scIALM: A method for sparse scRNA-seq expression matrix imputation using the Inexact Augmented Lagrange Multiplier with low error

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.

Network-based analysis of heterogeneous patient-matched brain and extracranial melanoma metastasis pairs reveals three homogeneous subgroups

Melanoma, the deadliest form of skin cancer, can metastasize to different organs. Molecular differences between brain and extracranial melanoma metastases are poorly understood. Here, promoter methylation and gene expression of 11 heterogeneous patient-matched pairs of brain and extracranial metastases were analyzed using melanoma-specific gene regulatory networks learned from public transcriptome and methylome data followed by network-based impact propagation of patient-specific alterations. This innovative data analysis strategy allowed to predict potential impacts of patient-specific driver candidate genes on other genes and pathways. The patient-matched metastasis pairs clustered into three robust subgroups with specific downstream targets with known roles in cancer, including melanoma (SG1: RBM38, BCL11B, SG2: GATA3, FES, SG3: SLAMF6, PYCARD). Patient subgroups and ranking of target gene candidates were confirmed in a validation cohort. Summarizing, computational network-based impact analyses of heterogeneous metastasis pairs predicted individual regulatory differences in melanoma brain metastases, cumulating into three consistent subgroups with specific downstream target genes.

Regularization, early-stopping and dreaming: A Hopfield-like setup to address generalization and overfitting

In this work we approach attractor neural networks from a machine learning perspective: we look for optimal network parameters by applying a gradient descent over a regularized loss function. Within this framework, the optimal neuron-interaction matrices turn out to be a class of matrices which correspond to Hebbian kernels revised by a reiterated unlearning protocol. Remarkably, the extent of such unlearning is proved to be related to the regularization hyperparameter of the loss function and to the training time. Thus, we can design strategies to avoid overfitting that are formulated in terms of regularization and early-stopping tuning. The generalization capabilities of these attractor networks are also investigated: analytical results are obtained for random synthetic datasets, next, the emerging picture is corroborated by numerical experiments that highlight the existence of several regimes (i.e., overfitting, failure and success) as the dataset parameters are varied.

Dimensionality of Oppositional Defiant Disorder Symptoms Across Elementary-School Grades

Various models of the dimensionality of behaviors associated with Oppositional Defiant Disorder (ODD) have been proposed or reported. Many of these models describe ODD-related behaviors in either two- or three-factor models. The purpose of the study was to determine which of the models of ODD-related behaviors demonstrated the best fit using teacher report of 15,521 children across eight grade levels and to examine measurement invariance of the model across grades. Confirmatory factor analyses were conducted to determine which of the models demonstrated best fit of teacher-reported ODD-related behaviors across eight grades. A two-factor model from a preliminary analysis of a subset of the current data demonstrated a better model fit than any of the existing six models examined and demonstrated measurement invariance across all grades. Across all of the models, affective and behavioral symptoms loaded onto separate factors, which may be an important consideration to inform future clinical and empirical work.

Mapping potential pathways from polygenic liability through brain structure to psychological problems across the transition to adolescence

BACKGROUND

We used a polygenic score for externalizing behavior (extPGS) and structural MRI to examine potential pathways from genetic liability to conduct problems via the brain across the adolescent transition.

METHODS

Three annual assessments of child conduct problems, attention-deficit/hyperactivity problems, and internalizing problems were conducted across across 9-13 years of age among 4,475 children of European ancestry in the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®).

RESULTS

The extPGS predicted conduct problems in each wave (R2 = 2.0%-2.9%). Bifactor models revealed that the extPRS predicted variance specific to conduct problems (R2 = 1.7%-2.1%), but also variance that conduct problems shared with other measured problems (R2 = .8%-1.4%). Longitudinally, extPGS predicted levels of specific conduct problems (R2 = 2.0%), but not their slope of change across age. The extPGS was associated with total gray matter volume (TGMV; R2 = .4%) and lower TGMV predicted both specific conduct problems (R2 = 1.7%-2.1%) and the variance common to all problems in each wave (R2 = 1.6%-3.1%). A modest proportion of the polygenic liability specific to conduct problems in each wave was statistically mediated by TGMV.

CONCLUSIONS

Across the adolescent transition, the extPGS predicted both variance specific to conduct problems and variance shared by all measured problems. The extPGS also was associated with TGMV, which robustly predicted conduct problems. Statistical mediation analyses suggested the hypothesis that polygenic variation influences individual differences in brain development that are related to the likelihood of conduct problems during the adolescent transition, justifying new research to test this causal hypothesis.

Structured learning in time-dependent Cox models

Cox models with time-dependent coefficients and covariates are widely used in survival analysis. In high-dimensional settings, sparse regularization techniques are employed for variable selection, but existing methods for time-dependent Cox models lack flexibility in enforcing specific sparsity patterns (ie, covariate structures). We propose a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules. Our method can adapt to arbitrary grouping structures, including interaction selection, temporal, spatial, tree, and directed acyclic graph structures. It achieves accurate estimation with low false alarm rates. We develop the sox package, implementing a network flow algorithm for efficiently solving models with complex covariate structures. sox offers a user-friendly interface for specifying grouping structures and delivers fast computation. Through examples, including a case study on identifying predictors of time to all-cause death in atrial fibrillation patients, we demonstrate the practical application of our method with specific selection rules.

A systematic review and meta-analysis of the diagnostic accuracy of the neutrophil-to-lymphocyte ratio and the platelet-to-lymphocyte ratio in systemic lupus erythematosus

The wide range of clinical and serological manifestations in systemic lupus erythematosus (SLE) and the lack of accepted diagnostic criteria warrant the identification of novel, more accurate biomarkers. Hematological indices derived from full blood cell counts, particularly the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR), have shown promise in SLE; however, a critical appraisal of their diagnostic accuracy is lacking. We sought to address this issue by conducting a systematic review and meta-analysis of the diagnostic accuracy of the NLR and PLR in SLE. The electronic databases PubMed, Scopus, and Web of Science were systematically searched from inception to 15 March 2024 for studies reporting the sensitivity and specificity of the NLR and PLR, obtained by receiver operating characteristic (ROC) curve analysis, for the presence of SLE, disease severity, organ involvement (lupus nephritis, pericarditis, and pleural disease), and complications (infections). The risk of bias was assessed using the JBI Critical Appraisal Checklist (PROSPERO registration number: CRD42024531446). The NLR exhibited good accuracy for the diagnosis of SLE (eight studies; area under the curve, AUC = 0.81, 95% CI 0.78-0.85) and lupus nephritis (nine studies; AUC = 0.81, 95% CI 0.77-0.84), but not for severe disease (nine studies; AUC = 0.69, 95% CI 0.65-0.73) or infections (six studies; AUC = 0.73, 95% CI 0.69-0.77). The PLR exhibited good accuracy for the diagnosis of severe disease (six studies; AUC = 0.85, 95% CI 0.81-0.87). There were an insufficient number of studies to assess the accuracy of the PLR for the diagnosis of SLE, lupus nephritis, or infections. No study investigated the NLR and PLR in SLE patients with pericarditis or pleural disease. Therefore, the NLR and the PLR have a relatively high diagnostic accuracy for the presence of SLE and lupus nephritis (NLR) and severe disease (PLR). Further studies are warranted to determine whether the NLR and PLR, in combination with clinical evaluation and other serological biomarkers, can enhance the diagnosis and management of SLE.

Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics

BACKGROUND

Data from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternatives, they may not always be suitable for health frameworks.

OBJECTIVE

This paper aims to support researchers and data custodians in three ways: (1) providing a concise overview of the literature on statistical inference methods for horizontally partitioned data; (2) describing the methods applicable to generalized linear models (GLM) and assessing their underlying distributional assumptions; (3) adapting existing methods to make them fully usable in health settings.

METHODS

A scoping review methodology was employed for the literature mapping, from which methods presenting a methodological framework for GLM analyses with horizontally partitioned data were identified and assessed from the perspective of applicability in health settings. Statistical theory was used to adapt methods and to derive the properties of the resulting estimators.

RESULTS

From the review, 41 articles were selected, and six approaches were extracted for conducting standard GLM-based statistical analysis. However, these approaches assumed evenly and identically distributed data across nodes. Consequently, statistical procedures were derived to accommodate uneven node sample sizes and heterogeneous data distributions across nodes. Workflows and detailed algorithms were developed to highlight information-sharing requirements and operational complexity.

CONCLUSIONS

This paper contributes to the field of health analytics by providing an overview of the methods that can be used with horizontally partitioned data, by adapting these methods to the context of heterogeneous health data and by clarifying the workflows and quantities exchanged by the methods discussed. Further analysis of the confidentiality preserved by these methods is needed to fully understand the risk associated with the sharing of summary statistics.

Estimation of Personal Symptom Networks Using the Ising Model for Adult Survivors of Childhood Cancer: A Simulation Study with Real-World Data Application

Purpose

Childhood cancer survivors experience interconnected symptoms, patterns of which can be elucidated by network analysis. However, current symptom networks are constructed based on the average survivors without considering individual heterogeneities. We propose to evaluate personal symptom network estimation using the Ising model with covariates through simulations and estimate personal symptom network for adult childhood cancer survivors.

Patients and Methods

We adopted the Ising model with covariates to construct networks by employing logistic regressions for estimating associations between binary symptoms. Simulation experiments assessed the robustness of this method in constructing personal symptom network. Real-world data illustration included 1708 adult childhood cancer survivors from the St. Jude Lifetime Cohort Study (SJLIFE), a retrospective cohort study with prospective follow-up to characterize the etiology and late effects for childhood cancer survivors. Patients' baseline symptoms in 10 domains (cardiac, pulmonary, sensation, nausea, movement, pain, memory, fatigue, anxiety, depression) and individual characteristics (age, sex, race/ethnicity, attained education, personal income, and marital status) were self-reported using survey. Treatment variables (any chemo or radiation therapy) were obtained from medical records. Personal symptom network of 10 domains was estimated using the Ising model, incorporating individual characteristics and treatment data.

Results

Simulations confirmed the robustness of the Ising model with covariates in constructing personal symptom networks. Real-world data analysis identified age, sex, race/ethnicity, education, marital status, and treatment (any chemo and radiation therapy) as major factors influencing symptom co-occurrence. Older childhood cancer survivors showed stronger cardiac-fatigue associations. Survivors of racial/ethnic minorities had stronger pain-fatigue associations. Female survivors with above-college education demonstrated stronger pain-anxiety associations. Unmarried survivors who received radiation had stronger association between movement and memory problems.

Conclusion

The Ising model with covariates accurately estimates personal symptom networks. Individual heterogeneities exist in symptom co-occurrence patterns for childhood cancer survivors. The estimated personal symptom network offers insights into interconnected symptom experiences.

Causal mediation analysis with a three-dimensional image mediator

Causal mediation analysis is increasingly abundant in biology, psychology, and epidemiology studies and so forth. In particular, with the advent of the big data era, the issue of high-dimensional mediators is becoming more prevalent. In neuroscience, with the widespread application of magnetic resonance technology in the field of brain imaging, studies on image being a mediator emerged. In this study, a novel causal mediation analysis method with a three-dimensional image mediator is proposed. We define the average casual effects under the potential outcome framework, explore several sufficient conditions for the valid identification, and develop techniques for estimation and inference. To verify the effectiveness of the proposed method, a series of simulations under various scenarios is performed. Finally, the proposed method is applied to a study on the causal effect of mother's delivery mode on child's IQ development. It is found that cesarean section may have a negative effect on intellectual performance and that this effect is mediated by white matter development. Additional prospective and longitudinal studies may be necessary to validate these emerging findings.

Association of diet with per- and polyfluoroalkyl substances in plasma and human milk in the New Hampshire Birth Cohort Study

Per- and polyfluoroalkyl substances (PFAS) are related to various adverse health outcomes, and food is a common source of PFAS exposure. Dietary sources of PFAS have not been adequately explored among U.S. pregnant individuals. We examined associations of dietary factors during pregnancy with PFAS concentrations in maternal plasma and human milk in the New Hampshire Birth Cohort Study. PFAS concentrations, including perfluorohexane sulfonate (PFHxS), perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), perfluorononanoate (PFNA), and perfluorodecanoate (PFDA), were measured in maternal plasma collected at ∼28 gestational weeks and human milk collected at ∼6 postpartum weeks. Sociodemographic, lifestyle and reproductive factors were collected from prenatal questionnaires and diet from food frequency questionnaires at ∼28 gestational weeks. We used adaptive elastic net (AENET) to identify important dietary variables for PFAS concentrations. We used multivariable linear regression to assess associations of dietary variables selected by AENET models with PFAS concentrations. Models were adjusted for sociodemographic, lifestyle, and reproductive factors, as well as gestational week of blood sample collection (plasma PFAS), postpartum week of milk sample collection (milk PFAS), and enrollment year. A higher intake of fish/seafood, eggs, coffee, or white rice during pregnancy was associated with higher plasma or milk PFAS concentrations. For example, every 1 standard deviation (SD) servings/day increase in egg intake during pregnancy was associated with 4.4 % (95 % CI: 0.6, 8.4), 3.3 % (0.1, 6.7), and 10.3 % (5.6, 15.2) higher plasma PFOS, PFOA, and PFDA concentrations respectively. Similarly, every 1 SD servings/day increase in white rice intake during pregnancy was associated with 7.5 % (95 % CI: -0.2, 15.8) and 12.4 % (4.8, 20.5) greater milk PFOS and PFOA concentrations, respectively. Our study suggests that certain dietary factors during pregnancy may contribute to higher PFAS concentrations in maternal plasma and human milk, which could inform interventions to reduce PFAS exposure for both birthing people and offspring.

Omics feature selection with the extended SIS R package: identification of a body mass index epigenetic multimarker in the Strong Heart Study

The statistical analysis of omics data poses a great computational challenge given their ultra-high-dimensional nature and frequent between-features correlation. In this work, we extended the iterative sure independence screening (ISIS) algorithm by pairing ISIS with elastic-net (Enet) and 2 versions of adaptive elastic-net (adaptive elastic-net (AEnet) and multistep adaptive elastic-net (MSAEnet)) to efficiently improve feature selection and effect estimation in omics research. We subsequently used genome-wide human blood DNA methylation data from American Indian participants in the Strong Heart Study (n = 2235 participants; measured in 1989-1991) to compare the performance (predictive accuracy, coefficient estimation, and computational efficiency) of ISIS-paired regularization methods with that of a bayesian shrinkage and traditional linear regression to identify an epigenomic multimarker of body mass index (BMI). ISIS-AEnet outperformed the other methods in prediction. In biological pathway enrichment analysis of genes annotated to BMI-related differentially methylated positions, ISIS-AEnet captured most of the enriched pathways in common for at least 2 of all the evaluated methods. ISIS-AEnet can favor biological discovery because it identifies the most robust biological pathways while achieving an optimal balance between bias and efficient feature selection. In the extended SIS R package, we also implemented ISIS paired with Cox and logistic regression for time-to-event and binary endpoints, respectively, and a bootstrap approach for the estimation of regression coefficients.

Sparse spectral graph analysis and its application to gastric cancer drug resistance-specific molecular interplays identification

Uncovering acquired drug resistance mechanisms has garnered considerable attention as drug resistance leads to treatment failure and death in patients with cancer. Although several bioinformatics studies developed various computational methodologies to uncover the drug resistance mechanisms in cancer chemotherapy, most studies were based on individual or differential gene expression analysis. However the single gene-based analysis is not enough, because perturbations in complex molecular networks are involved in anti-cancer drug resistance mechanisms. The main goal of this study is to reveal crucial molecular interplay that plays key roles in mechanism underlying acquired gastric cancer drug resistance. To uncover the mechanism and molecular characteristics of drug resistance, we propose a novel computational strategy that identified the differentially regulated gene networks. Our method measures dissimilarity of networks based on the eigenvalues of the Laplacian matrix. Especially, our strategy determined the networks' eigenstructure based on sparse eigen loadings, thus, the only crucial features to describe the graph structure are involved in the eigenanalysis without noise disturbance. We incorporated the network biology knowledge into eigenanalysis based on the network-constrained regularization. Therefore, we can achieve a biologically reliable interpretation of the differentially regulated gene network identification. Monte Carlo simulations show the outstanding performances of the proposed methodology for differentially regulated gene network identification. We applied our strategy to gastric cancer drug-resistant-specific molecular interplays and related markers. The identified drug resistance markers are verified through the literature. Our results suggest that the suppression and/or induction of COL4A1, PXDN and TGFBI and their molecular interplays enriched in the Extracellular-related pathways may provide crucial clues to enhance the chemosensitivity of gastric cancer. The developed strategy will be a useful tool to identify phenotype-specific molecular characteristics that can provide essential clues to uncover the complex cancer mechanism.

BHAFT: Bayesian heredity-constrained accelerated failure time models for detecting gene-environment interactions in survival analysis

In addition to considering the main effects, understanding gene-environment (G × E) interactions is imperative for determining the etiology of diseases and the factors that affect their prognosis. In the existing statistical framework for censored survival outcomes, there are several challenges in detecting G × E interactions, such as handling high-dimensional omics data, diverse environmental factors, and algorithmic complications in survival analysis. The effect heredity principle has widely been used in studies involving interaction identification because it incorporates the dependence of the main and interaction effects. However, Bayesian survival models that incorporate the assumption of this principle have not been developed. Therefore, we propose Bayesian heredity-constrained accelerated failure time (BHAFT) models for identifying main and interaction (M-I) effects with novel spike-and-slab or regularized horseshoe priors to incorporate the assumption of effect heredity principle. The R package rstan was used to fit the proposed models. Extensive simulations demonstrated that BHAFT models had outperformed other existing models in terms of signal identification, coefficient estimation, and prognosis prediction. Biologically plausible G × E interactions associated with the prognosis of lung adenocarcinoma were identified using our proposed model. Notably, BHAFT models incorporating the effect heredity principle could identify both main and interaction effects, which are highly useful in exploring G × E interactions in high-dimensional survival analysis. The code and data used in our paper are available at https://github.com/SunNa-bayesian/BHAFT.

A new type of generalized information criterion for regularization parameter selection in penalized regression with application to treatment process data

We propose a new approach to select the regularization parameter using a new version of the generalized information criterion (GIC ) in the subject of penalized regression. We prove the identifiability of bridge regression model as a prerequisite of statistical modeling. Then, we propose asymptotically efficient generalized information criterion (AGIC ) and prove that it has asymptotic loss efficiency. Also, we verified the better performance of AGIC in comparison to the older versions of GIC . Furthermore, we propose MSE search paths to order the selected features by lasso regression based on numerical studies. The MSE search paths provide a way to cover the lack of feature ordering in lasso regression model. The performance of AGIC with other types of GIC is compared using MSE and model utility in simulation study. We exert AGIC and other criteria to analyze breast and prostate cancer and Parkinson disease datasets. The results confirm the superiority of AGIC in almost all situations.

Explaining neural scaling laws

The population loss of trained deep neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains the origins of and connects these scaling laws. We identify variance-limited and resolution-limited scaling behavior for both dataset and model size, for a total of four scaling regimes. The variance-limited scaling follows simply from the existence of a well-behaved infinite data or infinite width limit, while the resolution-limited regime can be explained by positing that models are effectively resolving a smooth data manifold. In the large width limit, this can be equivalently obtained from the spectrum of certain kernels, and we present evidence that large width and large dataset resolution-limited scaling exponents are related by a duality. We exhibit all four scaling regimes in the controlled setting of large random feature and pretrained models and test the predictions empirically on a range of standard architectures and datasets. We also observe several empirical relationships between datasets and scaling exponents under modifications of task and architecture aspect ratio. Our work provides a taxonomy for classifying different scaling regimes, underscores that there can be different mechanisms driving improvements in loss, and lends insight into the microscopic origin and relationships between scaling exponents.

Exploring Effects of Age on Conflict Processing in the Light of Practice in a Large-Scale Dataset

INTRODUCTION

The possible decline of cognitive functions with age has been in the focus of cognitive research in the last decades. The present study investigated effects of aging on conflict processing in a big dataset of a Stroop-inspired online training task.

METHODS

We focused on the temporal dynamics of conflict processing in the light of task practice by means of inspecting delta plots and Lorenz-interference curves to gain insights on a process level.

RESULTS

The results indicate a relatively constant increase of cognitive conflict over the course of adulthood and a decrease with practice. Furthermore, the latency of the automatic processing of conflicting information relative to the controlled processing of task-relevant information decreases relatively constantly with age. This effect is moderated by practice, that is, the relative latency of the automatic processing decreases less with age at high practice levels.

CONCLUSION

As such, practice seems to be able to partially counteract age-related differences in conflict processing, on a process level.

Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms

Despite growing interest in estimating individualized treatment rules, little attention has been given the binary outcome setting. Estimation is challenging with nonlinear link functions, especially when variable selection is needed. We use a new computational approach to solve a recently proposed doubly robust regularized estimating equation to accomplish this difficult task in a case study of depression treatment. We demonstrate an application of this new approach in combination with a weighted and penalized estimating equation to this challenging binary outcome setting. We demonstrate the double robustness of the method and its effectiveness for variable selection. The work is motivated by and applied to an analysis of treatment for unipolar depression using a population of patients treated at Kaiser Permanente Washington.

Evaluating associations between trauma-related characteristics and functional recovery in individuals with spinal cord injury

OBJECTIVE

To determine the associations between trauma variables, acute phase-related variables, and patient-level characteristics with functional recovery during inpatient rehabilitation for individuals with spinal cord injury (SCI). The associations were evaluated by linking individuals' records between the Pennsylvania Trauma Systems Outcomes Study and the National SCI Model Systems databases.

DESIGN

Retrospective cohort analysis.

SETTING

Two SCI Model Centers in Pennsylvania, United States.

METHODS

We used a record linkage toolkit in Python to link 735 individuals with traumatic SCI between the databases. The percentage for true-match and error were 92.0% and 0.1%, respectively. The functional recovery during inpatient rehabilitation was determined in 604 individuals with SCI by ordinary least squares regression (OLS) and gradient boosting regression (GBR) analyses.

RESULTS

The OLS and GBR analyses indicated older age, greater impairment (SCI level combined with American Spinal Injury Association impairment scale), presence of diabetes mellitus, pulmonary complications during acute care, and longer length of stay at an inpatient rehabilitation facility were associated with lower functional recovery (OLS R2 = 0.56 and GBR R2 = 0.58).

CONCLUSIONS

Trauma and acute care variables in addition to patient characteristics were associated with functional recovery during inpatient rehabilitation in individuals with SCI. Further investigation is needed to understand the role of diabetes mellitus and pulmonary complications, which have not been previously associated with functional recovery in individuals with SCI.

Multilevel Intervention Stepped Wedge Designs (MLI-SWDs)

Multilevel interventions (MLIs) hold promise for reducing health inequities by intervening at multiple types of social determinants of health consistent with the socioecological model of health. In spite of their potential, methodological challenges related to study design compounded by a lack of tools for sample size calculation inhibit their development. We help address this gap by proposing the Multilevel Intervention Stepped Wedge Design (MLI-SWD), a hybrid experimental design which combines cluster-level (CL) randomization using a Stepped Wedge design (SWD) with independent individual-level (IL) randomization. The MLI-SWD is suitable for MLIs where the IL intervention has a low risk of interference between individuals in the same cluster, and it enables estimation of the component IL and CL treatment effects, their interaction, and the combined intervention effect. The MLI-SWD accommodates cross-sectional and cohort designs as well as both incomplete (clusters are not observed in every study period) and complete observation patterns. We adapt recent work using generalized estimating equations for SWD sample size calculation to the multilevel setting and provide an R package for power and sample size calculation. Furthermore, motivated by our experiences with the ongoing NC Works 4 Health study, we consider how to apply the MLI-SWD when individuals join clusters over the course of the study. This situation arises when unemployment MLIs include IL interventions that are delivered while the individual is unemployed. This extension requires carefully considering whether the study interventions will satisfy additional causal assumptions but could permit randomization in new settings.

Bayesian semiparametric Markov renewal mixed models for vocalization syntax

Speech and language play an important role in human vocal communication. Studies have shown that vocal disorders can result from genetic factors. In the absence of high-quality data on humans, mouse vocalization experiments in laboratory settings have been proven useful in providing valuable insights into mammalian vocal development, including especially the impact of certain genetic mutations. Such data sets usually consist of categorical syllable sequences along with continuous intersyllable interval (ISI) times for mice of different genotypes vocalizing under different contexts. ISIs are of particular importance as increased ISIs can be an indication of possible vocal impairment. Statistical methods for properly analyzing ISIs along with the transition probabilities have however been lacking. In this article, we propose a class of novel Markov renewal mixed models that capture the stochastic dynamics of both state transitions and ISI lengths. Specifically, we model the transition dynamics and the ISIs using Dirichlet and gamma mixtures, respectively, allowing the mixture probabilities in both cases to vary flexibly with fixed covariate effects as well as random individual-specific effects. We apply our model to analyze the impact of a mutation in the Foxp2 gene on mouse vocal behavior. We find that genotypes and social contexts significantly affect the length of ISIs but, compared to previous analyses, the influences of genotype and social context on the syllable transition dynamics are weaker.

Recovery Trajectories Following Complex Elbow Injuries and Radial Head Arthroplasty: A Longitudinal Study Over 8 Years

PURPOSE

Radial head arthroplasty (RHA) is commonly performed to manage comminuted unreconstructible radial head fractures. Although the outcomes of RHA are often satisfactory, revisions are usually considered when pain intensity is higher than expected. Therefore, it is important to investigate the recovery trajectories of patients following RHA over an extended period and the characteristics that may lead to unfavorable outcomes.

METHODS

The Patient-Rated Elbow Evaluation (PREE) was used to assess recovery in 94 patients at baseline (within 2-7 days after surgery); 3 and 6 months; and 1, 2, 3, 4, 5, and 8 years after RHA. Lower PREE values indicate lower pain and disability. Latent growth curve analysis was used to determine classes of recovery. The characteristics of the participants in the identified recovery trajectory classes were then compared.

RESULTS

Two distinct recovery trajectories were identified: optimal and suboptimal recoveries. Most patients (84%) belonged to the optimal recovery class, which exhibited significantly lower baseline PREE scores, a consistent pattern of recovery, and a relatively high rate of change. Patients in the suboptimal recovery class (16%) had significantly higher baseline PREE scores and continued to experience relatively higher levels of pain and disability for the duration of the study; their rate of recovery was much slower. Patients belonging to the 2 recovery trajectories did not differ based on age or sex. Although we had low power in other variables, a qualitative exploration showed that the number of current or previous smokers was higher in the suboptimal recovery trajectory class.

CONCLUSIONS

In this longitudinal cohort study, we show that high postsurgical pain and disability, and potentially smoking, may adversely affect the recovery trajectory following RHA. Clinicians are recommended to assess these potential factors while considering revision surgeries.

TYPE OF STUDY/LEVEL OF EVIDENCE

Prognostic IV.

Mediation analysis using incomplete information from publicly available data sources

Our work was motivated by the question whether, and to what extent, well-established risk factors mediate the racial disparity observed for colorectal cancer (CRC) incidence in the United States. Mediation analysis examines the relationships between an exposure, a mediator and an outcome. All available methods require access to a single complete data set with these three variables. However, because population-based studies usually include few non-White participants, these approaches have limited utility in answering our motivating question. Recently, we developed novel methods to integrate several data sets with incomplete information for mediation analysis. These methods have two limitations: (i) they only consider a single mediator and (ii) they require a data set containing individual-level data on the mediator and exposure (and possibly confounders) obtained by independent and identically distributed sampling from the target population. Here, we propose a new method for mediation analysis with several different data sets that accommodates complex survey and registry data, and allows for multiple mediators. The proposed approach yields unbiased causal effects estimates and confidence intervals with nominal coverage in simulations. We apply our method to data from U.S. cancer registries, a U.S.-population-representative survey and summary level odds-ratio estimates, to rigorously evaluate what proportion of the difference in CRC risk between non-Hispanic Whites and Blacks is mediated by three potentially modifiable risk factors (CRC screening history, body mass index, and regular aspirin use).

Online causal inference with application to near real-time post-market vaccine safety surveillance

Streaming data routinely generated by social networks, mobile or web applications, e-commerce, and electronic health records present new opportunities to monitor the impact of an intervention on an outcome via causal inference methods. However, most existing causal inference methods have been focused on and applied to static data, that is, a fixed data set in which observations are pooled and stored before performing statistical analysis. There is thus a pressing need to turn static causal inference into online causal learning to support near real-time monitoring of treatment effects. In this paper, we present a framework for online estimation and inference of treatment effects that can incorporate new information as it becomes available without revisiting prior observations. We show that, under mild regularity conditions, the proposed online estimator is asymptotically equivalent to the offline oracle estimator obtained by pooling all data. Our proposal is motivated by the need for near real-time vaccine effectiveness and safety monitoring, and our proposed method is applied to a case study on COVID-19 vaccine safety surveillance.

An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma

Asthma is a disease characterized by chronic airway hyperresponsiveness and inflammation, with signs of variable airflow limitation and impaired lung function leading to respiratory symptoms such as shortness of breath, chest tightness and cough. Eosinophilic asthma is a distinct phenotype that affects more than half of patients diagnosed with severe asthma. It can be effectively treated with monoclonal antibodies targeting specific immunological signaling pathways that fuel the inflammation underlying the disease, particularly Interleukin-5 (IL-5), a cytokine that plays a crucial role in asthma. In this study, we propose a data analysis pipeline aimed at identifying subphenotypes of severe eosinophilic asthma in relation to response to therapy at follow-up, which could have great potential for use in routine clinical practice. Once an optimal partition of patients into subphenotypes has been determined, the labels indicating the group to which each patient has been assigned are used in a novel way. For each input variable in a specialized logistic regression model, a clusterwise effect on response to therapy is determined by an appropriate interaction term between the input variable under consideration and the cluster label. We show that the clusterwise odds ratios can be meaningfully interpreted conditional on the cluster label. In this way, we can define an effect measure for the response variable for each input variable in each of the groups identified by the clustering algorithm, which is not possible in standard logistic regression because the effect of the reference class is aliased with the overall intercept. The interpretability of the model is enforced by promoting sparsity, a goal achieved by learning interactions in a hierarchical manner using a special group-Lasso technique. In addition, valid expressions are provided for computing odds ratios in the unusual parameterization used by the sparsity-promoting algorithm. We show how to apply the proposed data analysis pipeline to the problem of sub-phenotyping asthma patients also in terms of quality of response to therapy with monoclonal antibodies.

asteRIa enables robust interaction modeling between chromatin modifications and epigenetic readers

Chromatin, the nucleoprotein complex consisting of DNA and histone proteins, plays a crucial role in regulating gene expression by controlling access to DNA. Chromatin modifications are key players in this regulation, as they help to orchestrate DNA transcription, replication, and repair. These modifications recruit epigenetic 'reader' proteins, which mediate downstream events. Most modifications occur in distinctive combinations within a nucleosome, suggesting that epigenetic information can be encoded in combinatorial chromatin modifications. A detailed understanding of how multiple modifications cooperate in recruiting such proteins has, however, remained largely elusive. Here, we integrate nucleosome affinity purification data with high-throughput quantitative proteomics and hierarchical interaction modeling to estimate combinatorial effects of chromatin modifications on protein recruitment. This is facilitated by the computational workflow asteRIa which combines hierarchical interaction modeling, stability-based model selection, and replicate-consistency checks for a stable estimation of Robust Interactions among chromatin modifications. asteRIa identifies several epigenetic reader candidates responding to specific interactions between chromatin modifications. For the polycomb protein CBX8, we independently validate our results using genome-wide ChIP-Seq and bisulphite sequencing datasets. We provide the first quantitative framework for identifying cooperative effects of chromatin modifications on protein binding.

New findings on the risk of hypertension from organophosphorus exposure under different glycemic statuses: The key role of lipids?

BACKGROUND AND OBJECTIVE

Considering the widespread use of organophosphorus pesticides (OPs) and the global prevalence of hypertension (HTN), as well as studies indicating that different glycemic statuses may respond differently to the biological effects of OPs. Therefore, this study, based on the Henan rural cohort, aims to investigate the association between OPs exposure and HTN, and further explores whether lipids mediate these associations.

METHODS

We measured the plasma levels of OPs in 2730 participants under different glycemic statuses using gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS). A generalized linear model, Quantile g-computation (QGC), adaptive elastic net (AENET), and Bayesian kernel machine regression (BKMR) models were used to assess the impact of OPs exposure on HTN, with least absolute shrinkage and selection operator (LASSO) penalty regression identifying main OPs. Mediation models were used to evaluate the intermediary role of blood lipids in the OPs-HTN relationship.

RESULTS

The detection rates for all OPs were high, ranging from 76.35 % to 99.17 %. In the normal glucose tolerance (NGT) population, single exposure models indicated that malathion and phenthoate were associated with an increased incidence of HTN (P-FDR < 0.05), with corresponding odds ratios (ORs) and 95 % confidence intervals (CIs) of 1.624 (1.167,2.260) and 1.290 (1.072,1.553), respectively. QGC demonstrated a positive association between OP mixtures and HTN, with malathion and phenthoate being the primary contributors. Additionally, the AENET model's Exposure Response Score (ERS) suggested that the risk of HTN increases with higher ERS (P < 0.001). Furthermore, BKMR revealed that co-exposure to OPs increases HTN risk, with phenthoate having a significant impact. Furthermore, triglycerides (TG) mediated 6.55 % of the association between phenthoate and HTN. However, no association was observed in the impaired fasting glucose (IFG) and type 2 diabetes mellitus (T2DM) populations.

CONCLUSIONS

Our findings suggest that in the NGT population, OPs may significantly contribute to the development of HTN, proposing TG as a potential novel target for HTN prevention.

Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy

INTRODUCTION

The correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for-gestational-age (LGA) babies in women exposed to radiation before becoming pregnant, there is no prediction model yet.

MATERIAL AND METHODS

The data were collected from the National Free Preconception Health Examination Project in China. A sum of 455 neonates (42 SGA births and 423 non-LGA births) were included. A training set (n = 319) and a test set (n = 136) were created from the dataset at random. To develop prediction models for LGA neonates, conventional logistic regression (LR) method and six machine learning methods were used in this study. Recursive feature elimination approach was performed by choosing 10 features which made a big contribution to the prediction models. And the Shapley Additive Explanation model was applied to interpret the most important characteristics that affected forecast outputs.

RESULTS

The random forest (RF) model had the highest average area under the receiver-operating-characteristic curve (AUC) for predicting LGA in the test set (0.843, 95% confidence interval [CI]: 0.714-0.974). Except for the logistic regression model (AUC: 0.603, 95%CI: 0.440-0.767), other models' AUCs displayed well. Thereinto, the RF algorithm's final prediction model using 10 characteristics achieved an average AUC of 0.821 (95% CI: 0.693-0.949).

CONCLUSION

The prediction model based on machine learning might be a promising tool for the prenatal prediction of LGA births in women with radiation exposure before pregnancy.

Model Selection for Exposure-Mediator Interaction

In mediation analysis, the exposure often influences the mediating effect, i.e., there is an interaction between exposure and mediator on the dependent variable. When the mediator is high-dimensional, it is necessary to identify non-zero mediatorsM and exposure-by-mediator ( X -by- M ) interactions. Although several high-dimensional mediation methods can naturally handle X -by- M interactions, research is scarce in preserving the underlying hierarchical structure between the main effects and the interactions. To fill the knowledge gap, we develop the XMInt procedure to select M and X -by- M interactions in the high-dimensional mediators setting while preserving the hierarchical structure. Our proposed method employs a sequential regularization-based forward-selection approach to identify the mediators and their hierarchically preserved interaction with exposure. Our numerical experiments showed promising selection results. Further, we applied our method to ADNI morphological data and examined the role of cortical thickness and subcortical volumes on the effect of amyloid-beta accumulation on cognitive performance, which could be helpful in understanding the brain compensation mechanism.

Evaluating a multifaceted implementation strategy and package of evidence-based interventions based on WHO PEN for people living with HIV and cardiometabolic conditions in Lusaka, Zambia: protocol for the TASKPEN hybrid effectiveness-implementation stepped wedge cluster randomized trial

BACKGROUND

Despite increasing morbidity and mortality from non-communicable diseases (NCD) globally, health systems in low- and middle-income countries (LMICs) have limited capacity to address these chronic conditions, particularly in sub-Saharan Africa (SSA). There is an urgent need, therefore, to respond to NCDs in SSA, beginning by applying lessons learned from the first global response to any chronic disease-HIV-to tackle the leading cardiometabolic killers of people living with HIV (PLHIV). We have developed a feasible and acceptable package of evidence-based interventions and a multi-faceted implementation strategy, known as "TASKPEN," that has been adapted to the Zambian setting to address hypertension, diabetes, and dyslipidemia. The TASKPEN multifaceted implementation strategy focuses on reorganizing service delivery for integrated HIV-NCD care and features task-shifting, practice facilitation, and leveraging HIV platforms for NCD care. We propose a hybrid type II effectiveness-implementation stepped-wedge cluster randomized trial to evaluate the effects of TASKPEN on clinical and implementation outcomes, including dual control of HIV and cardiometabolic NCDs, as well as quality of life, intervention reach, and cost-effectiveness.

METHODS

The trial will be conducted in 12 urban health facilities in Lusaka, Zambia over a 30-month period. Clinical outcomes will be assessed via surveys with PLHIV accessing routine HIV services, and a prospective cohort of PLHIV with cardiometabolic comorbidities nested within the larger trial. We will also collect data using mixed methods, including in-depth interviews, questionnaires, focus group discussions, and structured observations, and estimate cost-effectiveness through time-and-motion studies and other costing methods, to understand implementation outcomes according to Proctor's Outcomes for Implementation Research, the Consolidated Framework for Implementation Research, and selected dimensions of RE-AIM.

DISCUSSION

Findings from this study will be used to make discrete, actionable, and context-specific recommendations in Zambia and the region for integrating cardiometabolic NCD care into national HIV treatment programs. While the TASKPEN study focuses on cardiometabolic NCDs in PLHIV, the multifaceted implementation strategy studied will be relevant to other NCDs and to people without HIV. It is expected that the trial will generate new insights that enable delivery of high-quality integrated HIV-NCD care, which may improve cardiovascular morbidity and viral suppression for PLHIV in SSA. This study was registered at ClinicalTrials.gov (NCT05950919).

Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals

Importance

Many medical journals, including JAMA, restrict the use of causal language to the reporting of randomized clinical trials. Although well-conducted randomized clinical trials remain the preferred approach for answering causal questions, methods for observational studies have advanced such that causal interpretations of the results of well-conducted observational studies may be possible when strong assumptions hold. Furthermore, observational studies may be the only practical source of information for answering some questions about the causal effects of medical or policy interventions, can support the study of interventions in populations and settings that reflect practice, and can help identify interventions for further experimental investigation. Identifying opportunities for the appropriate use of causal language when describing observational studies is important for communication in medical journals.

Observations

A structured approach to whether and how causal language may be used when describing observational studies would enhance the communication of research goals, support the assessment of assumptions and design and analytic choices, and allow for more clear and accurate interpretation of results. Building on the extensive literature on causal inference across diverse disciplines, we suggest a framework for observational studies that aim to provide evidence about the causal effects of interventions based on 6 core questions: what is the causal question; what quantity would, if known, answer the causal question; what is the study design; what causal assumptions are being made; how can the observed data be used to answer the causal question in principle and in practice; and is a causal interpretation of the analyses tenable?

Conclusions and Relevance

Adoption of the proposed framework to identify when causal interpretation is appropriate in observational studies promises to facilitate better communication between authors, reviewers, editors, and readers. Practical implementation will require cooperation between editors, authors, and reviewers to operationalize the framework and evaluate its effect on the reporting of empirical research.

Suicide on the Toronto Transit Commission subway system in Canada (1998-2021): a time-series analysis

Background

The Toronto Transit Commission (TTC) operates the public transit system in Toronto, Canada. From 1954 to 1980, there were 430 suicide deaths/attempts on the TTC subway system. In 2011, TTC implemented Crisis Link, a suicide helpline to connect subway passengers with counsellors. Upstream factors such as media reporting about suicide incidents may also influence suicidal behaviour. Our objectives were to investigate how Crisis Link and media reports about TTC suicide incidents influenced suicide rates.

Methods

Suicide data were obtained from the TTC and Coroner, with Crisis Link data provided by Distress Centres of Greater Toronto (1998-2021). Media articles were identified through a database search of Toronto media publications. Interrupted time-series analysis investigated the association between Crisis Link calls, media articles, and quarterly suicide rates on the subway system.

Findings

There were 302 suicides on TTC's subway system from 1998 to 2021. The introduction of Crisis Link was associated with a large but non-significant decrease in TTC-related suicide rate in the same quarter (IRR = 0.64, 95% CI = 0.36-1.12). Each subsequent post-Crisis-Link quarter experienced an average 2% increase in suicide rate (IRR = 1.02, 95% CI = 1.004-1.04). Furthermore, for each TTC-related media article in the previous quarter, the suicide rate on the TTC increased by 2% (IRR = 1.02, 95% CI = 1.004-1.04).

Interpretation

The Crisis Link helpline was associated with a large but non-significant short-term decrease in suicide rates. However, this outcome was not sustained; this may, in part, be attributable to media reporting which was associated with increased suicides. This should inform suicide prevention policies in Canada and worldwide.

Funding

No funding.

SLC45A3 Serves as a Potential Therapeutic Biomarker to Attenuate White Matter Injury After Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH) is a severe cerebrovascular disease, which impairs patients' white matter even after timely clinical interventions. Indicated by studies in the past decade, ICH-induced white matter injury (WMI) is closely related to neurological deficits; however, its underlying mechanism and pertinent treatment are yet insufficient. We gathered two datasets (GSE24265 and GSE125512), and by taking an intersection among interesting genes identified by weighted gene co-expression networks analysis, we determined target genes after differentially expressing genes in two datasets. Additional single-cell RNA-seq analysis (GSE167593) helped locate the gene in cell types. Furthermore, we established ICH mice models induced by autologous blood or collagenase. Basic medical experiments and diffusion tensor imaging were applied to verify the function of target genes in WMI after ICH. Through intersection and enrichment analysis, gene SLC45A3 was identified as the target one, which plays a key role in the regulation of oligodendrocyte differentiation involving in fatty acid metabolic process, etc. after ICH, and single-cell RNA-seq analysis also shows that it mainly locates in oligodendrocytes. Further experiments verified overexpression of SLC45A3 ameliorated brain injury after ICH. Therefore, SLC45A3 might serve as a candidate therapeutic biomarker for ICH-induced WMI, and overexpression of it may be a potential approach for injury attenuation.

Designing observational studies for credible causal inference in addiction research-Directed acyclic graphs, modified disjunctive cause criterion and target trial emulation

Randomized controlled trials (RCTs) are considered the gold standard for causal inference. With a sufficient sample size, randomization removes confounding up to the time of randomization and allows the treatment effect to be isolated. However, RCTs may have limited generalizability and transportability and are often not feasible in addiction research due to ethical or logistical constraints. The importance of observational studies from real-world settings has been increasingly recognized in research on health. This paper provides an overview of modern approaches to designing observational studies that enable causal inference. It illustrates three key techniques, Directed Acyclic Graphs (DAGs), modified Disjunctive Cause Criterion and Target Trial Emulation, and discusses the strengths and limitations of their applications.

Sparse Group Penalties for bi-level variable selection

Many data sets exhibit a natural group structure due to contextual similarities or high correlations of variables, such as lipid markers that are interrelated based on biochemical principles. Knowledge of such groupings can be used through bi-level selection methods to identify relevant feature groups and highlight their predictive members. One of the best known approaches of this kind combines the classical Least Absolute Shrinkage and Selection Operator (LASSO) with the Group LASSO, resulting in the Sparse Group LASSO. We propose the Sparse Group Penalty (SGP) framework, which allows for a flexible combination of different SGL-style shrinkage conditions. Analogous to SGL, we investigated the combination of the Smoothly Clipped Absolute Deviation (SCAD), the Minimax Concave Penalty (MCP) and the Exponential Penalty (EP) with their group versions, resulting in the Sparse Group SCAD, the Sparse Group MCP, and the novel Sparse Group EP (SGE). Those shrinkage operators provide refined control of the effect of group formation on the selection process through a tuning parameter. In simulation studies, SGPs were compared with other bi-level selection methods (Group Bridge, composite MCP, and Group Exponential LASSO) for variable and group selection evaluated with the Matthews correlation coefficient. We demonstrated the advantages of the new SGE in identifying parsimonious models, but also identified scenarios that highlight the limitations of the approach. The performance of the techniques was further investigated in a real-world use case for the selection of regulated lipids in a randomized clinical trial.

Exploring heterogeneity and dynamics of meteorological influences on US PM2.5: A distributed learning approach with spatiotemporal varying coefficient models

Particulate matter (PM) has emerged as a primary air quality concern due to its substantial impact on human health. Many recent research works suggest that PM2.5 concentrations depend on meteorological conditions. Enhancing current pollution control strategies necessitates a more holistic comprehension of PM2.5 dynamics and the precise quantification of spatiotemporal heterogeneity in the relationship between meteorological factors and PM2.5 levels. The spatiotemporal varying coefficient model stands as a prominent spatial regression technique adept at addressing this heterogeneity. Amidst the challenges posed by the substantial scale of modern spatiotemporal datasets, we propose a pioneering distributed estimation method (DEM) founded on multivariate spline smoothing across a domain's triangulation. This DEM algorithm ensures an easily implementable, highly scalable, and communication-efficient strategy, demonstrating almost linear speedup potential. We validate the effectiveness of our proposed DEM through extensive simulation studies, demonstrating that it achieves coefficient estimations akin to those of global estimators derived from complete datasets. Applying the proposed model and method to the US daily PM2.5 and meteorological data, we investigate the influence of meteorological variables on PM2.5 concentrations, revealing both spatial and seasonal variations in this relationship.

Occurrence of COVID-19 and serum per- and polyfluoroalkyl substances: A case-control study among workers with a wide range of exposures

Per- and polyfluoroalkyl substances (PFAS) are a broad class of synthetic chemicals; some are present in most humans in developed countries. Some studies suggest that certain PFAS may have immunotoxic effects in humans, which could put individuals with high levels of exposure at increased risk for infectious diseases such as COVID-19. We conducted a case-control study to examine the association between COVID-19 diagnosis and PFAS serum concentrations among employees and retirees from two 3 M facilities, one of which historically generated perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), and perfluorohexane sulfonic acid (PFHxS). Participants completed enrollment and follow-up study visits in the Spring of 2021. Participants were categorized as cases if they reported a COVID-19 diagnosis or became sick with at least one symptom of COVID-19 when someone else in their household was diagnosed, otherwise they were categorized as a control. COVID-19 diagnosis was modeled in relation to concentration of serum PFAS measured at enrollment after adjusting for covariates. The analytic sample comprised 573 individuals, 111 cases (19.4%) and 462 controls (80.6%). In adjusted models, the odds ratio of COVID-19 was 0.94 per interquartile range (14.3 ng/mL) increase in PFOS (95% confidence interval 0.85, 1.04). Results for PFOA, PFHxS, and perfluorononanoic acid (PFNA) were similar. Other PFAS present at lower concentrations were examined as categorical variables (above the limit of quantification [LOQ], yes vs. no [referent category]), and also showed no positive associations. In our study, which used individual-level data and included people with high occupational exposure, the serum concentrations of all PFAS examined were not associated with an increased odds ratio for COVID-19. At this point, the epidemiologic data supporting no association of COVID-19 occurrence with PFAS exposure are stronger than those suggesting a positive association.

Developing multidimensional participation profiles after traumatic brain injury: a TBI model systems study

Purpose. To characterize societal participation profiles after moderate-severe traumatic brain injury (TBI) along objective (Frequency) and subjective (Satisfaction, Importance, Enfranchisement) dimensions.Materials and Methods. We conducted secondary analyses of a TBI Model Systems sub-study (N = 408). Multiaxial assessment of participation included the Participation Assessment with Recombined Tools-Objective and -Subjective questionnaires (Participation Frequency and Importance/Satisfaction, respectively) and the Enfranchisement Scale. Participants provided responses via telephone interview 1-15 years post-injury. Multidimensional participation profiles (classes) were extracted using latent profile analysis.Results. A 4-class solution was identified as providing maximal statistical separation between profiles and being clinically meaningful based on profile demographic features. One profile group (48.5% of the sample) exhibited the "best" participation profile (High Frequency, Satisfaction, Importance, and Enfranchisement) and was also the most advantaged according to socioeconomic indicators. Other profile groups showed appreciable heterogeneity across participation dimensions. Age, race/ethnicity, education level, ability to drive, and urbanicity were features that varied between profiles.Conclusions. Societal participation is a critical, but inherently complex, TBI outcome that may not be adequately captured by a single index. Our data underscore the importance of a multidimensional approach to participation assessment and interpretation using profiles. The use of participation profiles may promote precision health interventions for community integration.Implications for RehabilitationOur study found unidimensional measures of societal participation in traumatic brain injury (TBI) populations that focus exclusively on frequency indicators may be overly simplistic and miss key subjective components of participationTaking a multidimensional perspective, we documented four meaningfully distinct participation subgroups (including both objective and subjective dimensions of societal participation) within the TBI rehabilitation populationMultidimensional profiles of participation may be used to group individuals with TBI into target groups for intervention (e.g., deeper goal assessment for individuals who do not rate standard participation activities as important, but also do not participate and do not feel enfranchised).

Exploration of Variables Predicting Sense of School Belonging Using the Machine Learning Method-Group Mnet

The purpose of this study was to explore variables related to school belonging from a holistic perspective, including a large number of variables in one model, different to the traditional analytical method. Using 2015 data from the Program for International Student Assessment (PISA), we sought to identify variables related to school belonging by searching for hundreds of predictors in one model using the group Mnet machine learning technique. The study repeated 100 rounds of model building after random data splitting. After exploring 504 variables (384 student and 99 parent), 32 variables were finally selected after selection counts. Variables predicting a sense of school belonging were categorized as individual/parent variables (e.g. motivation to achieve, tendency to cooperative learning, parental support) and school-related variables (e.g. school satisfaction, peer/teacher relationship, learning/physical activities). The significance and implications of the study as well as future research topics were discussed.

A framework for longitudinal latent factor modelling of treatment response in clinical trials with applications to Psoriatic Arthritis and Rheumatoid Arthritis

OBJECTIVE

Clinical trials involve the collection of a wealth of data, comprising multiple diverse measurements performed at baseline and follow-up visits over the course of a trial. The most common primary analysis is restricted to a single, potentially composite endpoint at one time point. While such an analytical focus promotes simple and replicable conclusions, it does not necessarily fully capture the multi-faceted effects of a drug in a complex disease setting. Therefore, to complement existing approaches, we set out here to design a longitudinal multivariate analytical framework that accepts as input an entire clinical trial database, comprising all measurements, patients, and time points across multiple trials.

METHODS

Our framework composes probabilistic principal component analysis with a longitudinal linear mixed effects model, thereby enabling clinical interpretation of multivariate results, while handling data missing at random, and incorporating covariates and covariance structure in a computationally efficient and principled way.

RESULTS

We illustrate our approach by applying it to four phase III clinical trials of secukinumab in Psoriatic Arthritis (PsA) and Rheumatoid Arthritis (RA). We identify three clinically plausible latent factors that collectively explain 74.5% of empirical variation in the longitudinal patient database. We estimate longitudinal trajectories of these factors, thereby enabling joint characterisation of disease progression and drug effect. We perform benchmarking experiments demonstrating our method's competitive performance at estimating average treatment effects compared to existing statistical and machine learning methods, and showing that our modular approach leads to relatively computationally efficient model fitting.

CONCLUSION

Our multivariate longitudinal framework has the potential to illuminate the properties of existing composite endpoint methods, and to enable the development of novel clinical endpoints that provide enhanced and complementary perspectives on treatment response.

Computer Vision Analysis of Caregiver-Child Interactions in Children with Neurodevelopmental Disorders: A Preliminary Report

We report preliminary results of computer vision analysis of caregiver-child interactions during free play with children diagnosed with autism (N = 29, 41-91 months), attention-deficit/hyperactivity disorder (ADHD, N = 22, 48-100 months), or combined autism + ADHD (N = 20, 56-98 months), and neurotypical children (NT, N = 7, 55-95 months). We conducted micro-analytic analysis of 'reaching to a toy,' as a proxy for initiating or responding to a toy play bout. Dyadic analysis revealed two clusters of interaction patterns, which differed in frequency of 'reaching to a toy' and caregivers' contingent responding to the child's reach for a toy by also reaching for a toy. Children in dyads with higher caregiver responsiveness had less developed language, communication, and socialization skills. Clusters were not associated with diagnostic groups. These results hold promise for automated methods of characterizing caregiver responsiveness in dyadic interactions for assessment and outcome monitoring in clinical trials.

Early Life Disadvantage and the Risk of Depressive Symptoms among Young Black Women

OVERVIEW

We examined the association between early-life socioeconomic disadvantage and depressive symptoms in adulthood and assessed whether social factors in adulthood modify the association.

METHODS

The 11-item Center for Epidemiologic Studies-Depression Scale (CES-D) assessed adult depressive symptoms among 1612 Black women and other participants with a uterus (hereafter participants) in the Study of Environment, Lifestyle and Fibroids. Baseline self-reported childhood factors (i.e., parents in the household, mother's educational attainment, food insecurity, neighborhood safety, childhood income, and quiet bedroom for sleep) were included in a latent class analysis to derive an early life disadvantage construct. Multivariable log-binomial models estimated the association between early life disadvantage and adult depressive symptoms. Potential effect modifiers included adult educational attainment, social support, and financial difficulty.

RESULTS

Participants classified as having high early life disadvantage had 1.34 times (95% CI: 1.20, 1.49) the risk of high depressive symptoms than those in the low early life disadvantage class after adjusting for age, first born status, and childhood health. Adult educational attainment and social support modified the association.

CONCLUSION

Early life disadvantage increased the risk of depressive symptoms in adulthood. Participants with at least some college education and with high social support had greater risk than those with less than college education and low social support, respectively. Thus, the mental health of Black women and other participants with a uterus exposed to early life disadvantage do not necessarily benefit from higher education or from social support.

Subgroups of cognitively affected and unaffected breast cancer survivors after chemotherapy: a data-driven approach

PURPOSE

It is assumed that a segment of breast cancer survivors are cognitively affected after chemotherapy. Our aim is to discover whether there is a qualitatively different cognitively affected subgroup of breast cancer survivors, or whether there are only quantitative differences between survivors in cognitive functioning.

METHODS

Latent profile analysis was applied to age-corrected neuropsychological data -measuring verbal memory, attention, speed, and executive functioning- from an existing sample of 62 breast cancer survivors treated with chemotherapy. Other clustering methods were applied as sensitivity analyses. Subgroup distinctness was established with posterior mean assignment probability and silhouette width. Simulations were used to calculate subgroup stability, posterior predictive checks to establish absolute fit of the subgrouping model. Subgrouping results were compared to traditional normative comparisons results.

RESULTS

Two subgroups were discovered. One had cognitive normal scores, the other -45%- had lower scores. Subgrouping results were consistent across clustering methods. The subgroups showed some overlap; 6% of survivors could fall in either. Subgroups were stable and described the data well. Results of the subgroup clustering model matched those of a traditional normative comparison method requiring small deviations on two cognitive domains.

CONCLUSIONS

We discovered that almost half of breast cancer survivors after chemotherapy form a cognitively affected subgroup, using a data-driven approach. This proportion is higher than previous studies using prespecified cutoffs observed.

IMPLICATIONS FOR CANCER SURVIVORS

A larger group of cancer survivors may be cognitively affected than previously recognized, and a less strict threshold for cognitive problems may be needed in this population.

Predictors of follow-up care for critically-ill patients with seizures and epileptiform abnormalities on EEG monitoring

OBJECTIVE

Post-hospitalization follow-up visits are crucial for preventing long-term complications. Patients with electrographic epileptiform abnormalities (EA) including seizures and periodic and rhythmic patterns are especially in need of follow-up for long-term seizure risk stratification and medication management. We sought to identify predictors of follow-up.

METHODS

This is a retrospective cohort study of all patients (age ≥ 18 years) admitted to intensive care units that underwent continuous EEG (cEEG) monitoring at a single center between 01/2016-12/2019. Patients with EAs were included. Clinical and demographic variables were recorded. Follow-up status was determined using visit records 6-month post discharge, and visits were stratified as outpatient follow-up, neurology follow-up, and inpatient readmission. Lasso feature selection analysis was performed.

RESULTS

723 patients (53 % female, mean (std) age of 62.3 (16.4) years) were identified from cEEG records with 575 (79 %) surviving to discharge. Of those discharged, 450 (78 %) had outpatient follow-up, 316 (55 %) had a neurology follow-up, and 288 (50 %) were readmitted during the 6-month period. Discharge on antiseizure medications (ASM), younger age, admission to neurosurgery, and proximity to the hospital were predictors of neurology follow-up visits. Discharge on ASMs, along with longer length of stay, younger age, emergency admissions, and higher illness severity were predictors of readmission.

SIGNIFICANCE

ASMs at discharge, demographics (age, address), hospital care teams, and illness severity determine probability of follow-up. Parameters identified in this study may help healthcare systems develop interventions to improve care transitions for critically-ill patients with seizures and other EA.

The expected sample allele frequencies from populations of changing size via orthogonal polynomials

In this article, discrete and stochastic changes in (effective) population size are incorporated into the spectral representation of a biallelic diffusion process for drift and small mutation rates. A forward algorithm inspired by Hidden-Markov-Model (HMM) literature is used to compute exact sample allele frequency spectra for three demographic scenarios: single changes in (effective) population size, boom-bust dynamics, and stochastic fluctuations in (effective) population size. An approach for fully agnostic demographic inference from these sample allele spectra is explored, and sufficient statistics for stepwise changes in population size are found. Further, convergence behaviours of the polymorphic sample spectra for population size changes on different time scales are examined and discussed within the context of inference of the effective population size. Joint visual assessment of the sample spectra and the temporal coefficients of the spectral decomposition of the forward diffusion process is found to be important in determining departure from equilibrium. Stochastic changes in (effective) population size are shown to shape sample spectra particularly strongly.

Cell Tree Rings: the structure of somatic evolution as a human aging timer

Biological age is typically estimated using biomarkers whose states have been observed to correlate with chronological age. A persistent limitation of such aging clocks is that it is difficult to establish how the biomarker states are related to the mechanisms of aging. Somatic mutations could potentially form the basis for a more fundamental aging clock since the mutations are both markers and drivers of aging and have a natural timescale. Cell lineage trees inferred from these mutations reflect the somatic evolutionary process, and thus, it has been conjectured, the aging status of the body. Such a timer has been impractical thus far, however, because detection of somatic variants in single cells presents a significant technological challenge. Here, we show that somatic mutations detected using single-cell RNA sequencing (scRNA-seq) from thousands of cells can be used to construct a cell lineage tree whose structure correlates with chronological age. De novo single-nucleotide variants (SNVs) are detected in human peripheral blood mononuclear cells using a modified protocol. A default model based on penalized multiple regression of chronological age on 31 metrics characterizing the phylogenetic tree gives a Pearson correlation of 0.81 and a median absolute error of ~4 years between predicted and chronological ages. Testing of the model on a public scRNA-seq dataset yields a Pearson correlation of 0.85. In addition, cell tree age predictions are found to be better predictors of certain clinical biomarkers than chronological age alone, for instance glucose, albumin levels, and leukocyte count. The geometry of the cell lineage tree records the structure of somatic evolution in the individual and represents a new modality of aging timer. In addition to providing a numerical estimate of "cell tree age," it unveils a temporal history of the aging process, revealing how clonal structure evolves over life span. Cell Tree Rings complements existing aging clocks and may help reduce the current uncertainty in the assessment of geroprotective trials.

Genetic Associations with Placental Proteins in Maternal Serum Identify Biomarkers for Hypertension in Pregnancy

Background

Preeclampsia is a complex syndrome that accounts for considerable maternal and perinatal morbidity and mortality. Despite its prevalence, no effective disease-modifying therapies are available. Maternal serum placenta-derived proteins have been in longstanding use as markers of risk for aneuploidy and placental dysfunction, but whether they have a causal contribution to preeclampsia is unknown.

Objective

We aimed to investigate the genetic regulation of serum placental proteins in early pregnancy and their potential causal links with preeclampsia and gestational hypertension.

Study design

This study used a nested case-control design with nulliparous women enrolled in the nuMoM2b study from eight clinical sites across the United States between 2010 and 2013. The first- and second-trimester serum samples were collected, and nine proteins were measured, including vascular endothelial growth factor (VEGF), placental growth factor, endoglin, soluble fms-like tyrosine kinase-1 (sFlt-1), a disintegrin and metalloproteinase domain-containing protein 12 (ADAM-12), pregnancy-associated plasma protein A, free beta-human chorionic gonadotropin, inhibin A, and alpha-fetoprotein. This study used genome-wide association studies to discern genetic influences on these protein levels, treating proteins as outcomes. Furthermore, Mendelian randomization was used to evaluate the causal effects of these proteins on preeclampsia and gestational hypertension, and their further causal relationship with long-term hypertension, treating proteins as exposures.

Results

A total of 2,352 participants were analyzed. We discovered significant associations between the pregnancy zone protein locus and concentrations of ADAM-12 (rs6487735, P= 3.03×10 -22 ), as well as between the vascular endothelial growth factor A locus and concentrations of both VEGF (rs6921438, P= 7.94×10 -30 ) and sFlt-1 (rs4349809, P= 2.89×10 -12 ). Our Mendelian randomization analyses suggested a potential causal association between first-trimester ADAM-12 levels and gestational hypertension (odds ratio=0.78, P= 8.6×10 -4 ). We also found evidence for a potential causal effect of preeclampsia (odds ratio=1.75, P =8.3×10 -3 ) and gestational hypertension (odds ratio=1.84, P =4.7×10 -3 ) during the index pregnancy on the onset of hypertension 2-7 years later. The additional mediation analysis indicated that the impact of ADAM-12 on postpartum hypertension could be explained in part by its indirect effect through gestational hypertension (mediated effect=-0.15, P= 0.03).

Conclusions

Our study discovered significant genetic associations with placental proteins ADAM-12, VEGF, and sFlt-1, offering insights into their regulation during pregnancy. Mendelian randomization analyses demonstrated evidence of potential causal relationships between the serum levels of placental proteins, particularly ADAM-12, and gestational hypertension, potentially informing future prevention and treatment investigations.

Linear modeling of zonal level crop production in Ethiopia

Accounting for zonal-level variations and identifying factors that have linear effects on crop production help to make better decisions and plan new policies for effective crop production and food security. The main objective of this study is to identify potential subsets of covariates and estimate their linear effects on crop production. A linear mixed effects model (random--intercept) is used on agricultural sample survey data for Meher seasons from 2012/13 to 2019/20 to explore and identify the best variance-covariance structure for the longitudinal data on 90 zones with eight repeated observations and different sampling weights. The minimum, mean, and maximum crop production by farmers across the country are 1.616, 8.693, and 147.843 quintals, respectively, and about 98 % of farmers produced less than 25 quintals. There is a small rate of increase in mean and median crop production by farmers across the years, and the variability between zones is highest in the year 2019/20 and in the Somali region. The histogram, kernel density, and P-P plots suggested a common logarithm transformation on the crop production variable. Results from the data exploration and variance-covariance structure selection methods suggested a heterogeneous compound symmetry (CSH) structure. Covariates region, year, proportion of farmers who practice pure-agriculture and other-agriculture types, proportion of farmers who use any type of fertilizer, farmer's age, area used, farmer association crop production, indigenous seed used, improved seed used, UREA fertilizer used, other fertilizers used, and percentage of crop damaged are significant in linearly explaining/affecting log crop production, and among these area used, farmers association crop production, UREA fertilizer used, and indigenous seed used have relatively highest effect on log crop production. Zones Wolayita, North-Shewa (Am), West-Arsi, West-Welega, Dawro, and Guji are top/good performers while zones Southwest-Shewa, Waghimra, Guraghe, South-Omo, Keffa, North-Wello, South-Wello, and Eastern Tigray are bottom/poor performers in crop production. Model assumptions and influence diagnostics results suggested the linearity of the model and normality of random effects and residuals are not violated, even though some zones have influences on either model parameters, precisions of estimates of these parameters, and predicted values.

Misstatements, misperceptions, and mistakes in controlling for covariates in observational research

We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.