement errors in inferential procedures may be substantial, often turning out in unreliable results. Many different measurement error correction techniques have been suggested in literature since the 80's. Most of them require many assumptions on the involved variables to be satisfied. However, it may be usually very hard to check whether these assumptions are satisfied, mainly because of the lack of information about the unobservable and mismeasured phenomenon. Thus, alternatives based on weaker assumptions on the variables may be preferable, in that they offer a gain in robustness of results. In this paper, we provide a review of robust techniques to correct for measurement errors affecting the covariates.
Attention is paid to methods which share properties of robustness against misspecifications of relationships between variables. Techniques are grouped according to the kind of underlying modeling assumptions and inferential methods.
Details about the techniques are given and their applicability is discussed. The basic framework is the epidemiological setting, where literature about the measurement error phenomenon is very substantial. The focus will be mainly on case-control studies
Measurement error correction in exploiting gene-environment independence in family-based case-control studies
No full textFamily-based case–control designs are commonly used in epidemiological studies for evaluating the role of genetic susceptibility and environmental exposure to risk factors in the etiology of rare diseases. Within this framework, it is often reasonable to assume genetic susceptibility and environmental exposure being conditionally independent of each other within families in the source population. We focus on this setting to explore the situation of measurement error affecting the assessment of the environmental exposure. We correct for measurement error through a likelihood- based method. We exploit a conditional likelihood approach to relate the probability of disease to the genetic and the environmental risk factors. We show that this approach provides less biased and more efficient results than that based on logistic regression. Regression calibration, instead, provides severely biased estimators of the parameters. The comparison of the correction methods is performed through simulation, under common measurement error structures
Improving likelihood-based inference in control rate regression
Full text linkControl rate regression is a diffuse approach to account for heterogeneity among studies in meta-analysis by including information about the outcome risk of patients in the control condition. Correcting for the presence of measurement error affecting risk information in the treated and in the control group has been recognized as a necessary step to derive reliable inferential conclusions. Within this framework, the paper considers the problem of small sample size as an additional source of misleading inference about the slope of the control rate regression. Likelihood procedures relying on first-order approximations are shown to be substantially inaccurate, especially when dealing with increasing heterogeneity and correlated measurement errors. We suggest to address the problem by relying on higher-order asymptotics. In particular, we derive Skovgaard's statistic as an instrument to improve the accuracy of the approximation of the signed profile log-likelihood ratio statistic to the standard normal distribution. The proposal is shown to provide much more accurate results than standard likelihood solutions, with no appreciable computational effort. The advantages of Skovgaard's statistic in control rate regression are shown in a series of simulation experiments and illustrated in a real data example. R code for applying first- and second-order statistic for inference on the slope on the control rate regression is provided
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