Influence Of Covariates Research Articles

Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors.

Functional concurrent, or varying-coefficient, regression models are a form of functional data analysis methods in which functional covariates and outcomes are collected concurrently. Two active areas of research for this class of models are identifying influential functional covariates and clustering their relations across observations. In various applications, researchers have applied and developed methods to address these objectives separately. However, no approach currently performs both tasks simultaneously. In this paper, we propose a fully Bayesian functional concurrent regression mixture model that simultaneously performs functional variable selection and clustering for subject-specific trajectories. Our approach introduces a novel spiked Ewens-Pitman attraction prior that identifies and clusters subjects' trajectories marginally for each functional covariate while using similarities in subjects' auxiliary covariate patterns to inform clustering allocation. Using simulated data, we evaluate the clustering, variable selection, and parameter estimation performance of our approach and compare its performance with alternative spiked processes. We then apply our method to functional data collected in a novel, smartphone-based smoking cessation intervention study to investigate individual-level dynamic relations between smoking behaviors and potential risk factors.

Evaluation of Structural Equation Model Forests' Performance to Identify Omitted Influential Covariates

Model misspecification is typical in applied structural equation modeling (SEM). Traditional specification search methods, such as modification indices, search for misspecifications within the model’s variables but overlook influential variables not initially included and fail to detect interactions. This study evaluates SEM forests as a complementary method to conduct SEM specification search related to omitted influential covariates. The omitted influential paths include unique, mixed, and interaction paths. SEM forests’ performance is evaluated under different factor loading magnitudes, covariate path magnitudes, and sample sizes. Results show SEM forests accurately identify omitted influential covariates without falsely identifying non-influential covariates in large samples (1,000) with strong covariate-latent variable paths ( β = .5).

Machine Learning Enhances Soil Aggregate Stability Mapping for Effective Land Management in a Semi-Arid Region

Soil aggregate stability (SAS) is needed to evaluate the soil’s resistance to degradation and erosion, especially in semi-arid regions. Traditional laboratory methods for assessing SAS are labor-intensive and costly, limiting timely and cost-effective monitoring. Thus, we developed cost-efficient wall-to-wall spatial prediction maps for two fundamental SAS proxies [mean weight diameter (MWD) and geometric mean diameter (GMD)], across a 5000-hectare area in Southwest Iran. Machine learning algorithms coupled with environmental and soil covariates were used. Our results showed that topographic covariates were the most influential covariates in predicting these SAS proxies. Overall, our SAS maps are valuable tools for sustainable soil and natural resource management, enabling decision-making for addressing potential soil degradation and promoting sustainable land use in semi-arid regions.

Food Insecurity and Depressive Symptoms Among Persons With Diabetes in the United States: Findings From the 2022 National Health Interview Survey (NHIS).

The purpose of this study is to examine whether food insecurity increases risk for depressive symptoms among adults with diabetes. Food insecurity is associated with depression in the general population. However, minimal research has examined this relationship among persons with diabetes. Adults with diabetes were identified from the 2022 National Health Interview Survey. Depressive symptoms were assessed by the 8-item Patient Health Questionnaire. Food insecurity status was measured with the USDA 10-item food insecurity scale. Participants were categorized as food secure, low food security, and very low food security. The association between food insecurity and depressive symptoms was examined using weighted logistic regression, adjusting for potentially influential covariates (age, sex, ethnicity, poverty to income ratio, education, diabetes type, body mass index, region, and general health). Among 2595 persons with diabetes, nearly one third (29.8%) reported symptoms of depression. Over one tenth (11.7%) had low or very low food security. Persons who had low or very low food security were more than twice as likely to report depressive symptoms (adjusted odds ratio [AOR] = 2.15, 95% CI, 1.39-3.32; AOR = 3.84, 95% CI, 2.28-6.45, respectively). Persons who were older, had higher income, and better general health were less likely to report depressive symptoms. Among adults with diabetes, low or very low food security was associated with increased risk for depressive symptoms. Further research is needed to evaluate the relationship between food insecurity and depressive symptoms among persons with diabetes.

Evaluating machine learning performance in predicting sodium adsorption ratio for sustainable soil-water management in the eastern Mediterranean

Soil salinization is a critical global issue for sustainable agriculture, impacting crop yields and posing a threat to achieving the Sustainable Development Goal (SDG) of ensuring food security. It is necessary to monitor it in detail and uncover its underlying factors at a regional scale. In this context, the present study aimed to evaluate soil health in the eastern Mediterranean region by using the Sodium Adsorption Ratio (SAR) as an indicator of soil salinity in three distinct soil horizons. The main objective of the research was to evaluate the performance of four machine learning (ML) models, including Random Forest (RF), Nu Support Vector Regression (NuSVR), Artificial Neural Network-Multi Layer Perceptron (ANN-MLP), and Gradient Boosting Regression (GBR), for accurate prediction of SAR following the Recursive Feature Elimination (RFE) as a feature selection method. Moreover, SHapely Additive exPlanations (SHAP) was applied as sensitivity analysis to identify the most influential covariates. Main findings of the research revealed that the average clay content in the surface horizon (H10-25cm) was 50.5% ± 10.4, which significantly increased to 57.5% ± 8.7 (p < 0.05). No significant mean differences were detected between the studied horizons for SAR and Na+. ML output revealed that NuSVR outperformed other algorithms in accurately predicting outcomes during both the training and testing stages. Moreover, Scenario 2 (SC2) with seven selected features from the RFE method facilitated highly accurate SAR predictions. Overall, the performance of ML models is ranked as NuSVR > GBR > ANN-MLP > RF. Lastly, SHAP sensitivity analysis identified CEC, Ca+2, Mg+2, and Na+ as the most influential variables for SAR prediction in both the training and testing stages. Hence, the research yielded valuable insights for efficient agricultural soil management at a regional level using state-of-the-art technology.

Characterization of mavacamten pharmacokinetics in patients with hypertrophic cardiomyopathy to inform dose titration.

Mavacamten is a selective, allosteric, reversible cardiac myosin inhibitor that has been developed for the treatment of adults with symptomatic obstructive hypertrophic cardiomyopathy (HCM). A population pharmacokinetic (PopPK) model was developed to characterize mavacamten pharmacokinetics (PK) and the variation in mavacamten exposure associated with intrinsic and extrinsic factors. Data from 12 clinical studies (phases 1, 2, and 3) were used. Evaluable participants were those who had at least one mavacamten concentration measurement with associated sampling time and dosing information. The base model included key covariates: body weight, cytochrome P450 isozyme 2C19 (CYP2C19) phenotype with respect to PK, and formulation. The final model was generated using stepwise covariate testing and refinement processes. Simulations were performed to evaluate PK: apparent clearance (CL/F); apparent central and peripheral volumes of distribution; and steady-state average, trough, and maximum concentrations. Overall, 9244 measurable PK observations from 497 participants were included. A two-compartment model structure was selected. After stepwise covariate model building and refinement, additional covariates included were: specified mavacamten dose, omeprazole or esomeprazole administration, health/disease status, estimated glomerular filtration rate, fed status, and sex. The final PopPK model accurately characterized mavacamten concentrations. At any given dose, CYP2C19 phenotype was the most influential covariate on exposure parameters (e.g., median CL/F was reduced by 72% in CYP2C19:poor metabolizers compared with the reference participant [CYP2C19:normal metabolizer]). CL/F was also approximately 16% higher in women than in men but lower in participants receiving concomitant omeprazole or esomeprazole (by 33% and 42%, respectively) than in participants not receiving such concomitant therapy.

Nedosiran population pharmacokinetic and pharmacodynamic modelling and simulation to guide clinical development and dose selection in patients with primary hyperoxaluria type 1.

To characterize pharmacokinetic and pharmacodynamic profiles of nedosiran in patients with primary hyperoxaluria type 1 (PH1), identify influential covariates and confirm therapeutic doses. A population pharmacokinetic (PK)/pharmacodynamic (PD) (POP-PKPD) model was developed to characterize the concentration-time course of nedosiran and the corresponding effect on 24-h urinary oxalate (Uox). Simulations of dosing to achieve clinically meaningful reduction in Uox in children, adolescents and adults with PH1 were performed. Analyses included PK data from 143 healthy participants and PH1/PH2 patients, and PD data from 46 PH1 patients. Nedosiran PK was described by a two-compartment model with dual n-transit absorption and parallel linear and nonlinear elimination. The relationship between nedosiran exposure and Uox was described by an indirect response model. Body weight, estimated glomerular filtration rate (eGFR) and disease status were identified as influential covariates for the POP-PK model. The simulation results supported a weight-banded dosing regimen of nedosiran sodium in adolescents and adults (≥12 years) with PH1 of 170 mg (weight ≥50 kg) and 136 mg (weight <50 kg), in children (6-11 years) with PH1 of 3.5mg/kg, and no dose adjustments for PH1 patients with relatively preserved kidney function (eGFR ≥ 30 mL/min/1.73m2). Following the proposed dosing regimens, the simulated median fold-changes in PK AUC0-τ,ss were acceptable (≤1.51 fold-change) and ~71% of PH1 patients across all age groups achieved near-normal Uox (<0.6mmol) by week 52. The final POP-PKPD model characterizes observed nedosiran PK and Uox data. Simulations support nedosiran dosing regimens in PH1 patients aged ≥6years with relatively preserved kidney function.

Population pharmacokinetics of total and unbound valemetostat and platelet dynamics in healthy volunteers and patients with non-Hodgkin lymphoma.

Valemetostat is an EZH2/1 inhibitor that has been approved in Japan for the treatment of patients with relapsed/refractory adult T-cell leukemia/lymphoma, based mainly on results from a single-arm phase II trial. It is currently under investigation worldwide for the treatment of other non-Hodgkin lymphomas (NHLs), including peripheral T-cell lymphoma, and for solid tumors. Semi-mechanistic population pharmacokinetic modeling of total and unbound valemetostat and an analysis of the platelet time course during treatment with valemetostat were conducted using data from five clinical trials (two in patients with NHL and three in healthy volunteers). Pharmacokinetic data, including 3162 total/1871 unbound valemetostat observations from 102 patients and 72 healthy volunteers, were described by a three-compartment model with sequential zero-/first-order absorption and saturable binding in the central compartment. Alpha-1-acid glycoprotein (AAG) was the most influential covariate for total valemetostat exposure, yet had little impact on unbound exposure, meaning no dose adjustment was warranted based on AAG levels. The longitudinal platelet data from 101 patients (2313 observations) were adequately described by a modified Friberg model with two proliferation compartments, which characterized unique spontaneous recovery of platelet counts without dose modifications. A model-based simulation quantitatively assessed the proposed dose-adjustment guidance in case of platelet count decreased by comparing the probability of treatment discontinuation due to platelet count decreased with or without the dose adjustment. In summary, the models described observed total and unbound valemetostat concentrations and a unique time course of platelets during treatment, which can justify the clinical dose and provide dose-adjustment guidance.

Measurement error–tolerant Poisson regression for Valley Fever incidence prediction

The incidence of Valley Fever can be predicted using a Poisson regression model, which links the expected incidence count to influential covariates with model coefficients. In real-world practice, the estimated model coefficients are sensitive to the inevitable measurement errors in the covariates, which are usually manifested as classical errors and/or Berkson errors. While conventional methods have separately addressed each type of error, the joint consideration of both, referred to as mixture measurement error in this article, is currently absent from the existing literature, leaving model coefficient estimators with significant biases. This article presents a measurement-error tolerant Poisson regression to mitigate the impacts of the mixture measurement error in covariates of health-care data analysis. An error-structureadapted quasi-likelihood estimation method is proposed to enhance the accuracy of model estimation. The effectiveness of the proposed method is demonstrated through a numerical case study built upon a Valley Fever investigation, validating its improved estimation accuracy and robustness.

Population pharmacokinetics of flucloxacillin as intermittent bolus infusion in patients with Staphylococcus aureus bloodstream infection.

Optimal antibiotic dosing for Staphylococcus aureus bloodstream infections (BSI) is still controversial. One reason is inter-individual variation in pharmacokinetics, which may be influenced by various patient-related factors, particularly in critically ill patients. To describe the population pharmacokinetics (PopPK) of the antibiotic flucloxacillin in patients with S. aureus BSI. Subsequently, we sought to translate the model into a user-friendly app for generating a priori and a posteriori time-concentration curves and dose recommendations to optimize dosing regimens. Total and unbound flucloxacillin concentrations were included from 49 patients from a prospective cohort study conducted during clinical routine, including non-critically ill and critically ill individuals who received intermittent bolus applications. These data were analysed using non-linear mixed-effects modelling. Most patients (98%) were treated with 2 g of flucloxacillin every 4 h. We developed a joint model that simultaneously described total and unbound concentrations. The model included an allometric effect of glomerular filtration rate on clearance and albumin on the albumin dissociation constant. The latter was especially important, as in our population the unbound fraction was higher at 11.5% (16.7% for critically ill patients) compared with reported values of approximately 5%. Based on our joint model, we developed a web-based app for optimizing dosing regimens of flucloxacillin. By utilizing data from clinical routine, we were able to create a predictive PopPK model of flucloxacillin and identify influential covariates. The web-based app is currently being validated in a clinical trial.

Complexity in the timing of the first postnatal ecdysis in snakes.

Lepidosaurian reptiles, particularly snakes, periodically shed the outer epidermal layers of their skin (ecdysis) to restore or enhance vital functions such as regulating water and gaseous exchange, growth, and protection against insult, infection or physical injury. Although many studies have focused on the nature and mechanisms of skin shedding, little attention has been paid to the timing of the first ecdysis in neonates following birth or hatching. A recent study investigated patterns of the time to first postnatal ecdysis in snakes based on a large dataset taken from the literature. The analysis demonstrated patterns in the time to first postnatal ecdysis related to phylogeny as well as several life history traits. While this assessment provides important advances in our knowledge of this topic, data on known biophysical drivers of ecdysis - temperature and humidity - were largely unavailable and were not evaluated. The first postnatal ecdysis of neonatal snakes can be viewed as an adaptive adjustment to the transition from the aqueous environment of the embryo to the aerial environment of the newborn. Hence, the timing of the first postnatal ecdysis is logically influenced by the aerial environment into which a newborn snake or hatchling finds itself. Therefore, in this Commentary, we first emphasize the putative plasticity of ecdysis with respect to epidermal lipids that structure the water permeability barrier and are established or renewed during ecdysis to reduce transepidermal evaporative water loss. We then discuss the likely importance of biophysical variables as influential covariates that need future investigation as potential co-determinants of the timing of first postnatal ecdysis.

Population Pharmacokinetic Modeling and Exposure-Efficacy and Body Weight-Response Analyses for Tezepelumab in Patients With Severe, Uncontrolled Asthma.

Tezepelumab is a human monoclonal antibody that blocks the activity of thymic stromal lymphopoietin. This analysis assessed the suitability of a fixed-dose regimen of tezepelumab 210 mg every 4 weeks (Q4W) in adults and adolescents with severe, uncontrolled asthma. A population pharmacokinetic model was developed using data from 1368 patients with asthma or healthy participants enrolled in 8 clinical studies (phases 1-3). Tezepelumab exposure-efficacy relationships were analyzed in the phase 3 NAVIGATOR study (NCT03347279), using asthma exacerbation rates over 52 weeks and changes in pre-bronchodilator forced expiratory volume in 1 s at week 52. Tezepelumab pharmacokinetics were well characterized by a 2-compartment linear disposition model with first-order absorption and elimination following subcutaneous and intravenous administration at 2.1-420 and 210-700 mg, respectively. There were no clinically relevant effects on tezepelumab pharmacokinetics from age (≥12 years), sex, race/ethnicity, renal or hepatic function, disease severity (inhaled corticosteroid dose level), concomitant asthma medication use, smoking history, or anti-drug antibodies. Body weight was the most influential covariate on tezepelumab exposure, but no meaningful differences in efficacy or safety were observed across body weight quartiles in patients with asthma who received tezepelumab 210 mg subcutaneously Q4W. There was no apparent relationship between tezepelumab exposure and efficacy at this dose regimen, suggesting that it is on the plateau of the exposure-response curve of tezepelumab. In conclusion, a fixed-dose regimen of tezepelumab 210 mg subcutaneously Q4W is appropriate for eligible adults and adolescents with severe, uncontrolled asthma.

A framework for optimizing environmental covariates to support model interpretability in digital soil mapping

A common practice in digital soil mapping (DSM) is to incorporate many environmental covariates into a machine-learning algorithm to predict the spatial patterns of soil attributes. Variance inflation factor (VIF), principal component analysis (PCA), and recursive feature elimination (RFE) are three statistical methods that can be used to reduce the number of covariates. This study aims 1) to compare VIF and PCA approaches; 2) to identify an approach to determine the minimum number of covariates in DSM to ensure model parsimony using RFE after using VIF; and 3) to examine methods to interpret the impact of covariates on the variability of the predicted soil properties. The study area was the province of British Columbia (BC), Canada. This study used legacy data for four soil properties to make digital soil maps: soil organic carbon (SOC%), pH, clay%, and coarse fragment (CF%). Seven models were made for each soil property to determine the influence on validation results by using a different number of covariates produced by various methods on validation results. The results showed that the number of covariates could be reduced from 70 to 4 to 12 with only a little or no difference in concordance correlation coefficient (CCC) validation results. The CCC results of pH models using 70 and 7 covariates were both 0.74, and for other soil properties, this difference was negligible. The validation results obtained from PCA models showed that the performance of PCA in reducing the number of covariates was not as effective as when using VIF. Moreover, this study showed that covariates related to precipitation were the most important for modeling SOC%, soil pH, and clay%. Topographic covariates were the most influential covariates for modeling soil CF%. This study emphasizes the potential benefits of combining various data reduction methods to achieve optimal outcomes and generate the most parsimonious and interpretable models.

Estimating soil N2O emissions induced by organic and inorganic fertilizer inputs using a Tier-2, regression-based meta-analytic approach for U.S. agricultural lands

Consistent methods are essential for generating country and region-specific estimates of greenhouse gas (GHG) emissions used for reporting and policymaking. The estimates of direct N2O emissions from U.S. agricultural soils have primarily relied on the use of emission factors (EFs, Tier-1) and process-based models (Tier-3). However, Tier-1 estimates are relatively crude while Tier-3 calculations can be costly. This work addressed this gap by developing a Tier-2, regression-based approach by leveraging a meta-database containing 1883 field N2O observations together with environmental and management covariates from 139 studies. Our results estimated higher monthly soil N2O emissions (N2Om, kg N/ha) during the growing season (0.38) than the fallow period (0.15), highlighting the importance of considering measurement periods when utilizing meta-databases for analyzing N2O drivers. Significantly different N2Om were found for tillage practices (conventional > no-till: 0.42 > 0.27), fertilizer type (liquid > solid manure: 0.55 > 0.32), and soil texture (fine > coarse: 0.36 > 0.22). The comparisons of the influence of crop type and rotation, water management, and soil order on N2O emissions are complicated by regional data availability and interactions among different factors. Additionally, the finding that N2O emissions reported based on area (N2Om), N input rate (EF), or yield can alter treatment rankings underscores the need to establish transparent criteria for rewarding or discouraging regionally-based management practices using N2O metrics. Finally, we show how General Linear Models (GLMs) can be used to estimate country and regional Tier-2 N2Om using a suite of covariates. Our GLMs identified tillage, water management, N input type and rate, soil properties, and elevation as the most influential covariates for the conterminous U.S. The limited accuracy of regional-scale GLMs, however, suggests the need to further improve the quality and availability of GHG and covariate data through concerted efforts in data collection.

Abstract 3861: Associations of intragenic rearrangement burden with immune cell infiltration and response to immune checkpoint blockade in cancer

Abstract While immune checkpoint blockade (ICB) induces durable cancer remission, only a small subset of patients can receive benefits, whereas a substantial portion of patients encounter severe immune-related adverse events. Further, about 4-29% of patients experience dramatic acceleration of disease progression after ICB treatment. Predictive biomarkers of ICB responses are in high demand to ensure patient benefits and avoid tragic clinical outcomes. In addition to PD-L1 tests, several genetic biomarkers are associated with ICB effectiveness, such as tumor mutation burden (TMB), somatic copy number aberrations, indels, and microsatellite instability. However, none of these neoantigen markers are sufficient to discriminate ICB responders in diverse cancer entities of distinct genetic profiles. In particular, TMB fails to predict in the cancer types with low simple mutation rates, such as triple-negative breast cancer (TNBC), ovarian cancer, and esophageal cancers. In TNBC, high TMB only accounts for 5-10% of patients, whereas a much larger proportion of tumors are inflamed (26-50%), which suggests a possible uncharacterized major source of neoantigens. Similarly, TMB is not predictive of ICB response in ovarian cancers according to the IMagyn050 trial. In esophageal cancer, TMB is not associated with T cell infiltration and does not predict ICB response. Our analyses of Pan-cancer whole genomes revealed that intragenic rearrangement (IGR) burden is a pivotal contributor to immune infiltration in breast, ovarian, esophageal, and endometrial cancers, which correlates with increased Macrophage M1 and CD8+ T cell signatures. IGRs result in exon duplications or deletions through rearrangements of a genomic distance of tens of kilobases. Multivariate regression against spatially counted tumor infiltrated lymphocytes in breast, endometrial, and ovarian cancers suggest that IGR burden is a more influential covariate than other genetic aberrations in these cancers. In the MEDI4736 trial evaluating durvalumab in esophageal adenocarcinoma, IGR burden correlates with patient benefits. In the IMVigor210 trial evaluating atezolizumab in urothelial carcinoma, IGR burden increases with platinum exposure and predicts patient benefits for TMB-low, platinum-exposed tumors. Altogether, we demonstrate that IGR burden contributes to T-cell inflammation and predicts ICB benefits in TMB-low, IGR-dominant tumors, or in platinum-exposed tumors. IGRs as a cryptic class of structural variants have been heretofore overlooked in cancer genomics studies. The association of IGRs with ICB response has not been previously reported in cancer in general. The development of IGR burden-based predictive marker could increase effectiveness of ICB therapy, reduce severe side-effects and unnecessary costs. Citation Format: Han Zhang, Sanghoon Lee, Renee R. Muthakana, Binfeng Lu, David N Boone, Daniel Lee, Xiaosong Wang. Associations of intragenic rearrangement burden with immune cell infiltration and response to immune checkpoint blockade in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3861.

Patterns in bird and pollinator occupancy and richness in a mosaic of urban office parks across scales and seasons.

Urbanization is a leading cause of global biodiversity loss, yet cities can provide resources required by many species throughout the year. In recognition of this, cities around the world are adopting strategies to increase biodiversity. These efforts would benefit from a robust understanding of how natural and enhanced features in urbanized areas influence various taxa. We explored seasonal and spatial patterns in occupancy and taxonomic richness of birds and pollinators among office parks in Santa Clara County, California, USA, where natural features and commercial landscaping have generated variation in conditions across scales. We surveyed birds and insect pollinators, estimated multi-species occupancy and species richness, and found that spatial scale (local, neighborhood, and landscape scale), season, and urban sensitivity were all important for understanding how communities occupied sites. Features at the landscape (distance to streams or baylands) and local scale (tree canopy, shrub, or impervious cover) were the strongest predictors of avian occupancy in all seasons. Pollinator richness was influenced by local tree canopy and impervious cover in spring, and distance to baylands in early and late summer. We then predicted the relative contributions of different spatial scales to annual bird species richness by simulating "good" and "poor" quality sites based on influential covariates returned by the previous models. Shifting from poor to good quality conditions locally increased annual avian richness by up to 6.8 species with no predicted effect on the quality of the neighborhood. Conversely, sites of poor local and neighborhood scale quality in good-quality landscapes were predicted to harbor 11.5 more species than sites of good local- and neighborhood-scale quality in poor-quality landscapes. Finally, more urban-sensitive bird species were gained at good quality sites relative to urban tolerant species, suggesting that urban natural features at the local and landscape scales disproportionately benefited them.

Validation of Population Pharmacokinetic Models for Clozapine Dosage Prediction.

Clozapine is unique in its capacity to ameliorate severe schizophrenia but at high risk of toxicity. A relationship between blood concentration and clinical response and evidence for concentration-response relationships to some adverse effects justify therapeutic drug monitoring of clozapine. However, the relationship between drug dose and blood concentration is quite variable. This variability is, in part, due to inductive and inhibitory interactions varying the activity of cytochrome P450 1A2 (CYP1A2), the principal pathway for clozapine elimination. Several population pharmacokinetic models have been presented to facilitate dose selection and to identify poor adherence in individual patients. These models have faced little testing for validity in independent populations or even for persisting validity in the source population. Therefore, we collected a large population of clozapine-treated patients (127 patients, 1048 timed plasma concentrations) in whom dosing and covariate information could be obtained with high certainty. A population pharmacokinetic model was constructed with data collected in the first 6 weeks from study enrolment (448 plasma concentrations), to estimate covariate influences and to allow alignment with previously published models. The model was tested for its performance in predicting the concentrations observed at later time intervals up to 5 years. The predictive performances of 6 published clozapine population models were then assessed in the entire population. The population pharmacokinetic model based on the first 6 weeks identified significant influences of sex, smoking, and cotreatment with fluvoxamine on clozapine clearance. The model built from the first 6 weeks had acceptable predictive performance in the same patient population up to the first 26 weeks using individual parameters, with a median predictive error (PE) of -0.1% to -15.9% and median absolute PE of 22.9%-27.1%. Predictive performance fell progressively with time after 26 weeks. Bayesian addition of plasma concentration observations within each prediction period improved individual predictions. Three additional observations extended acceptable predictive performance into the second 6 months of therapy. When the published models were tested with the entire data set, median PE ranged from -8% to +35% with a median absolute PE of >39% in all models. Thus, none of the tested models was successful in external validation. Bayesian addition of single patient observations improved individual predictions from all models but still without achieving acceptable performances. We conclude that the relationship between covariates and blood clozapine concentrations differs between populations and that relationships are not stable over time within a population. Current population models for clozapine are not capturing influential covariates.

Can Incorrect Analysis of Time-Dependent Exposure Explain Associations between PM2.5 Exposure and Risk of Dementia?

Epidemiological studies have reported positive associations between long-term exposure to particulate matter of 2.5 microns or less in diameter (PM2.5) and risk of Alzheimer's disease and other clinical dementia. Many of these studies have analyzed data using Cox Proportional Hazards (PH) regression, which estimates a hazard ratio (HR) for the treatment (in this case, exposure) effect on the time-to-event outcome while adjusting for influential covariates. PM2.5 levels vary over time. As air quality standards for PM2.5 have become more stringent over time, average outdoor PM2.5 levels have decreased substantially. Investigate whether a Cox PH analysis that does not properly account for exposure that varies over time could produce a biased HR of similar magnitude to the HRs reported in recent epidemiological studies of PM2.5 and dementia risk. Simulation analysis. We found that the biased HR can affect statistical analyses that consider exposure levels at event times only, especially if PM2.5 levels decreased consistently over time. Furthermore, the direction of such bias is away from the null and of a magnitude that is consistent with the reported estimates of dementia risk in several epidemiological studies of PM2.5 exposure (HR≈1.2 to 2.0). This bias can be avoided by correctly assigning exposure to study subjects throughout the entire follow-up period. We recommend that investigators provide a detailed description of how time-dependent exposure variables were accounted for in their Cox PH analyses when they report their results.

Application of Poisson state-space models and shared frailty models for multistage processes surveillance

ABSTRACT There are many applications in actual multistage processes with Poisson data. Control charts are an essential tool for monitoring quality variables in multistage processes. On the other hand, it is not straightforward to adjust a quality variable for the effect of all influential covariates, and ignoring latent variables introduces unobserved heterogeneity which diminishes the detection power of a monitoring scheme. Hence, the present paper proposes two monitoring schemes namely, the GEWMA control chart based on a Poisson state-space model and the modified EWMA control chart based on a shared frailty model in which latent variables are considered in Phase II. In this regard, expectation – maximization and Bayesian algorithms are used to estimate the parameters of the proposed models. A comparison between the two proposed control charts demonstrates that the GEWMA control chart based on a Poisson state-space model performs better than the modified EWMA control chart using the frailty model. Then, comprehensive simulation experiments are conducted to evaluate the performance of the proposed GEWMA. The results show that it performs satisfactorily under different shifts in single and multiple stages. Also, this control chart helps well to identify the out-of-control stage. Finally, a real case in health-care related processes is used to appraise the performance of the proposed monitoring scheme in practice.

Intragenic Rearrangement Burden Associates with Immune Cell Infiltration and Response to Immune Checkpoint Blockade in Cancer.

Immune checkpoint blockade (ICB) can induce durable cancer remission. However, only a small subset of patients gains benefits. While tumor mutation burden (TMB) differentiates responders from nonresponders in some cases, it is a weak predictor in tumor types with low mutation rates. Thus, there is an unmet need to discover a new class of genetic aberrations that predict ICB responses in these tumor types. Here, we report analyses of pan-cancer whole genomes which revealed that intragenic rearrangement (IGR) burden is significantly associated with immune infiltration in breast, ovarian, esophageal, and endometrial cancers, particularly with increased M1 macrophage and CD8+ T-cell signatures. Multivariate regression against spatially counted tumor-infiltrating lymphocytes in breast, endometrial, and ovarian cancers suggested that IGR burden is a more influential covariate than other genetic aberrations in these cancers. In the MEDI4736 trial evaluating durvalumab in esophageal adenocarcinoma, IGR burden correlated with patient benefits. In the IMVigor210 trial evaluating atezolizumab in urothelial carcinoma, IGR burden increased with platinum exposure and predicted patient benefit among TMB-low, platinum-exposed tumors. Altogether, we have demonstrated that IGR burden correlates with T-cell inflammation and predicts ICB benefit in TMB-low, IGR-dominant tumors, and in platinum-exposed tumors.