Covariate Effects Research Articles

Elucidating vaccine efficacy using a correlate of protection, demographics, and logistic regression

BackgroundVaccine efficacy (VE) assessed in a randomized controlled clinical trial can be affected by demographic, clinical, and other subject-specific characteristics evaluated as baseline covariates. Understanding the effect of covariates on efficacy is key to decisions by vaccine developers and public health authorities.MethodsThis work evaluates the impact of including correlate of protection (CoP) data in logistic regression on its performance in identifying statistically and clinically significant covariates in settings typical for a vaccine phase 3 trial. The proposed approach uses CoP data and covariate data as predictors of clinical outcome (diseased versus non-diseased) and is compared to logistic regression (without CoP data) to relate vaccination status and covariate data to clinical outcome.ResultsClinical trial simulations, in which the true relationship between CoP data and clinical outcome probability is a sigmoid function, show that use of CoP data increases the positive predictive value for detection of a covariate effect. If the true relationship is characterized by a decreasing convex function, use of CoP data does not substantially change positive or negative predictive value. In either scenario, vaccine efficacy is estimated more precisely (i.e., confidence intervals are narrower) in covariate-defined subgroups if CoP data are used, implying that using CoP data increases the ability to determine clinical significance of baseline covariate effects on efficacy.ConclusionsThis study proposes and evaluates a novel approach for assessing baseline demographic covariates potentially affecting VE. Results show that the proposed approach can sensitively and specifically identify potentially important covariates and provides a method for evaluating their likely clinical significance in terms of predicted impact on vaccine efficacy. It shows further that inclusion of CoP data can enable more precise VE estimation, thus enhancing study power and/or efficiency and providing even better information to support health policy and development decisions.

Functional Disability Due to Chronic Low Back Pain in the Geriatric Population of a Tertiary Care Hospital in North India: A Cross-Sectional Study.

Background Chronic low back pain (CLBP) is one of the painful and disabling conditions affecting the young as well as the geriatric population. There is a limited body of research to find out the impact of CLBP and functional disability on geriatric adults in the Indian region. Aim This study aims to determine the prevalence of functional disability due to CLBP in the geriatric population and to investigate the correlation between functional disability due to CLBP and other sociodemographic factors. Methodology A total of 157 geriatric adults were enrolled in the study, fulfilling the inclusion and exclusion criteria. Basic sociodemographic data, along with a clinical-radiological examination, was recorded. The Numeric Pain Rating Scale (NPRS), the Roland and Morris Disability Questionnaire, and the Quebec Back Pain Disability Scale were used as study tools. Summary measures (frequency, mean, median, etc.) are calculated according to the level of measurement of variables. The point prevalence of functional disability due to CLBP in the geriatric population, along with 95% confidence intervals, has been calculated. The prevalence estimates were estimated and calculated with SD variables using a t-test, chi-square test, or Fisher's exact test under bivariate analysis. The linear/logistic regression analysis was used to control for the effects of covariates. A significance level of 5% was set for all analyses due to the exploratory nature of the study. Statistical significance was considered at p < 0.05. Results According to the Roland and Morris Disability Questionnaire, 29% (N = 46) of the study geriatric participants had a severe disability, 45% (N = 70) had a moderate disability, and 26% (N = 41) had a mild disability. According to the Quebec Back Pain Disability Scale, 34% (N = 53) had scored more than 50, and 66% (N = 104) had scored less than 50. Statistically significant correlations have been found between the level of functional disability and intensity of pain (NPRS score), gender, associated illness, current and past occupation, and clinical diagnosis of CLBP (p < 0.05). Conclusion The prevalence of functional disability due to CLBP is higher in the geriatric population. It is associated with many influencing sociodemographic factors like gender, occupation, associated musculoskeletal illness, the intensity of low back pain, and clinico-radiological diagnosis. Early identification and timely interventions to reduce functional disability due to CLBP and associated risk factors are the need of the hour. Regular back muscle exercises, ergonomic modifications, and modification of activities of daily life are recommended to prevent functional disability due to CLBP.

Multilevel Latent Differential Structural Equation Model with Short Time Series and Time-Varying Covariates: A Comparison of Frequentist and Bayesian Estimators

Continuous-time modeling using differential equations is a promising technique to model change processes with longitudinal data. Among ways to fit this model, the Latent Differential Structural Equation Modeling (LDSEM) approach defines latent derivative variables within a structural equation modeling (SEM) framework, thereby allowing researchers to leverage advantages of the SEM framework for model building, estimation, inference, and comparison purposes. Still, a few issues remain unresolved, including performance of multilevel variations of the LDSEM under short time lengths (e.g., 14 time points), particularly when coupled multivariate processes and time-varying covariates are involved. Additionally, the possibility of using Bayesian estimation to facilitate the estimation of multilevel LDSEM (M-LDSEM) models with complex and higher-dimensional random effect structures has not been investigated. We present a series of Monte Carlo simulations to evaluate three possible approaches to fitting M-LDSEM, including: frequentist single-level and two-level robust estimators and Bayesian two-level estimator. Our findings suggested that the Bayesian approach outperformed other frequentist approaches. The effects of time-varying covariates are well recovered, and coupling parameters are the least biased especially using higher-order derivative information with the Bayesian estimator. Finally, an empirical example is provided to show the applicability of the approach.

Comparison of smokers’ mortality with non-smokers following out-of-hospital cardiac arrests: a systematic review and meta-analysis

ObjectiveAlthough some studies have linked smoking to mortality after out-of-hospital cardiac arrests (OHCAs), data regarding smoking and mortality after OHCAs have not yet been discussed in a meta-analysis. Thus, this study conducted this systematic review to clarify the association.MethodsThe study searched Medline-PubMed, Web of Science, Embase and Cochrane libraries between January 1972 and July 2022 for studies that evaluated the association between smoking and mortality after OHCAs. Studies that reportedly showed relative risk estimates with 95% confidence intervals (CIs) were included.ResultsIncorporating a collective of five studies comprising 2477 participants, the analysis revealed a lower mortality risk among smokers in the aftermath of OHCAs compared with non-smokers (odds ratio: 0.77; 95% CI 0.61–0.96; P < 0.05). Egger's test showed no publication bias in the relationship between smoking and mortality after OHCAs.ConclusionsAfter experiencing OHCAs, smokers had lower mortality than non-smokers. However, due to the lack of data, this ‘smoker’s paradox’ still needs other covariate effects and further studies to be considered valid.

The association between circadian syndrome and chronic kidney disease in an aging population: a 4-year follow-up study.

Circadian syndrome (CircS) is proposed as a novel risk cluster based on reduced sleep duration, abdominal obesity, depression, hypertension, dyslipidemia and hyperglycemia. However, the association between CircS and chronic kidney disease (CKD) remains unclear. To investigate the cross-sectional and longitudinal association between CircS and CKD, this study was performed. A national prospective cohort (China Health and Retirement Longitudinal Study, CHARLS) was used in this study. To define CKD, the estimated glomerular filtration rate (eGFR) was calculated based on the 2012 CKD-EPI creatinine-cystatin C equation. Participants with eGFR <60 mL.min-1/1.73/m2 were diagnosed with CKD. Multivariate binary logistic regression was used to assess the cross-sectional association between CircS and CKD. Subgroup and interactive analyses were performed to determine the interactive effects of covariates. In the sensitivity analysis, the obese population was excluded and another method for calculating the eGFR was used to verify the robustness of previous findings. In addition, participants without CKD at baseline were followed up for four years to investigate the longitudinal relationship between CircS and CKD. A total of 6355 participants were included in this study. In the full model, CircS was positively associated with CKD (OR = 1.28, 95% CI = 1.04-1.59, P < 0.05). As per one increase of CircS components, there was a 1.11-fold (95% CI = 1.04-1.18, P < 0.05) risk of prevalent CKD in the full model. A significant interactive effect of hyperuricemia in the CircS-CKD association (P for interaction < 0.01) was observed. Sensitivity analyses excluding the obese population and using the 2009 CKD-EPI creatinine equation to diagnose CKD supported the positive correlation between CircS and CKD. In the 2011-2015 follow-up cohort, the CircS group had a 2.18-fold risk of incident CKD (95% CI= 1.33-3.58, P < 0.01) in the full model. The OR was 1.29 (95% CI = 1.10-1.51, P < 0.001) with per one increase of CircS components. CircS is a risk factor for CKD and may serve as a predictor of CKD for early identification and intervention.

Environmental DNA mitochondrial markers to assess potential occupancy of Endangered Yaqui catfish in the Yaqui River basin, Mexico

Acquiring data on rare and threatened species can be challenging, particularly in remote areas. Environmental DNA (eDNA) offers a less effort-intensive method for detecting species compared to physical fish sampling methods. In our study, we focused on the Endangered Yaqui catfish Ictalurus pricei, a freshwater fish endemic to the Sonoran desert in Arizona, USA, and Sonora, Mexico, and the non-native channel catfish I. punctatus. We developed and employed mitochondrial eDNA markers to sample 35 locations in the Yaqui River basin in Mexico and employed a hierarchical Bayesian formulation of a co-occurrence model to investigate the interactions between the species while accounting for the effects of covariates on species occupancy and detection. Our best model included the influence of channel catfish mitochondrial eDNA on detecting Yaqui catfish mitochondrial eDNA, and we found that channel catfish mitochondrial eDNA detection was negatively related with water temperature and elevation but positively related to substrate size. Yaqui catfish occupancy, as determined with mitochondrial eDNA detection, was best explained by stream permanence and the presence of forested areas, while channel catfish mitochondrial eDNA occurrences were also associated with stream permanence, as well as conifer and shrub-dominated landscapes. Non-native channel catfish mitochondrial eDNA was found in all but 5 locations where Yaqui catfish mitochondrial eDNA was detected, indicating a high likelihood of interaction and hybridization. This potential for hybridization poses a significant threat to the already Endangered Yaqui catfish, emphasizing the need to protect and secure remaining populations for their long-term survival.

Reduction in time to viral suppression among persons living with HIV in Jamaica between 2017-2019.

HIV viral suppression is important for effective treatment and for reducing new infections. In 2019, only 66% of persons on antiretroviral treatment (ART) in Jamaica were virally suppressed. We aim to compare time to viral suppression by ART initiation year and type of treatment site to understand the implications for programming. We assessed time to viral suppression among 4560 persons who received viral load testing either pre or post ART initiation from 2017-2019. We used descriptive statistics and Kaplan-Meier estimates to compare survival curves by ART year (2017, 2018, 2019), sex and type of treatment site (public and non-governmental organizations). Persons were censored if suppression was not achieved. Mixed effects Cox regression was used to determine the effect of covariates on the likelihood of viral suppression. We report hazard ratios and 95% confidence intervals. Pre-ART viral load testing decreased from 36% in 2017 to 30% in 2019. For post-ART viral load tests, approximately 78% (n = 1589) of persons achieved suppression, 51% (n = 809) were female and 86% (n = 1341) used a public treatment site. The median time to suppression decreased by 3 months from 2017 to 2019. The likelihood of suppression was almost 2 times greater in 2018 (HR = 1.56, CI = 1.39-1.75) and 3 times greater in 2019 (HR = 3.17, CI = 2.76-3.64) compared to 2017. NGO treatment sites were also significantly associated with the likelihood of viral suppression compared to public sites. Pre-ART viral load testing and the time to viral suppression decreased over three years. Initiating ART after 2017 and early use of NGO treatment sites were found to significantly increase the likelihood of achieving suppression. This demonstrates improvements in the national HIV response but there is need to increase the number of persons on ART and achieving viral suppression.

Effect of Histamine H2 Receptor Antagonists on All-Cause Mortality in Critically Ill Patients With Essential Hypertension: A Retrospective Cohort Study.

Previous studies found that histamine H2 receptor antagonists (H2RAs) had blood pressure lowering and cardioprotective effects, but the impact of H2RAs on the survival outcomes of critically ill patients with essential hypertension is still unclear. The aim of this study was to investigate the association of H2RAs exposure with all-cause mortality in patients with essential hypertension based on Medical Information Mart for Intensive Care III database. A total of 17,739 patients were included, involving 8482 H2RAs users and 9257 non-H2RAs users. Propensity score matching (PSM) was performed to improve balance between 2 groups that were exposed to H2RAs or not. Kaplan-Meier survival curves were used to compare the cumulative survival rates and multivariable Cox regression models were performed to evaluate the association between H2RAs exposure and all-cause mortality. After 1:1 PSM, 4416 pairs of patients were enrolled. The results revealed potentially significant association between H2RAs exposure and decreased 30-day, 90-day, and 1-year mortalities in multivariate analyses (HR = 0.783, 95% CI: 0.696-0.882 for 30-day; HR = 0.860, 95% CI: 0.778-0.950 for 90-day; and HR = 0.883, 95% CI: 0.811-0.961 for 1-year mortality, respectively). Covariate effect analyses showed that the use of H2RAs was more beneficial in essential hypertension patients with age ≥ 60, BMI ≥ 25 kg/m2, coronary arteriosclerosis, stroke, and acute kidney failure, respectively. In conclusion, H2RAs exposure was related to lower mortalities in critically ill patients with essential hypertension, which provided novel potential strategy for the use of H2RAs in essential hypertension patients.

Statistical Inference For Noisy Matrix Completion Incorporating Auxiliary Information

This article investigates statistical inference for noisy matrix completion in a semi-supervised model when auxiliary covariates are available. The model consists of two parts. One part is a low-rank matrix induced by unobserved latent factors; the other part models the effects of the observed covariates through a coefficient matrix which is composed of high-dimensional column vectors. We model the observational pattern of the responses through a logistic regression of the covariates, and allow its probability to go to zero as the sample size increases. We apply an iterative least squares (LS) estimation approach in our considered context. The iterative LS methods in general enjoy a low computational cost, but deriving the statistical properties of the resulting estimators is a challenging task. We show that our method only needs a few iterations, and the resulting entry-wise estimators of the low-rank matrix and the coefficient matrix are guaranteed to have asymptotic normal distributions. As a result, individual inference can be conducted for each entry of the unknown matrices. We also propose a simultaneous testing procedure with multiplier bootstrap for the high-dimensional coefficient matrix. This simultaneous inferential tool can help us further investigate the effects of covariates for the prediction of missing entries. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

SpVC for the detection and interpretation of spatial gene expression variation

Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity in spatial contexts. However, existing methods for identifying spatially variable genes often focus solely on statistical significance, limiting their ability to capture continuous expression patterns and integrate spot-level covariates. To address these challenges, we introduce spVC, a statistical method based on a generalized Poisson model. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. Simulation and real data applications confirm spVC’s accuracy in these tasks, highlighting its versatility in spatial transcriptomics analysis.

Partial-linear single-index transformation models with censored data.

In studies with time-to-event outcomes, multiple, inter-correlated, and time-varying covariates are commonly observed. It is of great interest to model their joint effects by allowing a flexible functional form and to delineate their relative contributions to survival risk. A class of semiparametric transformation (ST) models offers flexible specifications of the intensity function and can be a general framework to accommodate nonlinear covariate effects. In this paper, we propose a partial-linear single-index (PLSI) transformation model that reduces the dimensionality of multiple covariates into a single index and provides interpretable estimates of the covariate effects. We develop an iterative algorithm using the regression spline technique to model the nonparametric single-index function for possibly nonlinear joint effects, followed by nonparametric maximum likelihood estimation. We also propose a nonparametric testing procedure to formally examine the linearity of covariate effects. We conduct Monte Carlo simulation studies to compare the PLSI transformation model with the standard ST model and apply it to NYU Langone Health de-identified electronic health record data on COVID-19 hospitalized patients' mortality and a Veteran's Administration lung cancer trial.

Association between maternal mental health-related hospitalisation in the 5 years prior to or during pregnancy and adverse birth outcomes: a population-based retrospective cohort data linkage study in the Northern Territory of Australia

Mental health conditions prior to or during pregnancy that are not addressed can have adverse consequences for pregnancy and birth outcomes. This study aimed to determine the extent to which women's mental health-related hospitalisation (MHrH) prior to or during pregnancy was associated with a risk of adverse birth outcomes. We linked the perinatal data register for all births in the Northern Territory, Australia, from the year 1999 to 2017, to hospital admissions records to create a cohort of births to women aged 15-44 years with and without MHrH prior to or during pregnancy. We used Modified Poisson Regression and Latent Class Analysis to assess the association between maternal MHrH and adverse birth outcomes (i.e., stillbirth, preterm birth, low birth weight, and short birth length). We explored a mediation effect of covariates on theoretical causal paths. We calculated the adjusted Population Attributable Fraction (PAF) and Preventive Fractions for the Population (PFP) for valid associations. From 72,518 births, 70,425 births (36.4% for Aboriginal women) were included in the analysis. The Latent Class Analys identified two classes: high (membership probability of 10.5%) and low adverse birth outcomes. Births to Aboriginal women with MHrH were around two times more likely to be in the class of high adverse birth outcomes. MHrH prior to or during pregnancy increased the risk of all adverse birth outcomes in both populations with risk ranging from 1.19 (95% CI: 1.05, 1.35) to 7.89 (1.17, 53.37). Eight or more antenatal care visits and intrauterine growth restriction mostly played a significant mediation role between maternal MHrH and adverse birth outcomes with mediation effects ranging from 1.04 (1.01, 1.08) to 1.39 (1.14, 1.69). MHrH had a low to high population impact with a PAF ranging from 16.1% (5.1%, 25.7%) to 87.3% (14.3%, 98.1%). Eight or above antenatal care visits avert extra adverse birth outcomes that range from 723 (332-765) stillbirths to 3003 (1972-4434) preterm births. Maternal MHrH is a modifiable risk factor that explained a low to moderate risk of adverse birth outcomes in the Northern Territory. The knowledge highlights the need for the development and implementation of preconception mental health care into routine health services. The Child and Youth Development Research Partnership (CYDRP) data repository is supported by a grant from the Northern Territory Government.

Predictive value of the systemic immune-inflammation index on one-year mortality in geriatric hip fractures.

Geriatric hip fractures are associated with a high incidence of mortality. This study examines the predictive value of the systemic immune-inflammation index (SII) on one-year mortality in elderly hip fracture patients. A single-center retrospective study was conducted between February 2017 and October 2020. Three hundred and eleven surgically treated consecutive hip fracture patients were included in the study. Admission, postoperative first day, and postoperative fifth-day SII values were calculated. The receiver operating characteristic (ROC) curve was used to calculate the cut-off values, and patients were divided into high and low groups according to these cut-off values. After univariate Cox regression analysis, significant factors were included in the multivariate Cox proportional hazards model to adjust the effect of covariates and explore independent predictive factors associated with mortality. Further subgroup analysis was performed to evaluate the accuracy of the results for different clinical and biological characteristics. The mean age was 80.7 ± 8.0 years, and women made up the majority (67.8%) of the patients. The one-year mortality rate was 28.0%. After univariate and multivariate analyses, high postoperative fifth-day SII remained an independent predictor of one-year mortality (adjusted HR 2.16, 95% CI 1.38-3.38, p = 0.001). Older age, male gender, Charlson comorbidity index (CCI) ≥ 2, and hypoalbuminemia were found to be other independent predictors. The optimal cut-off value of the postoperative fifth-day SII was calculated at 1751.9 units (p < 0.001). The postoperative fifth-day SII is a simple and useful inflammatory biomarker for predicting one-year mortality in patients with hip fracture.

Five multivariate Duchenne muscular dystrophy progression models bridging six-minute walk distance and MRI relaxometry of leg muscles.

The study aimed to provide quantitative information on the utilization of MRI transverse relaxation time constant (MRI-T2) of leg muscles in DMD clinical trials by developing multivariate disease progression models of Duchenne muscular dystrophy (DMD) using 6-min walk distance (6MWD) and MRI-T2. Clinical data were collected from the prospective and longitudinal ImagingNMD study. Disease progression models were developed by a nonlinear mixed-effect modeling approach. Univariate models of 6MWD and MRI-T2 of five muscles were developed separately. Age at assessment was the time metric. Multivariate models were developed by estimating the correlation of 6MWD and MRI-T2 modelvariables. Full model estimation approach for covariate analysis and five-fold cross validation were conducted. Simulations were performed to compare the models and predict the covariate effects on the trajectories of 6MWD and MRI-T2. Sigmoid Imax and Emax models best captured the profiles of 6MWD and MRI-T2 over age. Steroid use, baseline 6MWD, and baseline MRI-T2 were significant covariates. The median age at which 6MWD is half of its maximum decrease in the five models was similar, while the median age at which MRI-T2 is half of its maximum increase varied depending on the type of muscle. The models connecting 6MWD and MRI-T2 successfully quantified how individual characteristics alter disease trajectories. The models demonstrate a plausible correlation between 6MWD and MRI-T2, supporting the use of MRI-T2. The developed models will guide drug developers in using the MRI-T2 to most efficient use in DMD clinical trials.

Multivariate Multinomial Logit Models with Associations among Dependent Variables

The authors introduce a new group of multinomial logit models with special contrasts to identify covariate effects on multiple categorical dependent variables that are strongly associated with each other. The authors first develop the method for a case with two dependent variables and then extend the method to a case with three dependent variables. The model can account for both nominal and ordinal scales of categorical dependent variables. The authors formulate the covariate effects to represent unique effects on each dependent variable so that they become independent across different dependent variables. The application focuses on the multiplicity of occupational attainments by analyzing how gender, race, educational attainment, and parental occupation characteristics affect three distinct but nonindependent dimensions of occupations: socioeconomic status, social skill level, and math and science skill levels.

Semi-parametric Spatio-Temporal Hawkes Process for Modelling Road Accidents in Rome

AbstractWe propose a semi-parametric spatio-temporal Hawkes process with periodic components to model the occurrence of car accidents in a given spatio-temporal window. The overall intensity is split into the sum of a background component capturing the spatio-temporal varying intensity and an excitation component accounting for the possible triggering effect between events. The spatial background is estimated and evaluated on the road network, allowing the derivation of accurate risk maps of road accidents. We constrain the spatio-temporal excitation to preserve an isotropic behaviour in space, and we generalize it to account for the effect of covariates. The estimation is pursued by maximizing the expected complete data log-likelihood using a tailored version of the stochastic-reconstruction algorithm that adopts ad hoc boundary correction strategies. An original application analyses the car accidents that occurred on the Rome road network in the years 2019, 2020, and 2021. Results highlight that car accidents of different types exhibit varying degrees of excitation, ranging from no triggering to a 10% chance of triggering further events.

A clinical score to predict recovery in end-stage kidney disease due to acute kidney injury.

Acute kidney injury (AKI) is a major contributor to end-stage kidney disease (ESKD). About one-third of patients with ESKD due to AKI recover kidney function. However, the inability to accurately predict recovery leads to improper triage of clinical monitoring and impacts the quality of care in ESKD. Using data from the United States Renal Data System from 2005 to 2014 (n=22922), we developed a clinical score to predict kidney recovery within 90 days and within 12 months after dialysis initiation in patients with ESKD due to AKI. Multivariable logistic regressions were used to examine the effect of various covariates on the primary outcome of kidney recovery to develop the scoring system. The resulting logistic parameter estimates were transformed into integer point totals by doubling and rounding the estimates. Internal validation was performed. Twenty-four percent and 34% of patients with ESKD due to AKI recovered kidney function within 90 days and 12 months, respectively. Factors contributing to points in the two scoring systems were similar but not identical, and included age, race/ethnicity, body mass index, congestive heart failure, cancer, amputation, functional status, hemoglobin and prior nephrology care. Three score categories of increasing recovery were formed: low score (0-6), medium score (7-9) and high score (10-12), which exhibited 90-day recovery rates of 12%, 26% and 57%. For the 12-month scores, the low, medium and high groups consisted of scores 0-5, 6-8 and 9-11, with 12-month recovery rates of 16%, 33% and 62%, respectively. The internal validation assessment showed no overfitting of the models. A clinical score derived from information available at incident dialysis predicts renal recovery at 90 days and 12 months in patients with presumed ESKD due to AKI. The score can help triage appropriate monitoring to facilitate recovery and begin planning long-term dialysis care for others.

Mesoscale activity drives the habitat suitability of yellowfin tuna in the Gulf of Mexico

Yellowfin tuna, Thunnus albacares, represents an important component of commercial and recreational fisheries in the Gulf of Mexico (GoM). We investigated the influence of environmental conditions on the spatiotemporal distribution of yellowfin tuna using fisheries’ catch data spanning 2012–2019 within Mexican waters. We implemented hierarchical Bayesian regression models with spatial and temporal random effects and fixed effects of several environmental covariates to predict habitat suitability (HS) for the species. The best model included spatial and interannual anomalies of the absolute dynamic topography of the ocean surface (ADTSA and ADTIA, respectively), bottom depth, and a seasonal cyclical random effect. High catches occurred mainly towards anticyclonic features at bottom depths > 1000 m. The spatial extent of HS was higher in years with positive ADTIA, which implies more anticyclonic activity. The highest values of HS (> 0.7) generally occurred at positive ADTSA in oceanic waters of the central and northern GoM. However, high HS values (> 0.6) were observed in the southern GoM, in waters with cyclonic activity during summer. Our results highlight the importance of mesoscale features for the spatiotemporal distribution of yellowfin tunas and could help to develop dynamic fisheries management strategies in Mexico and the U.S. for this valuable resource.

High‐dimensional feature screening for nonlinear associations with survival outcome using restricted mean survival time

SummaryFeature screening is an important tool in analysing ultrahigh‐dimensional data, particularly in the field of Omics and oncology studies. However, most attention has been focused on identifying features that have a linear or monotonic impact on the response variable. Detecting a sparse set of variables that have a nonlinear or nonmonotonic relationship with the response variable is still a challenging task. To fill the gap, this paper proposed a robust model‐free screening approach for right‐censored survival data by providing a new perspective of quantifying the covariate effect on the restricted mean survival time, rather than the routinely used hazard function. The proposed measure, based on the difference between the restricted mean survival time of covariate‐stratified and overall data, is able to identify comprehensive types of associations including linear, nonlinear, nonmonotone and even local dependencies like change points. The sure screening property is established, and a more flexible iterative screening procedure is developed to increase the accuracy of the variable screening. Simulation studies are carried out to demonstrate the superiority of the proposed method in selecting important features with a complex association with the response variable. The potential of applying the proposed method to handle interval‐censored failure time data has also been explored in simulations, and the results have been promising. The method is applied to a breast cancer dataset to identify potential prognostic factors, which reveals potential associations between breast cancer and lymphoma.

Covariate adjusted meta-analytic predictive (CA-MAP) prior for historical borrowing using patient-level data

ABSTRACT Utilization of historical data is increasingly common for gaining efficiency in the drug development and decision-making processes. The underlying issue of between-trial heterogeneity in clinical trials is a barrier in making these methods standard practice in the pharmaceutical industry. Common methods for historical borrowing discount the borrowed information based on the similarity between outcomes in the historical and current data. However, individual clinical trials and their outcomes are intrinsically heterogenous due to differences in study design, patient characteristics, and changes in standard of care. Additionally, differences in covariate distributions can produce inconsistencies in clinical outcome data between historical and current data when there may be a consistent covariate effect. In such scenario, borrowing historical data is still advantageous even though the population level outcome summaries are different. In this paper, we propose a covariate adjusted meta-analytic-predictive (CA-MAP) prior for historical control borrowing. A MAP prior is assigned to each covariate effect, allowing the amount of borrowing to be determined by the consistency of the covariate effects across the current and historical data. This approach integrates between-trial heterogeneity with covariate level heterogeneity to tune the amount of information borrowed. Our method is unique as it directly models the covariate effects instead of using the covariates to select a similar population to borrow from. In summary, our proposed patient-level extension of the MAP prior allows for the amount of historical control borrowing to depend on the similarity of covariate effects rather than similarity in clinical outcomes.