Modeling longitudinal data with interval censored anchoring events

Indiana University-Purdue University Indianapolis (IUPUI)In many longitudinal studies, the time scales upon which we assess the primary outcomes are anchored by pre-specified events. However, these anchoring events are often not observable and they are randomly distributed with unknown distribution. Without direct observations of the anchoring events, the time scale used for analysis are not available, and analysts will not be able to use the traditional longitudinal models to describe the temporal changes as desired. Existing methods often make either ad hoc or strong assumptions on the anchoring events, which are unveri able and prone to biased estimation and invalid inference. Although not able to directly observe, researchers can often ascertain an interval that includes the unobserved anchoring events, i.e., the anchoring events are interval censored. In this research, we proposed a two-stage method to fit commonly used longitudinal models with interval censored anchoring events. In the first stage, we obtain an estimate of the anchoring events distribution by nonparametric method using the interval censored data; in the second stage, we obtain the parameter estimates as stochastic functionals of the estimated distribution. The construction of the stochastic functional depends on model settings. In this research, we considered two types of models. The first model was a distribution-free model, in which no parametric assumption was made on the distribution of the error term. The second model was likelihood based, which extended the classic mixed-effects models to the situation that the origin of the time scale for analysis was interval censored. For the purpose of large-sample statistical inference in both models, we studied the asymptotic properties of the proposed functional estimator using empirical process theory. Theoretically, our method provided a general approach to study semiparametric maximum pseudo-likelihood estimators in similar data situations. Finite sample performance of the proposed method were examined through simulation study. Algorithmically eff- cient algorithms for computing the parameter estimates were provided. We applied the proposed method to a real data analysis and obtained new findings that were incapable using traditional mixed-effects models.2 year

Morphic cohomology and singular cohomology of motives over the complex numbers

AbstractMorphic cohomology and singular cohomology of motives over the complex numbers are defined via the triangulated category of motives. Regarding morphic cohomology as functors defined on the triangulated category of motives, natural transformations of morphic cohomology are studied

Distribution‐free estimation of local growth rates around interval censored anchoring events

Biological processes are usually defined on timelines that are anchored by specific events. For example, cancer growth is typically measured by the change in tumor size from the time of oncogenesis. In the absence of such anchoring events, longitudinal assessments of the outcome lose their temporal reference. In this paper, we considered the estimation of local change rates in the outcomes when the anchoring events are interval‐censored. Viewing the subject‐specific anchoring event times as random variables from an unspecified distribution, we proposed a distribution‐free estimation method for the local growth rates around the unobserved anchoring events. We expressed the rate parameters as stochastic functionals of the anchoring time distribution and showed that under mild regularity conditions, consistent and asymptotically normal estimates of the rate parameters could be achieved, with a biom13015-gra-0001 convergence rate. We conducted a carefully designed simulation study to evaluate the finite sample performance of the method. To motivate and illustrate the use of the proposed method, we estimated the skeletal growth rates of male and female adolescents, before and after the unobserved pubertal growth spurt (PGS) times

The prediction of the quality of results in Logic Synthesis using Transformer and Graph Neural Networks

In the logic synthesis stage, structure transformations in the synthesis tool need to be combined into optimization sequences and act on the circuit to meet the specified circuit area and delay. However, logic synthesis optimization sequences are time-consuming to run, and predicting the quality of the results (QoR) against the synthesis optimization sequence for a circuit can help engineers find a better optimization sequence faster. In this work, we propose a deep learning method to predict the QoR of unseen circuit-optimization sequences pairs. Specifically, the structure transformations are translated into vectors by embedding methods and advanced natural language processing (NLP) technology (Transformer) is used to extract the features of the optimization sequences. In addition, to enable the prediction process of the model to be generalized from circuit to circuit, the graph representation of the circuit is represented as an adjacency matrix and a feature matrix. Graph neural networks(GNN) are used to extract the structural features of the circuits. For this problem, the Transformer and three typical GNNs are used. Furthermore, the Transformer and GNNs are adopted as a joint learning policy for the QoR prediction of the unseen circuit-optimization sequences. The methods resulting from the combination of Transformer and GNNs are benchmarked. The experimental results show that the joint learning of Transformer and GraphSage gives the best results. The Mean Absolute Error (MAE) of the predicted result is 0.412

Development and validation of a composite score for excessive alcohol use screening

This study was undertaken to develop a composite measure that combines the discriminant values of individual laboratory markers routinely used for excessive alcohol use (EAU) for an improved screening performance. The training sample consisted of 272 individuals with known history of EAU and 210 non-alcoholic individuals. The validation sample included 100 EAU and 75 controls. We used the estimated regression coefficients and the observed marker values to calculate the individual's composite screening score; this score was converted to a probability measure for excessive drinking in the given individual. A threshold value for the screening score based on an examination of the estimated sensitivity and specificity associated with different threshold values was proposed. Using regression coefficients estimated from the training sample, a composite score based on the levels of aspartate aminotransferase, alanine aminotransferase, per cent carbohydrate-deficient transferrin and mean corpuscular volume was calculated. The areas under the receiver operating characteristic curve (AUC) value of the selected model was 0.87, indicating a strong discriminating power and the AUC was better than that of each individual test. The score >0.23 corresponded to a sensitivity of 90% and a specificity of nearly 60%. The AUC value remained at a respectable level of 0.83 with the sensitivity and specificity at 91% and 49%, respectively, in the validation sample. We developed a novel composite score by using a combination of commonly used biomakers. However, the development of the mechanism-based biomarkers of EAU is needed to improve the screening and diagnosis of EAU in clinical practice

Stochastic functional estimates in longitudinal models with interval‐censored anchoring events

Timelines of longitudinal studies are often anchored by specific events. In the absence of the fully observed anchoring event times, the study timeline becomes undefined, and the traditional longitudinal analysis loses its temporal reference. In this paper, we considered an analytical situation where the anchoring events are interval censored. We demonstrated that by expressing the regression parameter estimators as stochastic functionals of a plug‐in estimate of the unknown anchoring event time distribution, the standard longitudinal models could be extended to accommodate the situation of less well‐defined timelines. We showed that for a broad class of longitudinal models, the functional parameter estimates are consistent and asymptotically normally distributed with a ⎯⎯√ convergence rate under mild regularity conditions. Applying the developed theory to linear mixed‐effects models, we further proposed a hybrid computational procedure that combines the strengths of the Fisher's scoring method and the expectation‐expectation (EM) algorithm for model parameter estimation. We conducted a simulation study to validate the asymptotic properties and to assess the finite sample performance of the proposed method. A real data example was used to illustrate the proposed method. The method fills in a gap in the existing longitudinal analysis methodology for data with less well‐defined timelines

Transgelin Induces Dysfunction of Fetal Endothelial Colony-Forming Cells From Gestational Diabetic Pregnancies

Fetal exposure to gestational diabetes mellitus (GDM) predisposes children to future health complications including hypertension and cardiovascular disease. A key mechanism by which these complications occur is through the functional impairment of vascular progenitor cells, including endothelial colony-forming cells (ECFCs). Previously, we showed that fetal ECFCs exposed to GDM have decreased vasculogenic potential and altered gene expression. In this study, we evaluate whether transgelin (TAGLN), which is increased in GDM-exposed ECFCs, contributes to vasculogenic dysfunction. TAGLN is an actin-binding protein involved in the regulation of cytoskeletal rearrangement. We hypothesized that increased TAGLN expression in GDM-exposed fetal ECFCs decreases network formation by impairing cytoskeletal rearrangement resulting in reduced cell migration. To determine if TAGLN is required and/or sufficient to impair ECFC network formation, TAGLN was reduced and overexpressed in ECFCs from GDM and uncomplicated pregnancies, respectively. Decreasing TAGLN expression in GDM-exposed ECFCs improved network formation and stability as well as increased migration. In contrast, overexpressing TAGLN in ECFCs from uncomplicated pregnancies decreased network formation, network stability, migration, and alignment to laminar flow. Overall, these data suggest that increased TAGLN likely contributes to the vasculogenic dysfunction observed in GDM-exposed ECFCs, as it impairs ECFC migration, cell alignment, and network formation. Identifying the molecular mechanisms underlying fetal ECFC dysfunction following GDM exposure is key to ascertain mechanistically the basis for cardiovascular disease predisposition later in life