Covariate Values Research Articles

Qualitative and quantitative damage assessment of composite pressure vessels on the basis of acoustic emission parameters

AbstractHydrogen storage composite pressure vessels may fail under sudden internal pressure increases or external structural impacts, potentially leading to explosions and other serious consequences. Acoustic emission (AE) detection technology offers notable advantages for identifying damage and evaluating the performance of composite pressure vessels. This paper conducts a hydraulic burst test on the Type IV glass fiber‐wound composite vessel to study the damage evolution behavior. A method that integrates qualitative analysis utilizing the Gaussian mixture model (GMM) AE monitoring with quantitative assessment grounded in AE rate process theory is proposed. The damage evolution period of the vessel was divided into three stages based on the AE characteristic parameters of rise time/maximum amplitude values, average frequency values, counts, and energy fluctuations. The GMM algorithm facilitated the classification of damage modes into two categories: brittle fracture and progressive degradation. The findings indicated that the GMM algorithm effectively differentiates between these two damage modes and captures the dynamic evolution of material damage. Rate process theory was employed to derive a fourth‐order polynomial functional relationship between the cumulative covariate values of the AE and the relative loads on the vessel. Finally, a linear relationship was established between the damage index D and the cumulative parameter values, demonstrating that the damage index D can effectively assess the incremental damage to the vessel. By combining qualitative and quantitative analyses, the method can accurately determine the damage degree of the vessel structure, thus providing an essential reference for damage monitoring and safety assessment of pressure vessels.Highlights Hydraulic burst test of composite vessel based on AE monitoring. Damage evolution is studied using qualitative and quantitative analyses. The GMM algorithm is used to cluster vessel damage modes. A damage analysis model is introduced to assess the incremental damage.

Imputation and Missing Indicators for Handling Missing Longitudinal Data: Data Simulation Analysis Based on Electronic Health Record Data.

Missing data in electronic health records are highly prevalent and result in analytical concerns such as heterogeneous sources of bias and loss of statistical power. One simple analytic method for addressing missing or unknown covariate values is to treat missingness for a particular variable as a category onto itself, which we refer to as the missing indicator method. For cross-sectional analyses, recent work suggested that there was minimal benefit to the missing indicator method; however, it is unclear how this approach performs in the setting of longitudinal data, in which correlation among clustered repeated measures may be leveraged for potentially improved model performance. This study aims to conduct a simulation study to evaluate whether the missing indicator method improved model performance and imputation accuracy for longitudinal data mimicking an application of developing a clinical prediction model for falls in older adults based on electronic health record data. We simulated a longitudinal binary outcome using mixed effects logistic regression that emulated a falls assessment at annual follow-up visits. Using multivariate imputation by chained equations, we simulated time-invariant predictors such as sex and medical history, as well as dynamic predictors such as physical function, BMI, and medication use. We induced missing data in predictors under scenarios that had both random (missing at random) and dependent missingness (missing not at random). We evaluated aggregate performance using the area under the receiver operating characteristic curve (AUROC) for models with and with no missing indicators as predictors, as well as complete case analysis, across simulation replicates. We evaluated imputation quality using normalized root-mean-square error for continuous variables and percent falsely classified for categorical variables. Independent of the mechanism used to simulate missing data (missing at random or missing not at random), overall model performance via AUROC was similar regardless of whether missing indicators were included in the model. The root-mean-square error and percent falsely classified measures were similar for models including missing indicators versus those with no missing indicators. Model performance and imputation quality were similar regardless of whether the outcome was related to missingness. Imputation with or with no missing indicators had similar mean values of AUROC compared with complete case analysis, although complete case analysis had the largest range of values. The results of this study suggest that the inclusion of missing indicators in longitudinal data modeling neither improves nor worsens overall performance or imputation accuracy. Future research is needed to address whether the inclusion of missing indicators is useful in prediction modeling with longitudinal data in different settings, such as high dimensional data analysis.

Sampling from covariate distribution may not always be necessary in PK/PD simulations: illustrative examples with antibiotics.

Pharmacokinetics (PK)/pharmacodynamics (PD) modeling and simulation is crucial for optimizing antimicrobial dosing. This study assessed covariate impact on PK variability and identified scenarios where fixing the covariate with median value proves effective PK/PD simulations for antibiotics with population PK (popPK) model including only one covariate effect. Three published popPK models were employed, with creatinine clearance (CRCL) identified as a covariate on clearance (CL) for meropenem and tobramycin, and total body weight (WT) associated with the volume of distributions for daptomycin. Given a fixed dose for Meropenem (1000 mg), and a WT based dose for tobramycin (7mg/kg) and daptomycin (6mg/kg), PK/PD simulation outcomes (e.g., percentage of PK/PD target attainment (PTA) and toxicity risk) were compared between fixed covariate-based and covariate distribution-based approaches. Covariate impact on PK was assessed through deterministic simulation using outer bounds of covariate versus simulation using median covariate value, with an overlap ratio calculated the percentage of overlapped area under concentration-time curve (AUC) between these two simulation approaches. Meropenem and tobramycin simulations showed a broader PK profiles and distinct PTA distribution with sampled covariate distribution, while daptomycin simulations exhibited consistency in PK/PD characteristics. CRCL had a relative strong impact on meropenem and tobramycin PK, while a weak impact of WT on daptomycin PK was observed from extensive overlap in simulated PK curves, with an overlap ratio of 99.5%. Regarding a weak covariate impact on PK with high overlap ratio, sampling from covariate distribution may not significantly enhance simulation performance, fixing covariate with median value could be efficient for antibiotic PK/PD simulations.

The analyses of structural covariance and structural covariance similarity of cortical morphological measures

The analyses of structural covariance and structural covariance similarity of cortical morphological measures

An interactive visualization dashboard for predicting the effect of sacubitril/valsartan initiation in patients with heart failure.

An interactive visualization dashboard for predicting the effect of sacubitril/valsartan initiation in patients with heart failure.

Using Subject Level Covariate Information in Bayesian Mixture Models for Basket Trials.

Basket trials are gaining importance with advancements in precision medicine. A basket trial evaluates one or more treatments for efficacy among more than one cancer type (histology) in a single clinical trial. Compared to traditional designs, basket trials can reduce the time required for testing and, by pooling across cancer types, they also allow the drugs to be tested for rare cancers. However, the potential for heterogeneity in treatment efficacy in different cancer types poses modeling challenges. Our model aims to assist the cancer type level go/no-go decisions in the initial phases of the trial through a latent cluster structure that incorporates subject-level covariate information. We model subjects' responses using a Bayesian mixture model where the mixture weights depend on a measure of similarly among subjects' covariate values. A simulation study demonstrates that our proposed Bayesian Partition Model with Covariates (BPMx) robustly estimates basket-level mean response and can provide insight about the latent cluster structure. We further illustrate the model using response data from a published basket trial.

Real-world survival comparison of second-line treatment strategies for patients with RAS/RAF wild-type, right-sided metastatic colorectal cancer.

73 Background: For patients with RAS/RAF wild type metastatic colorectal cancer (mCRC), systemic therapy usually includes fluoropyrimidine-based chemo + anti-VEGF or anti-EGFR targeted therapy. The PARADIGM trial established tumor sidedness as an important predictive biomarker for patients with RAS/RAF wild-type disease; in right-sided tumors, there was no survival difference between first-line chemo + anti-EGFR vs. chemo + anti-VEGF. However, there is no comparative data to guide second-line treatment decisions in this population. We aim to compare the effectiveness of second-line chemo + anti-EGFR versus chemo + anti-VEGF therapy for patients with RAS/RAF wild-type, right-sided mCRC who received first-line chemo + anti-VEGF. Methods: We used the nationwide Flatiron Health electronic health record-derived database, comprising de-identified patient-level structured and unstructured data obtained from ~280 cancer clinics and curated via technology-enabled abstraction. Patients ≥18 years old with RAS/RAF wild-type, right-sided mCRC who received first-line therapy consisting of chemotherapy ((FOLFIRI or FOLFOX or CAPEOX) + anti-VEGF and initiated second-line chemotherapy with either anti-EGFR or anti-VEGF targeted therapy between January 2013-May 2024 were included. Multiple imputation with chained equations imputed missing values for sidedness, RAS/RAF status, and propensity score covariates (age, gender, year of diagnosis, synchronous/metachronous disease, MMR/MSI status, ECOG score, CEA level, and first-line therapy duration). Cox proportional hazards modeling with stabilized inverse probability of treatment weighting (IPTW) assessed the association of anti-EGFR vs anti-VEGF treatment with overall survival. Results: 4,444 patients received appropriate first-line treatment and received chemo + anti-EGFR or chemo + anti-VEGF in the second line. Across 25 imputations, an average of 444 patients met inclusion criteria: 175 patients received chemo + anti-EGFR and 269 patients received chemo + anti-VEGF. Following IPTW, baseline characteristics were balanced between treatment groups. Patients who received chemotherapy + anti-EGFR had a 24% increased hazard of death when compared with patients who received chemotherapy + anti-VEGF, though this was not statistically significant (HR 1.24, 95% CI 0.96 – 1.61, p=0.097). Conclusions: Among patients with RAS/RAF wild-type, right-sided mCRC who received first-line chemotherapy + anti-VEGF, there is some evidence to support continuing anti-VEGF therapy vs. switching to anti-EGFR therapy in the second line, though the result was not statistically significant. Future studies should explore predictive biomarkers for EGFR vs VEGF-directed treatment for this population to determine the patients most likely to benefit from anti-EGFR therapy during their disease course.

Auto-Fusing Covariance and Phase Locking Value With Brain-Inspired Spiking Neural Networks for EEG-Based Driver Reaction Time Prediction

Auto-Fusing Covariance and Phase Locking Value With Brain-Inspired Spiking Neural Networks for EEG-Based Driver Reaction Time Prediction

Association Between Triglyceride/High-Density Lipoprotein Ratio and Incidence Risk of Heart Failure: A Population-Based Cohort Study.

Background/Objectives: The triglyceride/high-density lipoprotein (TG/HDL) ratio serves as a simple marker for insulin resistance. We investigated whether the TG/HDL ratio would be associated with the incidence risk of heart failure (HF). Methods: The study utilized data from the National Health Insurance Service-Health Screening Cohort database of South Korea from 2002 to 2019. The TG/HDL ratio was utilized as a time-dependent covariate or average value of at least three times throughout the follow-up period. The outcome of interest was incident heart failure (HF) corresponding with the International Classification of Disease, Tenth Revision code of I50. Results: A total of 293,968 individuals were included in this study. During the median 9.6 years (interquartile range 9.2-10.13), 27,852 individuals (9.47%) had a cumulative incidence of HF. Considering the multivariable time-dependent Cox proportional hazard model with the repeated measures of the TG/HDL ratio, per unit increase in the TG/HDL ratio significantly increased the risk of HF in the entire cohort (hazard ratio (HR): 1.007, 95% confidence interval (CI): 1.002-1.011), diabetes mellitus (DM) cohort (HR: 1.006. 95% CI: 1.002-1.010), and non-DM cohort (HR: 1.008, 95% CI: 1.003-1.013). Regarding average TG/HDL ratio quartiles, compared to the lowest quartiles (Q1), the highest quartiles (Q4) were positively associated with the incidence risk of HF accompanied by a significant p for trend (HR: 1.114, 95% CI: 1.075-1.155) in fully adjusted multivariable analysis. Conclusions: Our study demonstrated that the repeatedly measured TG/HDL ratio was associated with the incidence risk of HF regardless of the presence of DM history in the general population.

Propensity Score Matching: should we use it in designing observational studies?

BackgroundPropensity Score Matching (PSM) stands as a widely embraced method in comparative effectiveness research. PSM crafts matched datasets, mimicking some attributes of randomized designs, from observational data. In a valid PSM design where all baseline confounders are measured and matched, the confounders would be balanced, allowing the treatment status to be considered as if it were randomly assigned. Nevertheless, recent research has unveiled a different facet of PSM, termed “the PSM paradox”. As PSM approaches exact matching by progressively pruning matched sets in order of decreasing propensity score distance, it can paradoxically lead to greater covariate imbalance, heightened model dependence, and increased bias, contrary to its intended purpose.MethodsWe used analytic formula, simulation, and literature to demonstrate that this paradox stems from the misuse of metrics for assessing chance imbalance and bias.ResultsFirstly, matched pairs typically exhibit different covariate values despite having identical propensity scores. However, this disparity represents a “chance” difference and will average to zero over a large number of matched pairs. Common distance metrics cannot capture this “chance” nature in covariate imbalance, instead reflecting increasing variability in chance imbalance as units are pruned and the sample size diminishes. Secondly, the largest estimate among numerous fitted models, because of uncertainty among researchers over the correct model, was used to determine statistical bias. This cherry-picking procedure ignores the most significant benefit of matching design-reducing model dependence based on its robustness against model misspecification bias.ConclusionsWe conclude that the PSM paradox is not a legitimate concern and should not stop researchers from using PSM designs.

211. Defining Optimal Sampling Times for Cefepime Therapeutic Drug Monitoring in Clinical Practice

Abstract Background Clinicians performing beta-lactam therapeutic drug monitoring (TDM) lack evidence on when levels should ideally be drawn after a dose. Herein, we define the optimal timing (i.e., optimal sampling) for cefepime using real-world TDM data to validate our approach.Figure 1.External validation of the Bayesian prior non-parametric population PK model Methods De-identified data from two centers performing routine cefepime TDM were extracted by InsightRX and served as an external validation cohort. Plasma cefepime was quantified using validated LC-MS/MS assays for TDM and dosing was protocolized at each site. CRRT and ECMO patients were included but other dialysis patients were not. Bias (MPE) and precision (RMSE) of a non-parametric prior were assessed. Multiple-model optimal (MM-opt) sampling strategies were estimated for the first 24 hours of treatment. To mirror clinical practice, one- and two-sample designs were evaluated. Dose and covariate values informed optimal sampling times. Bayesian PK exposures were compared using all samples, trough-only sampling, or using a single optimally timed sample. AUCs were calculated from the posteriors. For fT >MIC analysis, the MIC was fixed at 8 mg/L. We used Pmetrics 2.1.1 for R.Figure 2.Distribution of MM-optimal sampling times in external validation data using n=1 sample Results 116 patients (42% female; median age, CRCL, and weight: 62 years, 76 mL/min, and 80 kg, respectively) contributed 235 levels. The PK model demonstrated acceptable bias and precision (-6% MPE, 30.9 RMSE) as a prior for estimating exposures from the TDM data (Fig1). For a one-sample approach, the most common MM-opt sampling times varied (Fig2) but were often a mid-point or trough. In the two-sample approach, sample one was often a mid-point and sample two was often a trough (Fig3). First 24-hr AUC and fT>MIC did not significantly differ using all available samples for analysis vs. limiting sampling to a single optimized time point vs. limiting sampling to a trough-only approach (P >0.05 for all comparisons; Fig4).Figure 3.Distribution of MM-optimal sampling times in external validation data using n=2 samples Conclusion Optimal cefepime sampling times depended on dosing regimen, and renal disposition. When limited to a single sample, optimal sampling times for cefepime TDM were often midpoint/trough levels, but when two samples were obtained the optimal sampling times were often a mid-point followed by a trough. Estimation of PK and PK/PD exposures was not significantly worse when using a validated Bayesian prior and a trough-only sampling approach.Figure 4.First 24 hours PK and PK/PD exposure estimates from Bayesian analysis of all external validation data and Bayesian analysis of n=1 optimally timed samples from the same population Disclosures Nathaniel J. Rhodes, PharmD MS, Apothecademy, LLC: Advisor/Consultant Brandon Smith, MD, PharmD, Melinta Therapeutics: Advisor/Consultant|Shionogi, INC: Advisor/Consultant Ryan K. Shields, PharmD, MS, Allergan: Advisor/Consultant|Cidara: Advisor/Consultant|Entasis: Advisor/Consultant|GSK: Advisor/Consultant|Melinta: Advisor/Consultant|Melinta: Grant/Research Support|Menarini: Advisor/Consultant|Merck: Advisor/Consultant|Merck: Grant/Research Support|Pfizer: Advisor/Consultant|Roche: Grant/Research Support|Shionogi: Advisor/Consultant|Shionogi: Grant/Research Support|Utility: Advisor/Consultant|Venatorx: Advisor/Consultant|Venatorx: Grant/Research Support Jasmine Hughes, PhD, InsightRX: Employee|InsightRX: Stocks/Bonds (Private Company) Maria-Stephanie Hughes, PharmD, InsightRX: Employee of company|InsightRX: Stocks/Bonds (Private Company) Fekade B. Sime, PhD, S.Aust., Gilead: Grant/Research Support|Pfizer: Grant/Research Support Patrick J. Kiel, PharmD, Amgen: Employee|Amgen: Stocks/Bonds (Public Company) Marc H. Scheetz, PharmD, MSc, Abbvie: Advisor/Consultant|Basilea: Advisor/Consultant|Cidara: Advisor/Consultant|DoseMe: Advisor/Consultant|Entasis: Advisor/Consultant|F2G: Advisor/Consultant|GSK: Advisor/Consultant|Lykos: Advisor/Consultant|Roche: Advisor/Consultant|Third Pole Therapeutics: Advisor/Consultant|Xelia: Advisor/Consultant

Effects of surgical approach and downstaging in esophageal adenocarcinoma patients treated with neoadjuvant chemotherapy: a 2010–2020 National Cancer Database (NCDB) study

BackgroundNeoadjuvant Chemoradiation (nCRT) has been shown to improve survival in patients with Esophageal Adenocarcinoma (EAC). The objective of this study is to assess the patient characteristics associated with tumor downstaging in a large national database. Additionally, we evaluated surgical approach and change in clinical versus pathological staging as predictors of patient survival.MethodsUsing the 2010–2020 National Cancer Database, we identified 6,400 patients with clinical stage 1B to 4A EAC who received nCRT and underwent esophagectomy. Multivariable logistic models were estimated to evaluate odds of downstaging, and complete downstaging. Multivariable marginal Cox proportional-hazard models were estimated to evaluate all-cause mortality hazard.Results3285 (51%) patients downstaged (of which 292 [5% of total] completely downstaged), 2430 (38%) had no change in stage, and 685 (11%) progressed. Generally, higher covariate values such as Clinical T, Clinical N, age, and Charlson-Deyo score were associated with higher odds of downstaging and lower odds of complete downstaging. Downstaging was associated with 31% lower risk of death compared to progression (p < .001) and 17% lower risk of death compared to no change (p < .001). Regarding surgical approach, when compared with open esophagectomy (OE), robotic-assisted minimally invasive esophagectomy (RAMIE) was associated with 17% lower adjusted risk of death (p = .002) while minimally invasive esophagectomy (MIE) was associated with a 10% decrease in adjusted risk of death (p = .009).ConclusionIn patients with EAC who underwent nCRT, pathological downstaging was associated with increased survival compared to no change or progression. Additionally, RAMIE and MIE were associated with lower risk of death compared to OE.Graphical

Design of the EWMA charts for simultaneously monitoring mean and variance using the conditional false alarm rate

The adoption of dynamic probability control limits (DPCLs) offers significant advantages in the development of control charts, particularly in scenarios characterized by temporal fluctuations in sample sizes, areas of opportunity, or other covariate values. In this study, we propose the DPCLs for the joint EWMA (JEWMA) chart to detect changes in the mean and/or variance of a normally distributed process. Monte Carlo simulations are used to estimate the conditional false alarm rate, zero-state and steady-state run-length properties of the JEWMA chart. Unlike existing JEWMA chart based on fixed control limits, the DPCLs-based JEWMA chart can accommodate time-varying sample sizes while ensuring consistent conditional false alarm rate maintenance over time.

Optimization of Covariance Matrices of Kalman Filter with Unknown Input Using Modified Directional Bat Algorithm

The proper selection of the model error covariance matrix and the measurement noise covariance matrix of Kalman filter is an optimization problem. Some scholars have studied this, but there is relatively little research on the selection of the two covariance matrices for Kalman filters with an unknown input. Recently, the authors proposed a modified directed bat algorithm (MDBA) which introduces the best historical location of individuals and the elimination strategy to effectively prevent falling into local optimal solution. So, two methods are proposed in this paper to optimize the model error covariance matrix and measurement noise covariance matrix of Kalman filter with unknown inputs (KF-UI) and extended Kalman filter with unknown inputs (EKF-UI) by MDBA, respectively. The objective functions are constructed using the measurement vectors and the corresponding estimated values, and MDBA is adopted to optimize the two covariance matrices of KF-UI and EKF-UI. To validate the effectiveness of proposed methods, two simple structure examples and a benchmark example are adopted. The influence of structural parameter uncertainties on KF-UI is also considered. The result shows that the MDBA-optimized KF-UI has a strong convergence and can take into account the effect of parameter uncertainties. Then, the effectiveness of the proposed MDBA-optimized EKF-UI method is validated by comparing it with EKF-UI with empirically selected covariance values through trial-and-error. The identification results showed that the proposed methods achieved better identification accuracy and enhanced convergence compared to KF-UI and EKF-UI with empirical covariance values.

Heavy tail robust estimation and inference for average treatment effects

. We study the probability tail properties of Inverse probability weighting (IPW) estimators of the average treatment effect (ATE) when there is limited overlap between the covariate distributions of the treatment and control groups. Under unconfoundedness of treatment assignment conditional on covariates, such limited overlap is manifested in the propensity score for certain units being very close (but not equal) to 0 or 1. This renders IPW estimators possibly heavy tailed, and with a slower than n rate of convergence. Historically estimators are either based on the assumption of strict overlap, i.e., the propensity score is bounded away from 0 and 1; or they truncate the propensity score; or trim observations based on a variety of techniques based on covariate or propensity score values. Trimming or truncation is ultimately based on the covariates, ignoring important information about the inverse probability weighted random variable Z that identifies ATE by E[Z] = ATE. We propose a tail-trimmed IPW estimator whose performance is robust to limited overlap. In terms of the propensity score, which is generally unknown, we plug-in its parametric estimator in the infeasible Z, and then negligibly trim the resulting feasible Z adaptively by its large values. Trimming leads to bias if Z has an asymmetric distribution and an infinite variance, hence we estimate and remove the bias using important improvements on existing theory and methods. Our estimator sidesteps dimensionality, bias and poor correspondence properties associated with trimming by the covariates or propensity score. Monte Carlo experiments demonstrate that trimming by the covariates or the propensity score requires the removal of a substantial portion of the sample to render a low bias and close to normal estimator, while our estimator has low bias and mean-squared error, and is close to normal, based on the removal of very few sample extremes.

Bayesian Nonparametric Model for Heterogeneous Treatment Effects With Zero-Inflated Data.

One goal of precision medicine is to develop effective treatments for patients by tailoring to their individual demographic, clinical, and/or genetic characteristics. To achieve this goal, statistical models must be developed that can identify and evaluate potentially heterogeneous treatment effects in a robust manner. The oft-cited existing methods for assessing treatment effect heterogeneity are based upon parametric models with interactions or conditioning on covariate values, the performance of which is sensitive to the omission of important covariates and/or the choice of their values. We propose a new Bayesian nonparametric (BNP) method for estimating heterogeneous causal effects in studies with zero-inflated outcome data, which arise commonly in health-related studies. We employ the enriched Dirichlet process (EDP) mixture in our BNP approach, establishing a connection between an outcome DP mixture and a covariate DP mixture. This enables us to estimate posterior distributions concurrently, facilitating flexible inference regarding individual causal effects. We show in a set of simulation studies that the proposed method outperforms two other BNP methods in terms of bias and mean squared error (MSE) of the conditional average treatment effect estimates. In particular, the proposed model has the advantage of appropriately reflecting uncertainty in regions where the overlap condition is violated compared to other competing models. We apply the proposed method to a study of the relationship between heart radiation dose parameters and the blood level of high-sensitivity cardiac troponin T (hs-cTnT) to examine if the effect of a high mean heart radiation dose on hs-cTnT varies by baseline characteristics.

Bayesian semiparametric inference in longitudinal metabolomics data

The article is motivated by an application to the EarlyBird cohort study aiming to explore how anthropometrics and clinical and metabolic processes are associated with obesity and glucose control during childhood. There is interest in inferring the relationship between dynamically changing and high-dimensional metabolites and a longitudinal response. Important aspects of the analysis include the selection of the important set of metabolites and the accommodation of missing data in both response and covariate values. With this motivation, we propose a flexible but parsimonious Bayesian semiparametric joint model for the outcome and the covariate generating processes, making novel use of nonparametric mean processes, latent factor models, and different classes of continuous shrinkage priors. The proposed approach efficiently addresses daunting dimensionality challenges, simplifies imputation tasks, and automates the selection of important predictors. Implementation via an efficient Markov chain Monte Carlo algorithm appropriately accounts for uncertainty in various aspects of the analysis. Simulation experiments illustrate the efficacy of the proposed methodology. The application to the EarlyBird cohort study illustrates its practical utility in enabling statistical integration of different molecular processes involved in glucose production and metabolism. From this study, we were able to show that glucose levels from 5 to 16 years of age are associated with different circulating levels of metabolites in the blood serum and can be fitted over time for a wide range of shapes of trajectories. The metabolites contributing the most to explaining glucose trajectories tend to be involved in different central energy metabolomic pathways. The methodology provides a tool to generate new hypotheses related to obesity and glucose control during childhood and adolescence.

Extrapolation before imputation reduces bias when imputing censored covariates

Modeling symptom progression to identify ideal subjects for a Huntington’s disease clinical trial is problematic since time to diagnosis, a key covariate, can be heavily censored. Imputation is an appealing strategy that replaces the censored covariate with its conditional mean, but existing methods saw over 200% bias under heavy censoring. Calculating conditional means well requires estimating and then integrating over the survival function of the censored covariate from the censored value to infinity. To estimate the survival function flexibly, existing methods use the semiparametric Cox model with Breslow’s estimator, leaving the integrand for the conditional means (the survival function) undefined beyond the observed data. The integral is then estimated up to the largest observed covariate value, and this approximation can cut off the tail of the survival function and lead to severe bias. We combine the semiparametric survival estimator with a parametric extension to approximate the integral up to infinity. In simulations, our proposed extrapolation-before-imputation approach substantially reduces the bias seen with existing imputation methods, sometimes even when the parametric extension was misspecified. We further demonstrate how imputing with corrected conditional means can prioritize subjects for clinical trials. The R code to reproduce results is available in the Supplementary Material.

Relative fat mass is a valuable predictor of lower urinary tract symptoms suggestive of benign prostatic hyperplasia in aging males: clinical implications.

The global prevalence of lower urinary tract symptoms suggestive of benign prostate hyperplasia (LUTS/BPH) escalates, with obesity recognized as a major contributing factor. However, the association between the relative fat mass (RFM) and LUTS/BPH remains unexplored. This 7-year follow-up study aimed to investigate the cross-sectional and longitudinal relationships between RFM and LUTS/BPH. Based on the China Health and Retirement Longitudinal Study (CHARLS) data, we interpolated missing values of covariates using multivariate imputation via chained equations grounded on the random forest method. Stratifying participants by tertiles of RFM, we employed multivariate binary logistic and restricted cubic spline (RCS) regressions to assess the odds ratio (OR) and dose-response relationship between RFM and LUTS/BPH. Subgroup and interactive analyses assessed covariate impacts. Sensitivity analysis involved stratifying RFM by median and quartiles and excluding males aged ≤60 years to confirm model robustness. A retrospective cohort [2011-2018] was used to investigate longitudinal associations, with additional cohorts [2011-2013, 2011-2015] for further sensitivity checks. A total of 6,253 males aged over 40 years were included in the 2011 baseline survey, with 4,321 observed in the 2018 follow-up. The final model revealed a positive correlation between high RFM and prevalent LUTS/BPH. Specifically, the risk for the third tertile group increased by a factor of 1.47 [95% confidence interval (CI): 1.16-1.87; P=0.001]. No significant interactive effects were observed. Linear trends were confirmed by RCS regression across demographics (P for overall <0.05). In the 2011-2018 cohort, the final model linked high RFM to a 1.41 times higher incidence of LUTS/BPH (95% CI: 1.11-1.80; P=0.003) in the third tertile subgroup. All sensitivity analyses consistently affirmed these positive associations. Positive cross-sectional and longitudinal associations between RFM and LUTS/BPH were observed, underscoring RFM's potential as a valuable predictor for prompt detection targeting LUTS/BPH in aging males. Consequently, early management and treatment strategies could be implemented.

Improved Malaria Therapy with Cationic Nanocapsules Demonstrated in Plasmodium berghei-Infected Rodents Using Whole Blood Surrogate Population PK/PD Modeling.

Objectives: Investigating how nanoparticle systems interact in whole blood (WB) is critical to evaluating the effectiveness of malaria therapy. Methods: We decided to establish a pharmacokinetic/pharmacodynamic (PK/PD) model of the quinine population in WB using Plasmodium berghei-infected mice, with a subsequent model comparison for nanocapsules coated with polysorbate (NCP80) or prepared with Eudragit® RS (NCEUD). The WB quinine population pharmacokinetic model in rats was developed using plasma and partition coefficients for rat erythrocytes. Mouse WB quinine population PK/PD modeling was developed using allometrically scaled literature-free mouse quinine pharmacokinetic data and covariate values to obtain a WB population pharmacokinetic model for quinine and nanocapsules in mice. This allowed for PK/PD modeling of the quinine population with the WB concentration and parasitemia data in mice. All models were built in NONMEN. Results: The WB quinine concentration profiles in rats were characterized using a two-compartment model. Nanoencapsulation reduced clearance and central compartment volume and increased peripherical compartimental volume. A maximum effect model described the PK/PD of the quinine WB population in mice, demonstrating that NCEUD enhances the antimalarial effect. Conclusions: Quinine WB is a good surrogate for describing the response to exposure in malaria. NCEUD outperformed NCP80 and free quinine, suggesting that cationic surfaces improve the potential for treating malaria.