Correlation In Longitudinal Data Research Articles

Morphological prognosis prediction of choroid neovascularization from longitudinal SD-OCT images.

Choroid neovascularization (CNV) has no obvious symptoms in the early stage, but with its gradual expansion, leakage, rupture, and bleeding, it can cause vision loss and central scotoma. In some severe cases, it will lead to permanent visual impairment. Accurate prediction of disease progression can greatly help ophthalmologists to formulate appropriate treatment plans and prevent further deterioration of the disease. Therefore, we aim to predict the growth trend of CNV to help the attending physician judge the effectiveness of treatment. In this paper, we develop a CNN-based method for CNV growth prediction. To achieve this, we first design a registration network to rigidly register the spectral domain optical coherence tomography (SD-OCT) B-scans of each subject at different time points to eliminate retinal displacements of longitudinal data. Then, considering the correlation of longitudinal data, we propose a co-segmentation network with a correlation attention guidance (CAG) module to cooperatively segment CNV lesions of a group of follow-up images and use them as input for growth prediction. Finally, based on the above registration and segmentation networks, an encoder-recurrent-decoder framework is developed for CNV growth prediction, in which an attention-based gated recurrent unit (AGRU) is embedded as the recurrent neural network to recurrently learn robust representations. The registration network rigidly registers the follow-up images of patients to the reference images with a root mean square error (RMSE) of 6.754 pixels. And compared with other state-of-the-art segmentation methods, the proposed segmentation network achieves high performance with the Dice similarity coefficients (Dsc) of 85.27%. Based on the above experiments, the proposed growth prediction network can play a role in predicting the future CNV morphology, and the predicted CNV has a Dsc of 83.69% with the ground truth, which is significantly consistent with the actual follow-up visit. The proposed registration and segmentation networks provide the possibility for growth prediction. In addition, accurately predicting the growth of CNV enables us to know the efficacy of the drug against individuals in advance, creating opportunities for formulating appropriate treatment plans.

Longitudinal SNP-set association analysis of quantitative phenotypes.

Many genetic epidemiological studies collect repeated measurements over time. This design not only provides a more accurate assessment of disease condition, but allows us to explore the genetic influence on disease development and progression. Thus, it is of great interest to study the longitudinal contribution of genes to disease susceptibility. Most association testing methods for longitudinal phenotypes are developed for single variant, and may have limited power to detect association, especially for variants with low minor allele frequency. We propose Longitudinal SNP-set/sequence kernel association test (LSKAT), a robust, mixed-effects method for association testing of rare and common variants with longitudinal quantitative phenotypes. LSKAT uses several random effects to account for the within-subject correlation in longitudinal data, and allows for adjustment for both static and time-varying covariates. We also present a longitudinal trait burden test (LBT), where we test association between the trait and the burden score in linear mixed models. In simulation studies, we demonstrate that LBT achieves high power when variants are almost all deleterious or all protective, while LSKAT performs well in a wide range of genetic models. By making full use of trait values from repeated measures, LSKAT is more powerful than several tests applied to a single measurement or average over all time points. Moreover, LSKAT is robust to misspecification of the covariance structure. We apply the LSKAT and LBT methods to detect association with longitudinally measured body mass index in the Framingham Heart Study, where we are able to replicate association with a circadian gene NR1D2.

Bayesian inference for additive mixed quantile regression models

Quantile regression problems in practice may require flexible semiparametric forms of the predictor for modeling the dependence of responses on covariates. Furthermore, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudinal data. We present a unified approach for Bayesian quantile inference on continuous response via Markov chain Monte Carlo (MCMC) simulation and approximate inference using integrated nested Laplace approximations (INLA) in additive mixed models. Different types of covariate are all treated within the same general framework by assigning appropriate Gaussian Markov random field (GMRF) priors with different forms and degrees of smoothness. We applied the approach to extensive simulation studies and a Munich rental dataset, showing that the methods are also computationally efficient in problems with many covariates and large datasets.

Simple State-Space Models in a Mixed Model Framework

State-space models play a central role in time series analysis. Biological time series, which present trend, seasonal, and cyclic fluctuations, can be well described by such models. In addition, biological experiments and surveys often have a relatively complex design structure calling for special attention. It is straightforward to account for design effects in a mixed linear model framework. This article shows how simple state-space models can be cast as a standard mixed model, provided the transition matrix of the state equation has a simple form. This opens up the opportunity for refined modeling of time series data involving complex blocking and treatment structures. Conversely, the state-space model gives rise to a special class of variance-covariance structures. Thus, integrating state-space components into a mixed model broadens the class of variance-covariance structures that may be employed to model serial correlation in longitudinal data. The approach is illustrated using several biological examples.

Inequality and Economic Growth: Data Comparisons and Econometric Tests

This paper discusses two issues in the relationship between inequality and economic growth: the data and the econometrics. We first review the inequality data set of Deininger and Squire, which, we argue, fails to provide adequate or accurate longitudinal and cross-country coverage. We then introduce our own measures of the inequality of manufacturing pay, based on the UNIDO Industrial Statistics. In our view, these provide indicators of inequality that are more stable, more reliable, and more comparable across countries than those of Deininger and Squire. Turning to the relationship between inequality and development, we diagnose several common econometric problems in the literature, including measurement error, omitted variable bias, serial correlation in longitudinal data, and the possible persistence of lagged dependent variables. By taking steps to account for these problems, we seek more reliable inferences concerning the relationship between inequality, national income and economic growth. We find evidence that generally supports Kuznets’ specification for industrializing countries: inequality tends to decline as per capita income increases. However, after 1981 two problems emerge. First, per capita GDP growth slows dramatically in most countries. Second, there is a worldwide trend toward rising inequality in our data, independent of GDP or its changes. The timing and geographic pattern of these increases suggest a link to the high real interest rates and global debt crisis of the period beginning in 1982.