arXiv subjects

Annamaria Guolo

Publications and source records attributed to Annamaria Guolo.

6 recordsLinked to original sources

arXiv2026

Dealing with partial missing correlations in multivariate and surrogate meta-analyses

This work addresses the issue of partially missing correlations within the framework of bivariate and surrogate meta-analyses. While restricting the analysis to complete-case studies may appear to constitute the most straightforward analytical strategy, such an approach has been demonstrated to yield substantial inefficiencies and potential bias in the resulting estimates. Current methodological contributions in the literature circumvent this limitation either through aggregate estimation procedures grounded in likelihood-based frameworks under simplifying assumptions, or by resorting to deterministic imputation strategies, such as the empirical mean derived from observed units. In the present paper, we propose a multiple imputation framework in which imputation is performed via stochastic procedures based on a Beta regression model, thereby explicitly accounting for the missing at random (MAR) assumption underlying the observed missingness mechanism. We demonstrate the effectiveness of our method through an extensive simulation study across various scenarios, comparing our proposal with simple mean imputation and complete-case analysis under the MAR assumption.

stat.ME↗

arXiv2023

Hierarchical multinomial processing tree models for meta-analysis of diagnostic accuracy studies

Meta-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.

stat.ME↗

arXiv2019

A pseudo-likelihood approach for multivariate meta-analysis of test accuracy studies with multiple thresholds

Multivariate meta-analysis of test accuracy studies when tests are evaluated in terms of sensitivity and specificity at more than one threshold represents an effective way to synthesize results by fully exploiting the data, if compared to univariate meta-analyses performed at each threshold independently. The approximation of logit transformations of sensitivities and specificities at different thresholds through a normal multivariate random-effects model is a recent proposal, that straightforwardly extends the bivariate models well recommended for the one threshold case. However, drawbacks of the approach, such as poor estimation of the within-study correlations between sensitivities and between specificities and severe computational issues, can make it unappealing. We propose an alternative method for inference on common diagnostic measures using a pseudo-likelihood constructed under a working independence assumption between sensitivities and between specificities at different thresholds in the same study. The method does not require within-study correlations, overcomes the convergence issues and can be effortlessly implemented. Simulation studies highlight a satisfactory performance of the method, remarkably improving the results from the multivariate normal counterpart under different scenarios. The pseudo-likelihood approach is illustrated in the evaluation of a test used for diagnosis of pre-eclampsia as a cause of maternal and perinatal morbidity and mortality.

stat.ME↗

arXiv2018

Improving likelihood-based inference in control rate regression

Control 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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arXiv2017

Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression

Random-effects models are frequently used to synthesise information from different studies in meta-analysis. While likelihood-based inference is attractive both in terms of limiting properties and of implementation, its application in random-effects meta-analysis may result in misleading conclusions, especially when the number of studies is small to moderate. The current paper shows how methodology that reduces the asymptotic bias of the maximum likelihood estimator of the variance component can also substantially improve inference about the mean effect size. The results are derived for the more general framework of random-effects meta-regression, which allows the mean effect size to vary with study-specific covariates.

stat.ME↗

arXiv2014

Beta regression for time series analysis of bounded data, with application to Canada Google${}^\circledR$ Flu Trends

Bounded time series consisting of rates or proportions are often encountered in applications. This manuscript proposes a practical approach to analyze bounded time series, through a beta regression model. The method allows the direct interpretation of the regression parameters on the original response scale, while properly accounting for the heteroskedasticity typical of bounded variables. The serial dependence is modeled by a Gaussian copula, with a correlation matrix corresponding to a stationary autoregressive and moving average process. It is shown that inference, prediction, and control can be carried out straightforwardly, with minor modifications to standard analysis of autoregressive and moving average models. The methodology is motivated by an application to the influenza-like-illness incidence estimated by the Google${}^\circledR$ Flu Trends project.

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