Correcting for Sampling Error in between-Cluster Effects: An Empirical Bayes Cluster-Mean Approach with Finite Population Corrections

With clustered data, such as where students are nested within schools or employees are nested within organizations, it is often of interest to estimate and compare associations among variables separately for each level. While researchers routinely estimate between-cluster effects using the sample cluster means of a predictor, previous research has shown that such practice leads to biased estimates of coefficients at the between level, and recent research has recommended the use of latent cluster means with the multilevel structural equation modeling framework. However, the latent cluster mean approach may not always be the best choice as it (a) relies on the assumption that the population cluster sizes are close to infinite, (b) requires a relatively large number of clusters, and (c) is currently only implemented in specialized software such as Mplus. In this paper, we show how using empirical Bayes estimates of the cluster means can also lead to consistent estimates of between-level coefficients, and illustrate how the empirical Bayes estimate can incorporate finite population corrections when information on population cluster sizes is available. Through a series of Monte Carlo simulation studies, we show that the empirical Bayes cluster-mean approach performs similarly to the latent cluster mean approach for estimating the between-cluster coefficients in most conditions when the infinite-population assumption holds, and applying the finite population correction provides reasonable point and interval estimates when the population is finite. The performance of EBM can be further improved with restricted maximum likelihood estimation and likelihood-based confidence intervals. We also provide an R function that implements the empirical Bayes cluster-mean approach, and illustrate it using data from the classic High School and Beyond Study.</p

Interpretable Dynamic Treatment Regimes

<p>Precision medicine is currently a topic of great interest in clinical and intervention science.  A key component of precision medicine is that it is evidence-based, that is, data-driven, and consequently there has been tremendous interest in estimation of precision medicine strategies using observational or randomized study data. One way to formalize precision medicine is through a treatment regime, which is a sequence of decision rules, one per stage of clinical intervention, that map up-to-date patient information to a recommended treatment. An optimal treatment regime is defined as maximizing the mean of some cumulative clinical outcome if applied to a population of interest. It is well-known that even under simple generative models an optimal treatment regime can be a highly nonlinear function of patient information. Consequently, a focal point of recent methodological research has been the development of flexible models for estimating optimal treatment regimes. However, in many settings, estimation of an optimal treatment regime is an exploratory analysis intended to generate new hypotheses for subsequent research and not to directly dictate treatment to new patients. In such settings, an estimated treatment regime that is interpretable in a domain context may be of greater value than an unintelligible treatment regime built using “black-box” estimation methods. We propose an estimator of an optimal treatment regime composed of a sequence of decision rules, each expressible as a list of “if-then” statements that can be presented as either a paragraph or as a simple flowchart that is immediately interpretable to domain experts. The discreteness of these lists precludes smooth, that is, gradient-based, methods of estimation and leads to nonstandard asymptotics. Nevertheless, we provide a computationally efficient estimation algorithm, prove consistency of the proposed estimator, and derive rates of convergence. We illustrate the proposed methods using a series of simulation examples and application to data from a sequential clinical trial on bipolar disorder. Supplementary materials for this article are available online.</p

Additional file 1 of Association between the triglyceride-glucose index and carotid plaque incidence: a longitudinal study

Additional file 1: Table S1. Baseline characteristics of the included and excluded populations. Table S2. Sensitivity analysis on the association between TyG index and carotid plaque

DataSheet_3_An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer.docx

BackgroundOvarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust.Methods420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed.Results64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes.ConclusionOur study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.</p

Additional file 1: of Comparison of efficacy between anti-vascular endothelial growth factor (VEGF) and laser treatment in Type-1 and threshold retinopathy of prematurity (ROP)

Electronic search strategy record. (DOCX 12 kb

DataSheet_1_An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer.pdf

BackgroundOvarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust.Methods420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed.Results64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes.ConclusionOur study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.</p

DataSheet_2_An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer.xlsx

BackgroundOvarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust.Methods420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed.Results64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes.ConclusionOur study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.</p

A Hydrogel Electrolyte toward a Flexible Zinc-Ion Battery and Multifunctional Health Monitoring Electronics

The compact design of an environmentally adaptive battery and effectors forms the foundation for wearable electronics capable of time-resolved, long-term signal monitoring. Herein, we present a one-body strategy that utilizes a hydrogel as the ionic conductive medium for both flexible aqueous zinc-ion batteries and wearable strain sensors. The poly(vinyl alcohol) hydrogel network incorporates nano-SiO2 and cellulose nanofibers (referred to as PSC) in an ethylene glycol/water mixed solvent, balancing the mechanical properties (tensile strength of 6 MPa) and ionic diffusivity at −20 °C (2 orders of magnitude higher than 2 M ZnCl2 electrolyte). Meanwhile, cathode lattice breathing during the solvated Zn2+ intercalation and dendritic Zn protrusion at the anode interface are mitigated. Besides the robust cyclability of the Zn∥PSC∥V2O5 prototype within a wide temperature range (from −20 to 80 °C), this microdevice seamlessly integrates a zinc-ion battery with a strain sensor, enabling precise monitoring of the muscle response during dynamic body movement. By employing transmission-mode operando XRD, the self-powered sensor accurately documents the real-time phasic evolution of the layered cathode and synchronized strain change induced by Zn deposition, which presents a feasible solution of health monitoring by the miniaturized electronics

A Hydrogel Electrolyte toward a Flexible Zinc-Ion Battery and Multifunctional Health Monitoring Electronics

The compact design of an environmentally adaptive battery and effectors forms the foundation for wearable electronics capable of time-resolved, long-term signal monitoring. Herein, we present a one-body strategy that utilizes a hydrogel as the ionic conductive medium for both flexible aqueous zinc-ion batteries and wearable strain sensors. The poly(vinyl alcohol) hydrogel network incorporates nano-SiO2 and cellulose nanofibers (referred to as PSC) in an ethylene glycol/water mixed solvent, balancing the mechanical properties (tensile strength of 6 MPa) and ionic diffusivity at −20 °C (2 orders of magnitude higher than 2 M ZnCl2 electrolyte). Meanwhile, cathode lattice breathing during the solvated Zn2+ intercalation and dendritic Zn protrusion at the anode interface are mitigated. Besides the robust cyclability of the Zn∥PSC∥V2O5 prototype within a wide temperature range (from −20 to 80 °C), this microdevice seamlessly integrates a zinc-ion battery with a strain sensor, enabling precise monitoring of the muscle response during dynamic body movement. By employing transmission-mode operando XRD, the self-powered sensor accurately documents the real-time phasic evolution of the layered cathode and synchronized strain change induced by Zn deposition, which presents a feasible solution of health monitoring by the miniaturized electronics

A Hydrogel Electrolyte toward a Flexible Zinc-Ion Battery and Multifunctional Health Monitoring Electronics

The compact design of an environmentally adaptive battery and effectors forms the foundation for wearable electronics capable of time-resolved, long-term signal monitoring. Herein, we present a one-body strategy that utilizes a hydrogel as the ionic conductive medium for both flexible aqueous zinc-ion batteries and wearable strain sensors. The poly(vinyl alcohol) hydrogel network incorporates nano-SiO2 and cellulose nanofibers (referred to as PSC) in an ethylene glycol/water mixed solvent, balancing the mechanical properties (tensile strength of 6 MPa) and ionic diffusivity at −20 °C (2 orders of magnitude higher than 2 M ZnCl2 electrolyte). Meanwhile, cathode lattice breathing during the solvated Zn2+ intercalation and dendritic Zn protrusion at the anode interface are mitigated. Besides the robust cyclability of the Zn∥PSC∥V2O5 prototype within a wide temperature range (from −20 to 80 °C), this microdevice seamlessly integrates a zinc-ion battery with a strain sensor, enabling precise monitoring of the muscle response during dynamic body movement. By employing transmission-mode operando XRD, the self-powered sensor accurately documents the real-time phasic evolution of the layered cathode and synchronized strain change induced by Zn deposition, which presents a feasible solution of health monitoring by the miniaturized electronics