Terms Of Intra-class Correlation Research Articles

Individual and community-level factors associated with early initiation of antenatal care: Multilevel modelling of 2018 Cameroon Demographic and Health Survey.

BackgroundEarly initiation of antenatal care (ANC) provided by skilled personnel is essential as it enables pregnant women to receive comprehensive reproductive health services. Early ANC utilisation could prevent complications related to pregnancy and improve maternal and neonatal health outcomes. Regardless of this, only forty-one in every hundred women in Cameroon seek early ANC services. Studies on the uptake of antenatal care in Cameroon have not focused on individual and community-level factors that influence early initiation of ANC. This study aimed at investigating the association between individual and community-level factors and early ANC uptake in Cameroon.MethodsThis study was a cross-sectional survey design. Data was extracted from the women’s file of the 2018 Cameroon Demographic and Health Survey (CDHS). A sample of 4,183 women aged 15–49 who had complete information on variables of interest to the study was used. The outcome variable was early ANC (i.e. women whose first ANC occurred between 0–3 months of pregnancy). Eighteen explanatory variables consisting of fifteen individual-level variables and three community-level variables were selected for the study. At 95% confidence interval (95% CI), two-level multilevel models were built. The results for the fixed effects were presented in adjusted odds ratio (aOR) and the random effects were expressed in terms of Intra-Class Correlation (ICC) and Primary Sampling Units (PSUs) variance.ResultsDescriptively, 46% [CI = 45.0–48.0] of the women aged 15–49 attended ANC earlier. The median age at which women started utilising early ANC was 28 (15 to 48) years. For the fixed effects results, it was found that the odds of seeking early ANC increased among those aged 35–39 [aOR = 1.78, CI = 1.24–2.57], the richest [aOR = 2.43, CI = 1.63–3.64] and those with secondary/higher education [aOR = 1.38, CI = 1.05–1.82]. Muslims [aOR = 0.73, CI = 0.60–0.88] and women at parity four or more had lesser odds to seek early ANC [aOR = 0.63, CI = 0.49–0.82]. The study found that primary sampling unit (community/cluster) [σ2 = 0.53, CI = 0.40–0.72] and individual [σ2 = 0.16, CI = 0.09–0.29] level variations exist in early initiation of ANC. About 14% (intra-class correlation (ICC) = 0.14) and 5% (ICC = 0.05) variability in early initiation of ANC were attributable to variations in the primary sampling unit (community/cluster) and individual-level factors, respectively.ConclusionIndividual-level factors (maternal age, wealth status, educational attainment and religious affiliation) were associated with early initiation of ANC whereas variations in cluster/community characteristics contributed to the variations in early initiation of ANC seeking. The Departments of Health Promotion, Health Information Center and eHealth under the Ministry of Public Health, Cameroon, have to strengthen mass sensitisation programs on early ANC uptake and such programs should consider individual differences such as age, wealth status, education, and religious affiliation in its program design.

Direct Cellularity Estimation on Breast Cancer Histopathology Images Using Transfer Learning.

Residual cancer burden (RCB) has been proposed to measure the postneoadjuvant breast cancer response. In the workflow of RCB assessment, estimation of cancer cellularity is a critical task, which is conventionally achieved by manually reviewing the hematoxylin and eosin- (H&E-) stained microscopic slides of cancer sections. In this work, we develop an automatic and direct method to estimate cellularity from histopathological image patches using deep feature representation, tree boosting, and support vector machine (SVM), avoiding the segmentation and classification of nuclei. Using a training set of 2394 patches and a test set of 185 patches, the estimations by our method show strong correlation to those by the human pathologists in terms of intraclass correlation (ICC) (0.94 with 95% CI of (0.93, 0.96)), Kendall's tau (0.83 with 95% CI of (0.79, 0.86)), and the prediction probability (0.93 with 95% CI of (0.91, 0.94)), compared to two other methods (ICC of 0.74 with 95% CI of (0.70, 0.77) and 0.83 with 95% CI of (0.79, 0.86)). Our method improves the accuracy and does not rely on annotations of individual nucleus.

A non-invasive biomechanical device to quantify knee rotational laxity: Verification of the device in human cadaveric specimens

BackgroundBiomechanical measurement tools have been developed and widely used to precisely quantify knee anterior-posterior laxity after anterior cruciate ligament (ACL) injury. However, validated objective device to document knee rotational laxity, though being developed by different researchers, are not yet widely used in the daily clinical practice. A new biomechanical device was developed to quantify knee internal and external rotations. MethodsThe reliability of the new biomechanical device which measures knee rotations were tested. Different torques (1-10Nm) were applied by the device to internally and externally rotate human cadaveric knees, which were held in a flexion angle of 30°. The rotations were measured by the device in degrees. There were two independent testers, and each tester carried out three trials. Intra-rater and inter-rater reliability were quantified in terms of intraclass correlation (ICC) coefficient among trials and between testers. The device was verified by the comparison with a computer assisted navigation system. ICC was measured. Mean, standard deviation and 95% confident interval of the difference as well as the root mean square difference were calculated. The correlations were deemed to be reliable if the ICC was above 0.75. ResultsThe intra-rater and inter-rater reliability achieved high correlation for both internal and external rotation, ranged from 0.959 to 0.992. ICC between the proposed meter and the navigation system for both internal and external rotation was 0.78. The mean differences were 2.3° and 2.5° for internal and external rotation respectively. ConclusionsA new knee rotational laxity meter was proposed in this study. Its reliability was verified by showing high correlation among trials. It also showed good correlation to a gold standard of measurement. It might be used to document knee rotational laxity for various purposes, especially after ACL injury, after further validation of the device in human subjects.

The relationship between multilevel models and non-parametric multilevel mixture models: Discrete approximation of intraclass correlation, random coefficient distributions, and residual heteroscedasticity.

Multilevel data structures are common in the social sciences. Often, such nested data are analysed with multilevel models (MLMs) in which heterogeneity between clusters is modelled by continuously distributed random intercepts and/or slopes. Alternatively, the non-parametric multilevel regression mixture model (NPMM) can accommodate the same nested data structures through discrete latent class variation. The purpose of this article is to delineate analytic relationships between NPMM and MLM parameters that are useful for understanding the indirect interpretation of the NPMM as a non-parametric approximation of the MLM, with relaxed distributional assumptions. We define how seven standard and non-standard MLM specifications can be indirectly approximated by particular NPMM specifications. We provide formulas showing how the NPMM can serve as an approximation of the MLM in terms of intraclass correlation, random coefficient means and (co)variances, heteroscedasticity of residuals at level 1, and heteroscedasticity of residuals at level 2. Further, we discuss how these relationships can be useful in practice. The specific relationships are illustrated with simulated graphical demonstrations, and direct and indirect interpretations of NPMM classes are contrasted. We provide an R function to aid in implementing and visualizing an indirect interpretation of NPMM classes. An empirical example is presented and future directions are discussed.

Feasibility of automated speech sample collection with stuttering children using interactive voice response (IVR) technology

Purpose: To investigate the feasibility of adopting automated interactive voice response (IVR) technology for remotely capturing standardized speech samples from stuttering children.Method: Participants were 10 6-year-old stuttering children. Their parents called a toll-free number from their homes and were prompted to elicit speech from their children using a standard protocol involving conversation, picture description and games. The automated IVR system was implemented using an off-the-shelf telephony software program and delivered by a standard desktop computer. The software infrastructure utilizes voice over internet protocol. Speech samples were automatically recorded during the calls. Video recordings were simultaneously acquired in the home at the time of the call to evaluate the fidelity of the telephone collected samples. Key outcome measures included syllables spoken, percentage of syllables stuttered and an overall rating of stuttering severity using a 10-point scale.Result: Data revealed a high level of relative reliability in terms of intra-class correlation between the video and telephone acquired samples on all outcome measures during the conversation task. Findings were less consistent for speech samples during picture description and games.Conclusion: Results suggest that IVR technology can be used successfully to automate remote capture of child speech samples.

Reproducibility of the Physiological Cost Index among Individuals with a Lower‐Limb Amputation and Healthy Adults

The physiological cost index (PCI) is a clinical measurement used to estimate the energy cost of walking. The reproducibility of the PCI has been questioned and no study has investigated the measurement error among individuals with a lower-limb amputation. The aim was to investigate the test-retest reproducibility of the PCI in individuals with a lower-limb amputation and healthy adults. The study comprised 28 individuals (20 males, eight females, mean age 49 years) with a unilateral amputation due to reasons other than vascular disease and 31 healthy volunteers (20 males, 11 females, mean age 47 years). PCI values were obtained by registering heart rate at rest and during level indoor walking for 5 minutes at a comfortable speed. A within-day test-retest assessment was performed. Reproducibility analyses included intra-class correlation, analyses of systematic differences between measurements, calculation of the smallest detectable change (SDC) and coefficient of variation (CV), and the results were finally visualized using Bland-Altman plots. The reliability in terms of intra-class correlation was excellent for both groups (0.966 and 0.948). In the amputee group, the PCI revealed a mean difference of 0.026 (p = 0.016) between tests (PCI = 0.555, standard deviation [SD] = 0.214 and PCI = 0.581, SD = 0.236, respectively). In the healthy group, there was no systematic difference between tests (PCI = 0.329, SD = 0.114 and PCI = 0.331, SD = 0.110, respectively). The SDC was 0.116 in the amputee group and 0.070 in the healthy group, giving a CV of 20.4% and 21.0%,respectively. The within-day test-retest reproducibility of the PCI was excellent among individuals with lower-limb amputations and healthy adults in terms of intra-class correlation and acceptable in terms of agreement. The SDC, which was calculated for each group, should be considered when demonstrating an individual difference after an intervention.

Bayes factor for testing the genetic background of quantitative threshold traits

The recent development of the Bayes factor methodology to test between nested competing models has allowed to evaluate the genetic component of continuous traits through a methodology based on the reparameterization of a variance component model in terms of intra-class correlation. In this study, we present a modification of that procedure to compare between threshold models that only differ in a bounded variable. An extra data-augmentation step is required to sample liability, an underlying continuous variable related with phenotypic records through an adequate number of thresholds. In this context, we describe a procedure to sample from a truncated multivariate normal distribution with non-null covariances between records, taking as starting point the Cholesky factorization and the inclusion of linear restrictions. The method was tested by computer simulations with satisfactory results.

Bayes factors for detection of Quantitative Trait Loci

A fundamental issue in quantitative trait locus (QTL) mapping is to determine the plausibility of the presence of a QTL at a given genome location. Bayesian analysis offers an attractive way of testing alternative models (here, QTL vs. no-QTL) via the Bayes factor. There have been several numerical approaches to computing the Bayes factor, mostly based on Markov Chain Monte Carlo (MCMC), but these strategies are subject to numerical or stability problems. We propose a simple and stable approach to calculating the Bayes factor between nested models. The procedure is based on a reparameterization of a variance component model in terms of intra-class correlation. The Bayes factor can then be easily calculated from the output of a MCMC scheme by averaging conditional densities at the null intra-class correlation. We studied the performance of the method using simulation. We applied this approach to QTL analysis in an outbred population. We also compared it with the Likelihood Ratio Test and we analyzed its stability. Simulation results were very similar to the simulated parameters. The posterior probability of the QTL model increases as the QTL effect does. The location of the QTL was also correctly obtained. The use of meta-analysis is suggested from the properties of the Bayes factor.

Bayes factors for detection of Quantitative Trait Loci

A fundamental issue in quantitative trait locus (QTL) mapping is to determine the plausibility of the presence of a QTL at a given genome location. Bayesian analysis offers an attractive way of testing alternative models (here, QTL vs. no-QTL) via the Bayes factor. There have been several numerical approaches to computing the Bayes factor, mostly based on Markov Chain Monte Carlo (MCMC), but these strategies are subject to numerical or stability problems. We propose a simple and stable approach to calculating the Bayes factor between nested models. The procedure is based on a reparameterization of a variance component model in terms of intra-class correlation. The Bayes factor can then be easily calculated from the output of a MCMC scheme by averaging conditional densities at the null intra-class correlation. We studied the performance of the method using simulation. We applied this approach to QTL analysis in an outbred population. We also compared it with the Likelihood Ratio Test and we analyzed its stability. Simulation results were very similar to the simulated parameters. The posterior probability of the QTL model increases as the QTL effect does. The location of the QTL was also correctly obtained. The use of meta-analysis is suggested from the properties of the Bayes factor.