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Repeatability involves the assessment of the agreement among repeated measurements from the same cluster of subjects, and this concept has been widely used in different scientific fields when data is structured in clusters. In the context of spatial trajectories, a degree of repeatability implies that individual trips can be distinguished from those of other individuals.Repeatability is usually assessed by the intraclass correlation coefficient (ICC), which is defined as the proportion of the total variance accounted for by among‐subject variability. However, where data are spatial trajectories the common approach to estimate the ICC does not apply because data involves sets of ordered spatial locations rather than single values.In this work, a novel approach based on spatial distances is proposed to estimate the ICC to assess the repeatability of spatial trajectories. The methodology is illustrated with a real case example of the flight trajectories of 36 Audouin's gulls (Ichthyaetus audouinii) moving through a heterogeneous landscape over a period of 18 days. Additionally, simulations were used to evaluate the performance of the approach under various scenarios.I demonstrate that ICC can be estimated on complex, spatially ordered data such as spatial trajectories, when using the appropriate spatial distance. [ABSTRACT FROM AUTHOR]

The intraclass correlation coefficient (ICC) is a widely used index to assess agreement with continuous data. The common approach for estimating the ICC involves estimating the variance components of a linear mixed model under assumptions such as linearity and normality of effects. However, if the outcomes are counts these assumptions are not met and the ICC estimates are biased and inefficient. In this situation, it is necessary to use alternative approaches that are better suited for count data. Here, the iccCounts R package is introduced for estimating the ICC under the Poisson, Negative Binomial, Zero-Inflated Poisson and Zero-Inflated Negative Binomial distributions. The utility of the iccCounts package is illustrated by three examples that involve the assessment of repeatability and concordance with count data. [ABSTRACT FROM AUTHOR]

Abstract Background Reducing childhood mortality is the fourth goal of the Millennium Development Goals agreed at the United Nations Millennium Summit in September 2000. However, childhood mortality in developing countries remains high. Providing an accurate picture of space and time-trend variations in child mortality in a region might generate further ideas for health planning actions to achieve such a reduction. The purpose of this study was to examine the spatio-temporal variation for child mortality rates in Manhiça, a district within the Maputo province of southern rural Mozambique during the period 1997-2005 using a proper generalized linear mixed model. Results The results showed that childhood mortality in all the area was modified from year to year describing a convex time-trend but the spatial pattern described by the neighbourhood-specific underlying mortality rates did not change during the entire period from 1997 to 2005, where neighbourhoods with highest risks are situated in the peripheral side of the district. The spatial distribution, though more blurred here, was similar to the spatial distribution of child malaria incidence in the same area. The peak in mortality rates observed in 2001 could have been caused by the precipitation system that started in early February 2000, following which heavy rains flooded parts of Mozambique's southern provinces. However, the mortality rates at the end of the period returned to initial values. Conclusions The results of this study suggest that the health intervention programmes established in Manhiça to alleviate the effects of flooding on child mortality should cover a period of around five years and that special attention might be focused on eradicating malaria transmission. These outcomes also suggest the utility of suitably modelling space-time trend variations in a region when a point effect of an environmental factor affects all the study area.

Abstract Background In an agreement assay, it is of interest to evaluate the degree of agreement between the different methods (devices, instruments or observers) used to measure the same characteristic. We propose in this study a technical simplification for inference about the total deviation index (TDI) estimate to assess agreement between two devices of normally-distributed measurements and describe its utility to evaluate inter- and intra-rater agreement if more than one reading per subject is available for each device. Methods We propose to estimate the TDI by constructing a probability interval of the difference in paired measurements between devices, and thereafter, we derive a tolerance interval (TI) procedure as a natural way to make inferences about probability limit estimates. We also describe how the proposed method can be used to compute bounds of the coverage probability. Results The approach is illustrated in a real case example where the agreement between two instruments, a handle mercury sphygmomanometer device and an OMRON 711 automatic device, is assessed in a sample of 384 subjects where measures of systolic blood pressure were taken twice by each device. A simulation study procedure is implemented to evaluate and compare the accuracy of the approach to two already established methods, showing that the TI approximation produces accurate empirical confidence levels which are reasonably close to the nominal confidence level. Conclusions The method proposed is straightforward since the TDI estimate is derived directly from a probability interval of a normally-distributed variable in its original scale, without further transformations. Thereafter, a natural way of making inferences about this estimate is to derive the appropriate TI. Constructions of TI based on normal populations are implemented in most standard statistical packages, thus making it simpler for any practitioner to implement our proposal to assess agreement.

Abstract Background MLPA method is a potentially useful semi-quantitative method to detect copy number alterations in targeted regions. In this paper, we propose a method for the normalization procedure based on a non-linear mixed-model, as well as a new approach for determining the statistical significance of altered probes based on linear mixed-model. This method establishes a threshold by using different tolerance intervals that accommodates the specific random error variability observed in each test sample. Results Through simulation studies we have shown that our proposed method outperforms two existing methods that are based on simple threshold rules or iterative regression. We have illustrated the method using a controlled MLPA assay in which targeted regions are variable in copy number in individuals suffering from different disorders such as Prader-Willi, DiGeorge or Autism showing the best performace. Conclusion Using the proposed mixed-model, we are able to determine thresholds to decide whether a region is altered. These threholds are specific for each individual, incorporating experimental variability, resulting in improved sensitivity and specificity as the examples with real data have revealed.

A logistic regression with random effects model is commonly applied to analyze clustered binary data, and every cluster is assumed to have a different proportion of success. However, it could be of interest to obtain the proportion of success over clusters (i.e. the marginal proportion of success). Furthermore, the degree of correlation among data of the same cluster (intraclass correlation) is also a relevant concept to assess, but when using logistic regression with random effects it is not possible to get an analytical expression of the estimators for marginal proportion and intraclass correlation. In our paper, we assess and compare approaches using different kinds of approximations: based on the logistic‐normal mixed effects model (LN), linear mixed model (LMM), and generalized estimating equations (GEE). The comparisons are completed by using two real data examples and a simulation study. The results show the performance of the approaches strongly depends on the magnitude of the marginal proportion, the intraclass correlation, and the sample size. In general, the reliability of the approaches get worsen with low marginal proportion and large intraclass correlation. LMM and GEE approaches arises as reliable approaches when the sample size is large. [ABSTRACT FROM AUTHOR]

Objective Neuroimaging is crucial in the presurgical evaluation of patients with medically refractory epilepsy. To improve the moderate sensitivity of [18F]fluorodeoxyglucose-positron emission tomography ( 18F-FDG-PET), our aim was to evaluate the usefulness of statistical parametric mapping ( SPM) to localize the seizure-onset zone ( SOZ) in PET studies deemed normal by visual assessment. Methods Fifty-five patients with medically refractory epilepsy whose 18F- FDG-PET was visually evaluated as normal were retrospectively included. Twenty of these patients had undergone surgical intervention. PET images were analyzed by SPM8 using a corrected p-value of p < 0.05 and three uncorrected p-values of p < 0.0001, p < 0.001, and p < 0.005, matched with minimum cluster sizes of k > 0, k > 20, k > 100, and k > 200, respectively. The SPM-identified potential seizure zone ( SZ) was compared to the SOZ, which was determined by consensus during patient management meetings in the epilepsy unit, taking into account presurgical tests. Studies in which the SPM-identified potential SZ was concordant with the SOZ were considered 'correctly localizing.' Results The SPM threshold combination with the least restrictive p-value and greatest minimum cluster size achieved the highest rate of correctly localizing studies. When p < 0.005/k > 200 was used, 40% (22/55) of studies were correctly localizing, and the concordance obtained in the surgically intervened subgroup was substantial (к = 0.607, 95% confidence interval [ CI] 0.258-0.957), which was comparable to the concordance obtained by magnetic resonance imaging ( MRI) (к = 0.783, 95% CI 0.509-1.000). Significance SPM offers improved SOZ localization in 18F- FDG-PET studies that are negative on visual assessment. For this purpose, statistical parametric maps could be thresholded with liberal p-values and restrictive cluster sizes. [ABSTRACT FROM AUTHOR]