Annamaria Guolo

Random-effects meta-analysis: the number of studies matters

This paper investigates the impact of the number of studies on meta-analysis and meta-regression within the random-effects model framework. It is frequently neglected that inference in random-effects models requires a substantial number of studies included in meta-analysis to guarantee reliable conclusions. Several authors warn about the risk of inaccurate results of the traditional DerSimonian and Laird approach especially in the common case of meta-analysis involving a limited number of studies. This paper presents a selection of likelihood and non-likelihood methods for inference in meta-analysis proposed to overcome the limitations of the DerSimonian and Laird procedure, with a focus on the effect of the number of studies. The applicability and the performance of the methods are investigated in terms of Type I error rates and empirical power to detect effects, according to scenarios of practical interest. Simulation studies and applications to real meta-analyses highlight that it is not possible to identify an approach uniformly superior to alternatives. The overall recommendation is to avoid the DerSimonian and Laird method when the number of meta-analysis studies is modest and prefer a more comprehensive procedure that compares alternative inferential approaches. R code for meta-analysis according to all of the inferential methods examined in the paper is provided.

Robust techniques for measurement error correction: a review

Measurement 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 80s. 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 the underlying modeling assumptions and the 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.

A simulation-based comparison of techniques to correct for measurement error in matched case–control studies

The presence of measurement errors affecting the covariates in regression models is a relevant topic in many scientific areas, as, for example, in epidemiology. An example is given by an epidemiological population-based matched case-control study on the aetiology of childhood malignancies, which is currently under completion in Italy. This study was aimed at evaluating the effects of childhood exposure to extremely low electromagnetic fields on the risk of disease occurrence by taking into account the possibility of erroneous measures of the exposure. Within this framework, we focus on the application of likelihood methods to correct for measurement error. This approach, which has received less attention in literature with respect to alternatives, is compared with commonly used methods such as regression calibration and SIMEX. The comparison is performed by simulation, under a broad range of measurement error structures.

Detection of silica particles in lung tissue by environmental scanning electron microscopy

For pathologists, pneumologists, and occupational and environmental physicians it is relevant to know silica levels in lung tissue to better define limits of exposure. Environmental Scanning Electron Microscopy (ESEM) has been employed to detect silica particles and to compare silica levels in subjects with and without Lung Cancer (LC). We investigated 25 paraffin-embedded tissue samples of patients with LC adenocarcinoma, and 20 fresh samples of subjects without LC deceased for extra-pulmonary diseases. Silica levels were quantified considering the Number of Spots of silica particles (NS), and the Number of Positive Zones (NPZ) in which there was at least one spot. Levels of NS and NPZ were assessed with Poisson-type regression models, and in two samples of silica-exposed workers with LC the performance of models were evaluated. LC patients displayed higher silica levels, as compared to controls; smoking, age and gender had no significant effects on this relationship. Values of NS and NPZ for the exposed workers were in agreement with model estimates. The fitted model between NS and NPZ might be useful in evaluating new observations and in the development of threshold limit values of silica in biological tissues. ESEM is a rapid, simple and valid tool for the determination of silica levels in lung tissues.

A Flexible Approach to Measurement Error Correction in Case–Control Studies

SUMMARY We investigate the use of prospective likelihood methods to analyze retrospective case-control data where some of the covariates are measured with error. We show that prospective methods can be applied and the case-control sampling scheme can be ignored if one adequately models the distribution of the error-prone covariates in the case-control sampling scheme. Indeed, subject to this, the prospective likelihood methods result in consistent estimates and information standard errors are asymptotically correct. However, the distribution of such covariates is not the same in the population and under case-control sampling, dictating the need to model the distribution flexibly. In this article, we illustrate the general principle by modeling the distribution of the continuous error-prone covariates using the skewnormal distribution. The performance of the method is evaluated through simulation studies, which show satisfactory results in terms of bias and coverage. Finally, the method is applied to the analysis of two data sets which refer, respectively, to a cholesterol study and a study on breast cancer.

Beta regression for time series analysis of bounded data, with application to Canada Google 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

Improved inference on a scalar fixed effect of interest in nonlinear mixed-effects models

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

Flu Trends project.

Improving likelihood-based inference in control rate regression

Likelihood-based inference on a scalar fixed effect of interest in nonlinear mixed-effects models usually relies on first-order approximations. If the sample size is small, tests and confidence intervals derived from first-order solutions can be inaccurate. An improved test statistic based on a modification of the signed likelihood ratio statistic is presented which was recently suggested by Skovgaard [1996. An explicit large-deviation approximation to one-parameter tests. Bernoulli 2, 145-165]. The finite sample behaviour of this statistic is investigated through a set of simulation studies. The results show that its finite-sample null distribution is better approximated by the standard normal than it is for its first-order counterpart. The R code used to run the simulations is freely available.

Is the laparoscopic adrenalectomy for pheochromocytoma the best treatment

SummaryRandom-effects models are frequently used to synthesize 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.