Intraocular amphiregulin and axial elongation in non-human adolescent primates.

The purpose of the experimental interventional study was to examine the influence of intraocularly applied amphiregulin, a member of the epidermal growth factor (EGF) family, on axial length in young non-human primates. It included three non-human primates (Macaca mulatta), aged 4-6 years. The left eyes received three intravitreal injections of amphiregulin (400ng/50 µl) in intervals of 4 weeks, while the right eyes received three intravitreal injections of phosphate buffered solution (50 µl) at the same time points. Ocular biometry was performed in weekly intervals. At baseline, the left eyes (study eyes) were shorter than the right (control) eyes (20.69 ± 0.21 mm versus 20.79 ± 0.24 mm; P < 0.001), with an inter-eye axial length (AL) difference (left minus right eye) of -0.10 ± 0.23 mm. Inter-eye AL difference increased (P < 0.001) to 0.15 ± 0.18 mm at study end, at 12 weeks after baseline. Axial elongation during the study was higher (P < 0.001) in the left eyes (20.69 ± 0.21 mm to 21.05 ± 0.29 mm or 0.36 ± 0.30 mm) than in the right eyes (20.79 ± 0.24 mm to 20.90 ± 0.31 mm or 0.11 ± 0.17 mm). In a parallel manner, inter-eye difference in vitreous cavity depth combined with lens thickness (left eye minus right eye) increased from -0.04 ± 0.17 mm at baseline to -0.02 ± 0.21 mm (P = 0.02), 0.04 ± 0.10 mm (P = 0.002), and to 0.42 ± 0.67 mm (P < 0.001) at 5, 6, and 12 weeks after baseline, respectively. The results suggest that intravitreally applied amphiregulin as EGF family member led to an increase in axial length in adolescent non-human primates. It supports the hypothesis of amphiregulin as EGF family member being involved in the process of axial elongation.

Establishment and comprehensive characterization of a novel dark-reared zebrafish model for myopia studies.

Myopia is predicted to impact approximately 5 billion people by 2050, necessitating mechanistic understanding of its development. Myopia results from dysregulated genetic mechanisms of emmetropization, caused by over-exposure to aberrant visual environments; however, these genetic mechanisms remain unclear. Recent human genome-wide association studies have identified a range of novel myopia-risk genes. To facilitate large-scale in vivo mechanistic examination of gene-environment interactions, this study aims to establish a myopia model platform that allows efficient environmental and genetic manipulations. We established an environmental zebrafish myopia model by dark-rearing. Ocular biometrics including relative ocular refraction were quantified using optical coherence tomography images. Spatial vision was assessed using optomotor response (OMR). Retinal function was analyzed via electroretinography (ERG). Myopia-associated molecular contents or distributions were examined using RT-qPCR or immunohistochemistry. Our model produces robust phenotypic changes, showing myopia after 2 weeks of dark-rearing, which were recoverable within 2 weeks after returning animals to normal lighting. 2-week dark-reared zebrafish have reduced spatial-frequency tuning function. ERG showed reduced photoreceptor and bipolar cell function (a- and b-waves) after only 2 days of dark-rearing, which worsened after 2 weeks of dark-rearing. We also found dark-rearing-induced changes to expression of myopia-risk genes, including egr1, vegfaa, vegfab, rbp3, gjd2a and gjd2b, inner retinal distribution of EFEMP1, TIMP2 and MMP2, as well as transiently reduced PSD95 density in the inner plexiform layer. Coupled with the gene editing tools available for zebrafish, our environmental myopia model provides an excellent platform for large-scale investigation of gene-environment interactions in myopia development.

Lipid profile in the aqueous humor of patients with myopia.

We examined the lipid profiles in the aqueous humor (AH) of myopic patients to identify differences and investigate the relationships among dissertating lipids. Additionally, we assessed spherical equivalents and axial lengths to explore the pathogenesis of myopia. Ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) was employed to qualitatively and quantitatively analyze the lipid composition of samples from myopic patients with axial lengths <26 mm (Group A) and >28 mm (Group B). Differences in lipid profiles between the two groups were determined using univariate and multivariate analyses. Receiver operator characteristic (ROC) curves were used to identify discriminating lipids. Spearman correlation analysis explored the associations between lipid concentrations and biometric parameters. Three hundred and nine lipids across 21 lipid classes have been identified in this study. Five lipids showed significant differences between Group B and Group A (VIP >1, P < 0.05): BMP (20:3/22:3), PG (22:1/24:0), PS (14:1/22:4), TG (44:2)_FA18:2, and TG (55:3)_FA18:1. The area under the curve (AUC) for these lipids was >0.75. Notably, the concentrations of BMP (20:3/22:3), PS (14:1/22:4), and TG (55:3)_FA18:1 were correlated with spherical equivalents, while BMP (20:3/22:3) and PS (14:1/22:4) correlated with axial lengths. Our study identified five differential lipids in myopic patients, with three showing significant correlations with the degree of myopia. These findings enhance our understanding of myopia pathogenesis through lipidomic alterations, emphasizing changes in cell membrane composition and function, energy metabolism and storage, and pathways involving inflammation, peroxisome proliferator-activated receptors (PPAR), and metabolic processes related to phosphatidylserine, phosphatidylglycerol, triglycerides, polyunsaturated fatty acids, and cholesterol.

Parallel comparison of ocular metrics in non-human primates with high myopia by LS900, ultrasonography and MRI-based 3D reconstruction.

We investigate the ocular dimensions and shape by using Lenstar900 (LS900), A-scan ultrasonography, and Magnetic Resonance Imaging (MRI) in highly myopic Macaca fascicularis. The ocular dimensions data of LS900, A-scan ultrasonography and MRI was assessed from 8 eyes (4 adult male cynomolgus macaque) with extremely high myopia (≤-1000DS) and compared by means of coefficients of concordance and 95% limits of agreement. Multiple regression analysis was performed to explore the associations between ocular biometry, volume, refraction and inter-instrument discrepancies. Test-retest reliability of three measurements of ocular parameters at two time points was almost equal (intraclass correlation = 0.831 to 1.000). The parallel-forms reliability of three measurements was strong for vitreous chamber depth (VCD) (coefficient of concordance = 0.919 to 0.981), moderate for axial length (AL) (coefficient of concordance = 0.486 to 0.981), and weak for anterior chamber depth (ACD) (coefficient of concordance = 0.267 to 0.621) and lens thickness (LT) (coefficient of concordance = 0.035 to 0.631). The LS900 and MRI systematically underestimated the ACD and LT comparing to A-scan ultrasonography (P < 0.05). Notably, the average AL on LS900 displayed a significant correlation with those on MRI (r = 0.978, P < 0.001) and A-scan ultrasonography (r = 0.990, P < 0.001). Almost 4/5 eyeballs were prolate. The mean eyeball volume positively correlated with AL (r = 0.782, P = 0.022), the width (r = 0.945, P = 0.000), and the length (r = 0.782, P = 0.022) of eyeball, while negatively correlated with SER (r = -0.901, P = 0.000). In conclusion, there was a high inter-instrument concordance for VCD with LS900, A-scan ultrasonography and MRI, while ACD and LT were underestimated with LS900 compared to A-scan ultrasonography, and the LS900 and A-scan ultrasonography could reliably measure the AL. MRI further revealed an equatorial globe shape in extremely myopic non-human primates.

Corpus callosum long-term biometry in very preterm children related to cognitive and motor outcomes.

BACKGROUND: The corpus callosum (CC) is suggested as an indirect biomarker of white matter volume, which is often affected in preterm birth. However, diagnosing mild white matter injury is challenging. METHODS: We studied 124 children born preterm (mean age: 8.4 ± 1.1 years), using MRI to assess CC measurements and cognitive/motor outcomes based on the Wechsler Intelligence Scale for Children-V (WPPSI-V) and Movement Assessment Battery for Children-2 (MABC-2). RESULTS: Children with normal outcomes exhibited greater height (10.2 ± 2.1 mm vs. 9.4 ± 2.3 mm; p = 0.01) and fractional anisotropy at splenium (895[680-1000] vs 860.5[342-1000]) and total CC length (69.1 ± 4.8 mm vs. 67.3 ± 5.1 mm; p = 0.02) compared to those with adverse outcomes. All measured CC areas were smaller in the adverse outcome group. Models incorporating posterior CC measurements demonstrated the highest specificity (83.3% Sp, AUC: 0.65) for predicting neurological outcomes. CC length and splenium height were the only linear measurements associated with manual dexterity and total MABC-2 score while both the latter and genu were related with Full-Scale Intelligence Quotient. CONCLUSIONS: CC biometry in children born very preterm at school-age is associated with outcomes and exhibits a specific subregion alteration pattern. The posterior CC may serve as an important neurodevelopmental biomarker in very preterm infants. IMPACT: The corpus callosum has the potential to serve as a reliable and easily measurable biomarker of white matter integrity in very preterm children. Estimating diffuse white matter injury in preterm infants using conventional MRI sequences is not always conclusive. The biometry of the posterior part of the corpus callosum is associated with cognitive and certain motor outcomes at school age in children born very preterm. Length and splenium measurements seem to serve as reliable biomarkers for assessing neurological outcomes in this population.

Optimal refinement of strata to balance covariates.

What is the best way to split one stratum into two to maximally reduce the within-stratum imbalance in many covariates? We formulate this as an integer program and approximate the solution by randomized rounding of a linear program. A linear program may assign a fraction of a person to each refined stratum. Randomized rounding views fractional people as probabilities, assigning intact people to strata using biased coins. Randomized rounding is a well-studied theoretical technique for approximating the optimal solution of certain insoluble integer programs. When the number of people in a stratum is large relative to the number of covariates, we prove the following new results: (i) randomized rounding to split a stratum does very little randomizing, so it closely resembles the linear programming relaxation without splitting intact people; (ii) the linear relaxation and the randomly rounded solution place lower and upper bounds on the unattainable integer programming solution; and because of (i), these bounds are often close, thereby ratifying the usable randomly rounded solution. We illustrate using an observational study that balanced many covariates by forming matched pairs composed of 2016 patients selected from 5735 using a propensity score. Instead, we form 5 propensity score strata and refine them into 10 strata, obtaining excellent covariate balance while retaining all patients. An R package optrefine at CRAN implements the method. Supplementary materials are available online.

High-dimensional covariate-augmented overdispersed poisson factor model.

The current Poisson factor models often assume that the factors are unknown, which overlooks the explanatory potential of certain observable covariates. This study focuses on high dimensional settings, where the number of the count response variables and/or covariates can diverge as the sample size increases. A covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. A group of identifiability conditions is provided to theoretically guarantee computational identifiability. We incorporate the interdependence of both response variables and covariates by imposing a low-rank constraint on the large coefficient matrix. To address the computation challenges posed by nonlinearity, two high-dimensional latent matrices, and the low-rank constraint, we propose a novel variational estimation scheme that combines Laplace and Taylor approximations. We also develop a criterion based on a singular value ratio to determine the number of factors and the rank of the coefficient matrix. Comprehensive simulation studies demonstrate that the proposed method outperforms the state-of-the-art methods in estimation accuracy and computational efficiency. The practical merit of our method is demonstrated by an application to the CITE-seq dataset. A flexible implementation of our proposed method is available in the R package COAP.

Improving prediction of linear regression models by integrating external information from heterogeneous populations: James-Stein estimators.

We consider the setting where (1) an internal study builds a linear regression model for prediction based on individual-level data, (2) some external studies have fitted similar linear regression models that use only subsets of the covariates and provide coefficient estimates for the reduced models without individual-level data, and (3) there is heterogeneity across these study populations. The goal is to integrate the external model summary information into fitting the internal model to improve prediction accuracy. We adapt the James-Stein shrinkage method to propose estimators that are no worse and are oftentimes better in the prediction mean squared error after information integration, regardless of the degree of study population heterogeneity. We conduct comprehensive simulation studies to investigate the numerical performance of the proposed estimators. We also apply the method to enhance a prediction model for patella bone lead level in terms of blood lead level and other covariates by integrating summary information from published literature.

Discussion on "Bayesian meta-analysis of penetrance for cancer risk" by Thanthirige Lakshika M. Ruberu, Danielle Braun, Giovanni Parmigiani, and Swati Biswas.

Ruberu et al. (2023) introduce an elegant approach to fit a complicated meta-analysis problem with diverse reporting modalities into the framework of hierarchical Bayesian inference. We discuss issues related to some of the involved parametric model assumptions.

Hypothesis tests in ordinal predictive models with optimal accuracy.

In real-world applications involving multi-class ordinal discrimination, a common approach is to aggregate multiple predictive variables into a linear combination, aiming to develop a classifier with high prediction accuracy. Assessment of such multi-class classifiers often utilizes the hypervolume under ROC manifolds (HUM). When dealing with a substantial pool of potential predictors and achieving optimal HUM, it becomes imperative to conduct appropriate statistical inference. However, prevalent methodologies in existing literature are computationally expensive. We propose to use the jackknife empirical likelihood method to address this issue. The Wilks' theorem under moderate conditions is established and the power analysis under the Pitman alternative is provided. We also introduce a novel network-based rapid computation algorithm specifically designed for computing a general multi-sample $U$-statistic in our test procedure. To compare our approach against existing approaches, we conduct extensive simulations. Results demonstrate the superior performance of our method in terms of test size, power, and implementation time. Furthermore, we apply our method to analyze a real medical dataset and obtain some new findings.

Propensity weighting plus adjustment in proportional hazards model is not doubly robust.

Recently, it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model and propensity score weighting, with the intention of forming a doubly robust estimator of an exposure effect hazard ratio that is unbiased in large samples when either the Cox model or the propensity score model is correctly specified. This combination does not, in general, produce a doubly robust estimator, even after regression standardization, when there is truly a causal effect. We demonstrate via simulation this lack of double robustness for the semiparametric Cox model, the Weibull proportional hazards model, and a simple proportional hazards flexible parametric model, with both the latter models fit via maximum likelihood. We provide a novel proof that the combination of propensity score weighting and a proportional hazards survival model, fit either via full or partial likelihood, is consistent under the null of no causal effect of the exposure on the outcome under particular censoring mechanisms if either the propensity score or the outcome model is correctly specified and contains all confounders. Given our results suggesting that double robustness only exists under the null, we outline 2 simple alternative estimators that are doubly robust for the survival difference at a given time point (in the above sense), provided the censoring mechanism can be correctly modeled, and one doubly robust method of estimation for the full survival curve. We provide R code to use these estimators for estimation and inference in the supporting information.

Post-selection inference in regression models for group testing data.

We develop a methodology for valid inference after variable selection in logistic regression when the responses are partially observed, that is, when one observes a set of error-prone testing outcomes instead of the true values of the responses. Aiming at selecting important covariates while accounting for missing information in the response data, we apply the expectation-maximization algorithm to compute maximum likelihood estimators subject to LASSO penalization. Subsequent to variable selection, we make inferences on the selected covariate effects by extending post-selection inference methodology based on the polyhedral lemma. Empirical evidence from our extensive simulation study suggests that our post-selection inference results are more reliable than those from naive inference methods that use the same data to perform variable selection and inference without adjusting for variable selection.

Visibility graph-based covariance functions for scalable spatial analysis in non-convex partially Euclidean domains.

We present a new method for constructing valid covariance functions of Gaussian processes for spatial analysis in irregular, non-convex domains such as bodies of water. Standard covariance functions based on geodesic distances are not guaranteed to be positive definite on such domains, while existing non-Euclidean approaches fail to respect the partially Euclidean nature of these domains where the geodesic distance agrees with the Euclidean distances for some pairs of points. Using a visibility graph on the domain, we propose a class of covariance functions that preserve Euclidean-based covariances between points that are connected in the domain while incorporating the non-convex geometry of the domain via conditional independence relationships. We show that the proposed method preserves the partially Euclidean nature of the intrinsic geometry on the domain while maintaining validity (positive definiteness) and marginal stationarity of the covariance function over the entire parameter space, properties which are not always fulfilled by existing approaches to construct covariance functions on non-convex domains. We provide useful approximations to improve computational efficiency, resulting in a scalable algorithm. We compare the performance of our method with those of competing state-of-the-art methods using simulation studies on synthetic non-convex domains. The method is applied to data regarding acidity levels in the Chesapeake Bay, showing its potential for ecological monitoring in real-world spatial applications on irregular domains.

Direct and indirect treatment effects in the presence of semicompeting risks.

Semicompeting risks refer to the phenomenon that the terminal event (such as death) can censor the nonterminal event (such as disease progression) but not vice versa. The treatment effect on the terminal event can be delivered either directly following the treatment or indirectly through the nonterminal event. We consider 2 strategies to decompose the total effect into a direct effect and an indirect effect under the framework of mediation analysis in completely randomized experiments by adjusting the prevalence and hazard of nonterminal events, respectively. They require slightly different assumptions on cross-world quantities to achieve identifiability. We establish asymptotic properties for the estimated counterfactual cumulative incidences and decomposed treatment effects. We illustrate the subtle difference between these 2 decompositions through simulation studies and two real-data applications in the Supplementary Materials.

Testing conditional quantile independence with functional covariate.

We propose a new non-parametric conditional independence test for a scalar response and a functional covariate over a continuum of quantile levels. We build a Cramer-von Mises type test statistic based on an empirical process indexed by random projections of the functional covariate, effectively avoiding the "curse of dimensionality" under the projected hypothesis, which is almost surely equivalent to the null hypothesis. The asymptotic null distribution of the proposed test statistic is obtained under some mild assumptions. The asymptotic global and local power properties of our test statistic are then investigated. We specifically demonstrate that the statistic is able to detect a broad class of local alternatives converging to the null at the parametric rate. Additionally, we recommend a simple multiplier bootstrap approach for estimating the critical values. The finite-sample performance of our statistic is examined through several Monte Carlo simulation experiments. Finally, an analysis of an EEG data set is used to show the utility and versatility of our proposed test statistic.

Regression models for average hazard.

Limitations of using the traditional Cox's hazard ratio for summarizing the magnitude of the treatment effect on time-to-event outcomes have been widely discussed, and alternative measures that do not have such limitations are gaining attention. One of the alternative methods recently proposed, in a simple 2-sample comparison setting, uses the average hazard with survival weight (AH), which can be interpreted as the general censoring-free person-time incidence rate on a given time window. In this paper, we propose a new regression analysis approach for the AH with a truncation time τ. We investigate 3 versions of AH regression analysis, assuming (1) independent censoring, (2) group-specific censoring, and (3) covariate-dependent censoring. The proposed AH regression methods are closely related to robust Poisson regression. While the new approach needs to require a truncation time τ explicitly, it can be more robust than Poisson regression in the presence of censoring. With the AH regression approach, one can summarize the between-group treatment difference in both absolute difference and relative terms, adjusting for covariates that are associated with the outcome. This property will increase the likelihood that the treatment effect magnitude is correctly interpreted. The AH regression approach can be a useful alternative to the traditional Cox's hazard ratio approach for estimating and reporting the magnitude of the treatment effect on time-to-event outcomes.

Bayesian inference for multivariate probit model with latent envelope.

The response envelope model proposed by Cook et al. (2010) is an efficient method to estimate the regression coefficient under the context of the multivariate linear regression model. It improves estimation efficiency by identifying material and immaterial parts of responses and removing the immaterial variation. The response envelope model has been investigated only for continuous response variables. In this paper, we propose the multivariate probit model with latent envelope, in short, the probit envelope model, as a response envelope model for multivariate binary response variables. The probit envelope model takes into account relations between Gaussian latent variables of the multivariate probit model by using the idea of the response envelope model. We address the identifiability of the probit envelope model by employing the essential identifiability concept and suggest a Bayesian method for the parameter estimation. We illustrate the probit envelope model via simulation studies and real-data analysis. The simulation studies show that the probit envelope model has the potential to gain efficiency in estimation compared to the multivariate probit model. The real data analysis shows that the probit envelope model is useful for multi-label classification.

Joint structure learning and causal effect estimation for categorical graphical models.

The scope of this paper is a multivariate setting involving categorical variables. Following an external manipulation of one variable, the goal is to evaluate the causal effect on an outcome of interest. A typical scenario involves a system of variables representing lifestyle, physical and mental features, symptoms, and risk factors, with the outcome being the presence or absence of a disease. These variables are interconnected in complex ways, allowing the effect of an intervention to propagate through multiple paths. A distinctive feature of our approach is the estimation of causal effects while accounting for uncertainty in both the dependence structure, which we represent through a directed acyclic graph (DAG), and the DAG-model parameters. Specifically, we propose a Markov chain Monte Carlo algorithm that targets the joint posterior over DAGs and parameters, based on an efficient reversible-jump proposal scheme. We validate our method through extensive simulation studies and demonstrate that it outperforms current state-of-the-art procedures in terms of estimation accuracy. Finally, we apply our methodology to analyze a dataset on depression and anxiety in undergraduate students.

Testing for similarity of multivariate mixed outcomes using generalized joint regression models with application to efficacy-toxicity responses.

A common problem in clinical trials is to test whether the effect of an explanatory variable on a response of interest is similar between two groups, for example, patient or treatment groups. In this regard, similarity is defined as equivalence up to a pre-specified threshold that denotes an acceptable deviation between the two groups. This issue is typically tackled by assessing if the explanatory variable's effect on the response is similar. This assessment is based on, for example, confidence intervals of differences or a suitable distance between two parametric regression models. Typically, these approaches build on the assumption of a univariate continuous or binary outcome variable. However, multivariate outcomes, especially beyond the case of bivariate binary responses, remain underexplored. This paper introduces an approach based on a generalized joint regression framework exploiting the Gaussian copula. Compared to existing methods, our approach accommodates various outcome variable scales, such as continuous, binary, categorical, and ordinal, including mixed outcomes in multi-dimensional spaces. We demonstrate the validity of this approach through a simulation study and an efficacy-toxicity case study, hence highlighting its practical relevance.