Covariate Effects Research Articles

Association between Jewish religious observance and mental health among Israeli adults: Findings from the Global Flourishing Study.

This study investigates the association between Jewish religious observance and several indicators of mental health and psychological well-being among Jewish Israeli adults. Data are from adult (18+) Jewish respondents in the Israeli sample (N = 2958) of the first wave of the Global Flourishing Study (GFS), a 22-nation population survey which will eventually consist of five annual waves of panel data. The GFS Israeli data were collected via a randomized, stratified, probability-based sampling design, and contained dozens of indicators of sociodemographic, socioeconomic, political, religious, health-related, and other constructs. Measures of Jewish religious observance, including religious service attendance, prayer, scripture reading, belief in God, and importance of Judaism, are statistically significant predictors of several single-item indicators of mental health (overall mental health, depression, anxiety) and psychological well-being (suffering, happiness, life satisfaction). Greater religious observance is associated with a higher self-rating of overall mental health, less depression and anxiety, less suffering, and greater happiness and life satisfaction. Nearly all results withstood adjusting for effects of several sociodemographic covariates. These results offer confirmation of prior studies using smaller samples or non-population-based designs and with fewer mental health and religious indicators. They suggest that evidence for a salutary association between religious observance and mental health or psychological well-being among Israeli Jewish adults is consistent with findings in this literature for adherents to other faith traditions throughout the world.

Role Of Socioeconomics, Psychological State, and Attitude Toward Care Giving on Quality of Life of Dependent Patients’ Caregivers in the Northeast of Thailand

Background Caring for patients with dependency is a burden on primary caregivers, which impacts them economically, socially, and psychologically. The perception of caregiving and the psychological state associated with it further contribute to these effects. Objectives This cross-sectional analytical study aimed to study the socioeconomic and caregiving burden factors associated with the Quality of Life (QOL) of dependent patients’ caregivers in the Northeastern region of Thailand. Methodology A total of 1,335 dependent patients’ caregivers aged 18–59 years in the Northeastern Region of Thailand were selected by multistage random sampling to respond to a self-administered structured questionnaire. The Generalized Linear Mixed Model (GLMM) was performed to identify socioeconomic and caregiving burden factors associated with QOL while controlling the effects of covariates, presenting Adjusted Odds Ratio (AOR) and 95% Confidence Intervals (CI). Results Among 1,335 dependent patients’ caregivers, more than half of them had poor QOL (58.05%; 95% CI:39.28 - 44.65). Factors associated with poor QOL were age 46-59 years (AOR=4.30; 95% CI: 2.84-6.51, p-value<0.001), insufficient financial status with debt (AOR=5.89; 95% CI: 3.85-9.01, p-value<0.001), low caregiving knowledge level (AOR=2.43; 95% CI: 1.63-3.64, p-value<0.001), average to low attitude caregiving level (AOR=4.45; 95% CI: 3.30-5.99, p-value <0.001), and having depression (AOR = 3.57; 95% CI: 1.93-6.59, p-value<0.001). Conclusion The findings from this study have important implications for healthcare practice and policy—interventions aimed at improving the QOL of caregivers.

A semiparametric accelerated failure time-based mixture cure tree

The mixture cure rate model (MCM) is the most widely used model for the analysis of survival data with a cured subgroup. In this context, the most common strategy to model the cure probability is to assume a generalized linear model with a known link function, such as the logit link function. However, the logit model can only capture simple effects of covariates on the cure probability. In this article, we propose a new MCM where the cure probability is modeled using a decision tree-based classifier and the survival distribution of the uncured is modeled using an accelerated failure time structure. To estimate the model parameters, we develop an expectation maximization algorithm. Our simulation study shows that the proposed model performs better in capturing nonlinear classification boundaries when compared to the logit-based MCM and the spline-based MCM. This results in more accurate and precise estimates of the cured probabilities, which in-turn results in improved predictive accuracy of cure. We further show that capturing nonlinear classification boundary also improves the estimation results corresponding to the survival distribution of the uncured subjects. Finally, we apply our proposed model and the EM algorithm to analyze an existing bone marrow transplant data.

Joint regression analysis of clustered current status data with latent variables.

Clustered current status data frequently occur in many fields of survival studies. Some potential factors related to the hazards of interest cannot be directly observed but are characterized through multiple correlated observable surrogates. In this article, we propose a joint modeling method for regression analysis of clustered current status data with latent variables and potentially informative cluster sizes. The proposed models consist of a factor analysis model to characterize latent variables through their multiple surrogates and an additive hazards frailty model to investigate covariate effects on the failure time and incorporate intra-cluster correlations. We develop an estimation procedure that combines the expectation-maximization algorithm and the weighted estimating equations. The consistency and asymptotic normality of the proposed estimators are established. The finite-sample performance of the proposed method is assessed via a series of simulation studies. This procedure is applied to analyze clustered current status data from the National Toxicology Program on a tumorigenicity study given by the United States Department of Health and Human Services.

Isotonic Estimation of Additive Covariate Effects Under Proportional Hazard Models

Isotonic Estimation of Additive Covariate Effects Under Proportional Hazard Models

Progression-free survival versus post-progression survival and overall survival in WHO grade 2 gliomas.

Progression-free survival (PFS) remains to be validated as an outcome measure for diffuse WHO grade 2 gliomas, and knowledge about the relationships between PFS, post-progression survival (PPS), and overall survival (OS) in this subset of tumors is limited. We sought to assess correlations between PFS and OS, and identify factors associated with PFS, PPS, and OS in patients treated for diffuse supratentorial WHO grade 2 gliomas. We included 319 patients from three independent observational cohorts. The correlation between PFS and OS was analyzed using independent exponential distributions for PFS and time from progression to death. Cox proportional hazards models were used to determine the effects of covariates on PFS, PPS, and OS. The overall correlation between PFS and OS was rs0.31. The correlation was rs 0.37 for astrocytomas and rs 0.19 for oligodendrogliomas. Longer PFS did not predict longer PPS. Patients with astrocytomas had shorter PFS, PPS, and OS. Larger preoperative tumor volume was a risk factor for shorter PFS, while older age was a risk factor for shorter PPS and OS. Patients who received early radio- and chemotherapy had longer PFS, but shorter PPS and OS. We found a weak correlation between PFS and OS in WHO grade 2 gliomas, with the weakest correlation observed in oligodendrogliomas. Our analyses did not demonstrate any association between PFS and PPS. Critically, predictors of PFS are not necessarily predictors of OS. There is a need for validation of PFS as an endpoint in diffuse WHO grade 2 gliomas.

Tympanostomy Tube Insertion With and Without Adenoidectomy

The benefit of adenoidectomy on otologic outcomes after tympanostomy tube (TT) insertion is unclear. Results from prior work are challenging to interpret due to small sample sizes, heterogeneous study designs, and varying outcome measures. To evaluate the association between adenoidectomy and otologic outcomes using a US population-level sample of children who received TTs, producing generalizable results for widespread clinical application. A matched cohort study was conducted using claims data from the Merative MarketScan Research Databases. The study included 601 848 children who received TTs between January 1, 2007, and December 31, 2021. Children who received adenoidectomy and TTs simultaneously (Ad+TT) were identified irrespective of the number of prior TTs. Control participants who received TTs without adenoidectomy were matched based on sex, age at the time of the procedure, and the number of prior TT procedures. Adenoidectomy without tonsillectomy was the primary exposure. The primary outcomes were repeat TT insertion and subsequent oral antibiotic prescriptions after TT insertions. Multivariable logistic regression was used to quantify the effects of adenoidectomy and covariates on each outcome. Stratified analyses were performed in children younger than 4 years and 4 years or older. Overall, 601 848 children (median [IQR] age, 2 [1-4] years; range, 0-11 years; 351 078 [58.3%] male) who received TTs were identified. The Ad+TT cohort included 201 932 children, with an equal number in the matched cohort. In children younger than 4 years, Ad+TT was common and was associated with lower odds of subsequent oral antibiotics (odds ratio [OR], 0.59; 95% CI, 0.58-0.60) but higher odds of repeat TT insertions (OR, 1.24; 95% CI, 1.22-1.27). In children 4 years or older, Ad+TT was associated with lower odds of repeat TT insertions (OR, 0.78; 95% CI, 0.75-0.81) and subsequent oral antibiotics (OR, 0.63; 95% CI, 0.62-0.65). This study found that in children younger than 4 years, Ad+TT was commonly performed and may have had a secondary benefit of reducing subsequent oral antibiotic courses; however, it was not associated with a reduction in the risks of repeat TT insertions. In children 4 years or older, Ad+TT was associated with a reduction in the risk of repeat TT insertions and subsequent oral antibiotics. Given these findings, Ad+TT may be offered in children 4 years or older to improve otologic outcomes.

Population Pharmacokinetic and Pharmacodynamic of Atorvastatin in Chinese Lung Transplant Recipients.

Atorvastatin is a widely prescribed cholesterol-lowering drug that inhibits 3-hydroxy-3-methylglutaryl coenzyme A reductase to reduce lipid levels. This study aimed to establish a population pharmacokinetic and pharmacodynamic model for atorvastatin in Chinese lung transplant recipients (LTRs), particularly those using voriconazole (VOR) and with different genotypes. It evaluated precise dosing regimens and analyzed the correlation between atorvastatin exposure and clinical outcomes. A nonlinear mixed-effects model was used for the population pharmacokinetic/pharmacodynamic (PK/PD) analysis. A one-compartment population PK model was developed, incorporating VOR, SLCO2A1 rs76906503, and SLC22A8 rs2187383 to assess apparent clearance and volume of distribution. LDL-C was modeled as a biomarker to evaluate atorvastatin efficacy. A Monte Carlo simulation was conducted to assess various dosing schemes and the effects of different covariates on achieving the target LDL concentration. The correlation between atorvastatin exposure and clinical outcomes was also evaluated. Results indicated that the average probability of target attainment for optimal dosing regimens across various covariate results exceeded 45.8%. Dosages of 10, 20, and 40 mg were deemed suitable for LTRs. A lower dose was recommended for LTRs taking VOR or with mutant-type genotypes to avoid overexposure and adverse reactions. The population PK/PD model offers valuable guidance for evaluating atorvastatin dosing regimens in clinical settings, particularly for LTRs using VOR and those with different genotypes.

Do non-farmers pay more for land than farmers?

Abstract The increase in farmland prices in many parts of the world over the past decade has sparked discussions about whether non-farmers pay higher prices for farmland. This study uses a causal machine learning approach with a rich data set of land transactions in Germany to quantify the potential price premium paid by non-farmers. By applying the causal forest method, we uncover the heterogeneity of price premiums and reveal moderating effects of covariates. We find that the average positive price premium by non-farmers decreases with parcel size and distance to a highway exit.

Differing effectiveness of transcranial random noise stimulation and transcranial direct current stimulation for enhancing working memory in healthy individuals: a randomized controlled trial

BackgroundTranscranial direct current stimulation (tDCS) applied to the left dorsolateral prefrontal cortex (DLPFC) is a promising technique for enhancing working memory (WM) performance in healthy and psychiatric populations. However, limited information is available about the effectiveness of transcranial random noise stimulation (tRNS) applied to the left DLPFC on WM. This study investigated the effectiveness of tRNS on WM compared with that of tDCS, which has established functional evidence.MethodsThis randomized, double-blind, sham-controlled trial enrolled 120 healthy right-handed adults who were randomly allocated to four stimulation groups: tRNS + direct current (DC) offset, tRNS, tDCS, or sham. Each stimulus was placed over the left DLPFC and had a current intensity of 2 mA applied for 20 min during the dual n-back task. The dual n-back task was repeated thrice: pre-stimulation, during stimulation, and post-stimulation. The d-prime scores, and response times were calculated as the main outcome measures. A linear mixed model was created to identify the main effects and interactions between the groups and times, with the group and time as fixed effects, and baseline performance and the subject as a covariate and random effect, respectively. The relationships between the benefit of each stimulus and baseline WM performance were also examined.ResultsFor the d-prime score during stimulation, the tRNS group significantly performed better than the sham group at online assessment (β = 0.310, p = 0.001). In the relationships between the benefit of each stimulus and baseline WM performance, the tRNS group had significantly larger negative line slopes than the sham group for the d-prime score (β = −0.233, p = 0.038).ConclusionstRNS applied to the left DLPFC significantly improved WM performance and generated greater benefits for healthy individuals with lower WM performance. These findings highlight the potential utility of tRNS for enhancing WM performance in individuals with lower WM performance and contribute evidence for clinical application to patients with cognitive decline.Trial RegistrationThis study was registered in the University Hospital Medical Information Network Clinical Trial Registry in Japan (UMIN000047365) on April 1, 2022; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000054021.

Conditional modeling of recurrent event data with terminal event.

Recurrent event data with a terminal event arise in follow-up studies. The current literature has primarily focused on the effect of covariates on the recurrent event process using marginal estimating equation approaches or joint modeling approaches via frailties. In this article, we propose a conditional model for recurrent event data with a terminal event, which provides an intuitive interpretation of the effect of the terminal event: at an early time, the rate of recurrent events is nearly independent of the terminal event, but the dependence gets stronger as time goes close to the terminal event time. A two-stage likelihood-based approach is proposed to estimate parameters of interest. Asymptotic properties of the estimators are established. The finite-sample behavior of the proposed method is examined through simulation studies. A real data of colorectal cancer is analyzed by the proposed method for illustration.

Deep Neural Network-Based Accelerated Failure Time Models Using Rank Loss.

An accelerated failure time (AFT) model assumes a log-linear relationship between failure times and a set of covariates. In contrast to other popular survival models that work on hazard functions, the effects of covariates are directly on failure times, the interpretation of which is intuitive. The semiparametric AFT model that does not specify the error distribution is sufficiently flexible and robust to depart from the distributional assumption. Owing to its desirable features, this class of model has been considered a promising alternative to the popular Cox model in the analysis of censored failure time data. However, in these AFT models, a linear predictor for the mean is typically assumed. Little research has addressed the non-linearity of predictors when modeling the mean. Deep neural networks (DNNs) have received much attention over the past few decades and have achieved remarkable success in a variety of fields. DNNs have a number of notable advantages and have been shown to be particularly useful in addressing non-linearity. Here, we propose applying a DNN to fit AFT models using Gehan-type loss combined with a sub-sampling technique. Finite sample properties of the proposed DNN and rank-based AFT model (DeepR-AFT) were investigated via an extensive simulation study. The DeepR-AFT model showed superior performance over its parametric and semiparametric counterparts when the predictor was nonlinear. For linear predictors, DeepR-AFT performed better when the dimensions of the covariates were large. The superior performance of the proposed DeepR-AFT was demonstrated using three real datasets.

Human serum albumin: prediction model and reference values for preterm and term neonates.

Human serum albumin (HSA) concentrations may alter HSA-bound drug distribution. This study aims to describe longitudinal real-world HSA trends, and to develop a prediction model for HSA concentrations using a large neonatal cohort. Patients admitted to the neonatal intensive care unit of the University Hospitals Leuven (postnatal age (PNA) ≤28days) were retrospectively included. Using linear mixed models, covariate effects on HSA were explored. A multivariable prediction model was developed (backward model selection procedure, 1% significance level). In total, 848 neonates were included [median(interquartile range) gestational age (GA) 35(32-38)weeks, birth weight (BW) 2400(1640-3130)grams]. Median HSA concentration was 32.3(28.7-35.6)g/L. Longitudinal analyses demonstrated increasing HSA concentrations with PNA and GA for most GA groups. Univariable analyses revealed significant associations of HSA with PNA, GA, BW, current weight, total and direct bilirubin, total plasma proteins, respiratory support, mechanical ventilation, sepsis, ibuprofen use, and C-reactive protein (p-values < 0.05). A high-performance (R2 = 76.3%) multivariable HSA prediction model was developed, and PNA- and GA-dependent HSA centiles were provided. Population-specific HSA centiles and an accurate neonatal HSA prediction model were developed, incorporating both maturational and non-maturational covariates. These results can enhance future clinical care and pharmacokinetic analyses to improve pharmacotherapy of HSA-bound drugs in neonates, respectively. To improve future pharmacokinetic modeling initiatives, a high-performance human serum albumin (HSA) prediction model was developed for (pre)term neonates, using a large, single-center cohort of real-world data. This prediction model integrates both maturational and non-maturational covariates, resulting in accurate HSA predictions in neonates. Additionally, HSA centiles based on postnatal and gestational age were developed, which can be easily applied in clinical practice when interpreting HSA concentrations of neonates. In general, unbound drug fractions are higher in neonates compared to older populations. To improve pharmacotherapy of HSA-bound drugs in neonates, the obtained results can be integrated in future pharmacokinetic-pharmacodynamic analyses.

TdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction

Abstract The aim of dynamic prediction is to provide individualized risk predictions over time, which are updated as new data become available. In pursuit of constructing a dynamic prediction model for a progressive eye disorder, age-related macular degeneration (AMD), we propose a time-dependent Cox survival neural network (tdCoxSNN) to predict its progression using longitudinal fundus images. tdCoxSNN builds upon the time-dependent Cox model by utilizing a neural network to capture the nonlinear effect of time-dependent covariates on the survival outcome. Moreover, by concurrently integrating a convolutional neural network with the survival network, tdCoxSNN can directly take longitudinal images as input. We evaluate and compare our proposed method with joint modelling and landmarking approaches through extensive simulations. We applied the proposed approach to two real datasets. One is a large AMD study, the Age-Related Eye Disease Study, in which more than 50,000 fundus images were captured over a period of 12 years for more than 4,000 participants. Another is a public dataset of the primary biliary cirrhosis disease, where multiple laboratory tests were longitudinally collected to predict the time-to-liver transplant. Our approach demonstrates commendable predictive performance in both simulation studies and the analysis of the two real datasets.

Immunogenicity dynamics and covariate effects after satralizumab administration predicted with a hidden Markov model.

Immunogenicity is the propensity of a therapeutic protein to generate an immune response to itself. While reporting of antidrug antibodies (ADAs) is increasing, model-based analysis of such data is seldom performed. Model-based characterization of factors affecting the emergence and dissipation of ADAs may inform drug development and/or improve understanding in clinical practice. This analysis aimed to predict ADA dynamics, including the potential influence of individual covariates, following subcutaneous satralizumab administration. Satralizumab is a humanized IgG2 monoclonal recycling IL-6 receptor antagonist antibody approved for treating neuromyelitis optica spectrum disorder (NMOSD). Longitudinal pharmacokinetic (PK) and ADA data from 154 NMOSD patients in two pivotal Phase 3 studies (NCT02028884, NCT02073279) and PK data from one Phase 1 study (SA-001JP) in 72 healthy volunteers were available for this analysis. An existing population PK model was adapted to derive steady-state concentration without ADA for each patient. A mixed hidden Markov model (mHMM) was developed whereby three different states were identified: one absorbing Markov state for non-ADA developer, and two dynamic and inter-connected Markov states-transient ADA negative and positive. Satralizumab exposure and body mass index impacted transition probabilities and, therefore, the likelihood of developing ADAs. In conclusion, the mHMM model was able to describe the time course of ADA development and identify patterns of ADA development in NMOSD patients following treatment with satralizumab, which may allow for the formulation of strategies to reduce the emergence or limit the impact of ADA in the clinical setting.

Leveraging External Aggregated Information for the Marginal Accelerated Failure Time Model.

It is becoming increasingly common for researchers to consider leveraging information from external sources to enhance the analysis of small-scale studies. While much attention has focused on univariate survival data, correlated survival data are prevalent in epidemiological investigations. In this article, we propose a unified framework to improve the estimation of the marginal accelerated failure time model with correlated survival data by integrating additional information given in the form of covariate effects evaluated in a reduced accelerated failure time model. Such auxiliary information can be summarized by using valid estimating equations and hence can then be combined with the internal linear rank-estimating equations via the generalized method of moments. We investigate the asymptotic properties of the proposed estimator and show that it is more efficient than the conventional estimator using internal data only. When population heterogeneity exists, we revise the proposed estimation procedure and present a shrinkage estimator to protect against bias and loss of efficiency. Moreover, the proposed estimation procedure can be further refined to accommodate the non-negligible uncertainty in the auxiliary information, leading to more trustable inference conclusions. Simulation results demonstrate the finite sample performance of the proposed methods, and empirical application on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial substantiates its practical relevance.

The association between pan-immune-inflammation value and chronic obstructive pulmonary disease: data from NHANES 1999-2018.

The pan-immune-inflammation value (PIV) is an emerging biomarker quantitatively reflecting the systemic immune-inflammatory status. The predictive value of PIV has been well-established across various clinical settings. However, its role in chronic obstructive pulmonary disease (COPD) remains unclear and necessitates further investigation. Data from NHANES 1999-2018 were filtered. Logistic regression analyses were used to assess the correlation between COPD prevalence and PIV in all participants. COX regression analyses and Kaplan-Meier survival curves were used to investigate the relationship between COPD all-cause mortality and PIV in COPD patients. Restricted cubic spline (RCS) analyses and piecewise linear regression analyses were additionally employed to explore the correlation between PIV and COPD. Subgroup analyses were performed to further clarify the effects of other covariates on the associations. Sensitivity analyses were employed to assess the robustness of the results. A total of 28,485 participants aged 40years and older were recruited for this study. After fully adjusting for covariates, higher PIV levels were independently associated with increased COPD prevalence (OR = 1.67; 95% CI: 1.39-2.01) and all-cause mortality (HR = 2.04; 95% CI: 1.41-2.95). The COPD prevalence curve exhibited an inflection point at Log10-PIV of 2.24, showing no significant correlation on the left side (OR = 0.86; 95% CI: 0.45-1.64) but a positive correlation on the right side (OR = 2.00; 95% CI: 1.57-2.55). The COPD all-cause mortality curve displayed an inflection point at Log10-PIV of 2.38, indicating a negative correlation on the left side (HR = 0.23; 95% CI: 0.12-0.44) and a positive correlation on the right side (HR = 4.12; 95% CI: 2.62-6.48). Subgroup analyses with interaction tests showed that the strength of the correlation between PIV and COPD prevalence was influenced by race, smoking status, and BMI (all p for interaction <0.05). The relationship between PIV and COPD all-cause mortality was unaffected by any covariates (all p for interaction >0.05). Elevated PIV levels are associated with increased COPD prevalence. COPD patients with either elevated or reduced PIV levels experience higher all-cause mortality. Further large-scale, longitudinal studies are required to corroborate these findings.

Association of specific PM2.5 chemical constituents and ozone exposure with pregnancy outcomes in women undergoing assisted reproductive technology treatment in central China

The associations of air pollutants exposure with assisted reproductive technology (ART) pregnancy outcomes are mixed, and the effects of specific components of fine particulate matter (PM2.5) and ozone (O3) are not well understood. We conducted a retrospective longitudinal study to explore the association of PM2.5 constituents and O3 exposure with three ART outcomes among women undergoing ART treatment. The exposure window was segmented into five periods corresponding to the cycle of ovarian stimulation and oocyte retrieval procedure. Generalized linear mixed model (GLMM) was applied to explore the relationships between PM2.5 constituents, O3, Normalized Vegetation Index (NDVI) exposure and three ART outcomes. The combined effect of PM2.5 constituents was evaluated by the quantile g (qg)-computation. We also explored the modifying effect of different covariate. Elevated exposure level of PM2.5 (OR = 0.915, 95% CI: 0.859, 0.974) and its constituents (BC: 0.905, 95% CI: 0.840, 0.975; OM: 0.910, 95% CI: 0.848, 0.976; NO3−: 0.909, 95% CI: 0.850, 0.972, SO42−: 0.905, 95% CI: 0.846, 0.968, and NH4+: 0.902, 95% CI: 0.842, 0.966) exposure throughout the year before oocyte retrieval (period 1) was correlated with a reduced odds ratio (OR) of live birth with statistical significance. Similarly, for each interquartile range (IQR) increase in O3 exposure during periods 2 (85 days prior to oocyte retrieval), 3 (30 days prior to oocyte retrieval), 4 (oocyte retrieval to embryo transfer) and 5 (embryo transfer to hCG test) was significantly related to a decreased OR of live birth. Especially, participants who underwent fresh embryo transfer cycles and received two cleavage-stage embryo transfer, and were younger than 30 years old, showed a higher susceptibility to particulate matter. Findings from this study suggest that PM2.5 constituents and O3 exposure may have adverse effects on the ART outcomes, highlighting the importance of identifying critical exposure periods for various air pollutants and the need for meticulous management of particulate matter.

Population Pharmacokinetics Modeling and Simulation of Deutenzalutamide, A Novel Androgen Receptor Antagonist, in Patients With Metastatic Castration-Resistant Prostate Cancer.

Deutenzalutamide is a new molecular entity androgen receptor antagonist. The primary aim of this study was to develop a population pharmacokinetic model of deutenzalutamide and evaluate effects of intrinsic and extrinsic factors on pharmacokinetics. A nonlinear mixed-effects modeling approach was performed to develop the population pharmacokinetic of deutenzalutamide using data from 1 Phase I trial of deutenzalutamide. Goodness-of-fit plots, prediction-corrected visual predictive check, and bootstrap analysis were carried out to evaluate the final model. Simulation for the developed model was used to evaluate the covariate effects on the pharmacokinetics of deutenzalutamide. A 2-compartment model with first-order absorption and elimination from the central compartment was established for deutenzalutamide. The final covariate included body weight on peripheral compartment volume. This is the first research developing the population pharmacokinetic model of deutenzalutamide in patients with metastatic castration-resistant prostate cancer, and it is expected to support the future clinical administration of deutenzalutamide.

Content adaptive JND profile by leveraging HVS inspired channel modeling and perception oriented energy allocation optimization

The existing just noticeable difference (JND) models consider the effects of various covariates, however, they rarely account for the fusion relationship between the covariates, i.e., they lack a holistic understanding of the mechanisms of visual perception and disregarding the significant impact of energy consumption on visual perception. In fact, visual perception is no exception to the rule that nerve activities and energy supply are inextricably linked. Based on this insight, this paper proposes a novel JND estimation model employing content-adaptive energy allocation. Primarily, the information theory is applied to the visual perception system by conceptualizing human visual system (HVS) as an information communication framework. Then, leveraging the relationship between energy consumption and information perception, this paper quantitatively measures the HVS energy consumption as uniform metric to describe the complicated and heterogeneous HVS perception process, and then construct JND model by fusing low-level and Semantic-level features. Numerous simulation results verify that the proposed JND model is significantly competitive with other frontier models and highly compatible with HVS.