Covariate Model Research Articles

Statistical Assessment of Water-quality Parameters for Different Flow Regimes

Groundwater and drainage-water sustainability is crucial to water management. Controlling the interaction between the groundwater and drain-water salinity at the watershed level has only been the topic of a limited number of studies. This study statistically compares salt concentration rate across time and four different flow regimes. We sampled water from 2014 to 2018. The study used R software to analyze water-quality parameters utilizing PCA and indices. Multiple water-quality indices were used to test irrigation water. A linear model with TDS as the response was fitted to the data. Another two-factor repeated-measurement covariate model was built. Water-quality indices suggest that measured water from varied flow regimes in the study area is suitable for irrigation. Based on the statistical models, Tukey's adjustment pairwise demonstrates considerable salinity variations between drain water and surface water. However, the main effect of the flow regime on TDS is significant. This indicates that the TDS value varies depending on the flow regime, not on time plus the interactions between these regimes. Keywords: Total dissolved solids (TDSs), Groundwater, ANCOVA, Flow condition, Tile drain, PCA

Impact of covariate model building methods on their clinical relevance evaluation in population pharmacokinetic analyses: comparison of the full model, stepwise covariate model (SCM) and SCM+ approaches.

Covariate analysis in population pharmacokinetics is key for adjusting doses for patients. The main objective of this work was to compare the adequacy of various modeling approaches on covariate clinical relevance decision-making. The full model, stepwise covariate model (SCM) and SCM+ PsN algorithms were compared in a clinical trial simulation of a 383-patient population pharmacokinetic study mixing rich and sparse designs. A one-compartment model with first-order absorption was used. A base model including a body weight effect on CL/F and V/F and a covariate model including 4 additional covariates-parameters relationships were simulated. As for forest plots, ratios between covariates at a specific value and that of a typical individual were calculated with their 90% confidence interval (CI90) using standard errors. Covariates on CL, V and KA were considered relevant if their CI90 fell completely outside the reference area [0.8-1.2]. All approaches provided unbiased covariate ratio estimates. For covariates with a simulated effect, the 3 approaches correctly identify their clinical relevance. However, significant covariates were missed in up to 15% of cases with SCM/SCM+. For covariate with no simulated effects, the full model mainly identified them as non-relevant or with insufficient information while SCM/SCM+ mainly did not select them. SCM/SCM+ assume that non-selected covariates are non-relevant when it could be due to insufficient information, whereas the full model does not make this assumption and is faster. This study must be extended to other methods and completed by a more complex high-dimensional simulation framework.

Covariate modeling in pharmacometrics: General points for consideration.

Modeling the relationships between covariates and pharmacometric model parameters is a central feature of pharmacometric analyses. The information obtained from covariate modeling may be used for dose selection, dose individualization, or the planning of clinical studies in different population subgroups. The pharmacometric literature has amassed a diverse, complex, and evolving collection of methodologies and interpretive guidance related to covariate modeling. With the number and complexity of technologies increasing, a need for an overview of the state of the art has emerged. In this article the International Society of Pharmacometrics (ISoP) Standards and Best Practices Committee presents perspectives on best practices for planning, executing, reporting, and interpreting covariate analyses to guide pharmacometrics decision making in academic, industry, and regulatory settings.

Population pharmacokinetic analyses of methotrexate in pediatric patients: a systematic review.

Methotrexate is widely utilized in the chemotherapy of malignant tumors and autoimmune diseases in the pediatric population, but dosing can be challenging. Several population pharmacokinetic models were developed to characterize factors influencing variability and improve individualization of dosing regimens. However, significant covariates included varied across studies. The primary objective of this review was to summarize and discuss population pharmacokinetic models of methotrexate and covariates that influence pharmacokinetic variability in pediatric patients. Systematic searches were conducted in the PubMed and EMBASE databases from inception to 7 July 2023. Reporting Quality was evaluated based on a checklist with 31 items. The characteristics of studies and information for model construction and validation were extracted, summarized, and discussed. Eighteen studies (four prospective studies and fourteen retrospective studies with sample sizes of 14 to 772 patients and 2.7 to 93.1 samples per patient) were included in this study. Two-compartment models were the commonly used structural models for methotrexate, and the clearance range of methotrexate ranged from 2.32 to 19.03L/h (median: 6.86L/h). Body size and renal function were found to significantly affect the clearance of methotrexate for pediatric patients. There were limited reports on the role of other covariates, such as gene polymorphisms and co-medications, in the pharmacokinetic parameters of methotrexate pediatric patients. Internal and external evaluations were used to assess the performance of the population pharmacokinetic models. A more rigorous external evaluation needs to be performed before routine clinical use to select the appropriate PopPK model. Further research is necessary to incorporate larger cohorts or pool analyses in specific susceptible pediatric populations to improve the understanding of predicted exposure profiles and covariate identification.

Automated covariate modeling using efficient simulation of pharmacokinetics

Pharmacometric modeling plays an important role in drug development and personalized medicine. Pharmacometric covariate models can be used to describe the relationships between patient characteristics (such as age and weight) and pharmacokinetic (PK) parameters. Traditionally, the functional structure of these relationships are obtained manually. This is a time-consuming task, and consequently limits the search space of covariate relationships. The use of data-driven machine learning (ML) in pharmacometrics has the potential to automate the search for adequate model structures, which can speed up the modeling process and enable the evaluation of a wider range of model candidates. Even with moderately sized data sets, ML approaches require millions of simulations of pharmacokinetic (PK) models, which dictates the need for an efficient simulator. In this paper, we demonstrate how to automate covariate modeling using neural networks (NNs), that are trained using efficient PK simulation techniques. We apply the methodology to a propofol data set with 1031 individuals and compare the results to previously published covariate models for propofol. We use the NN as a function approximator that relates covariates to the parameters of a three-compartment PK model, and train it on dose and plasma concentration time series. Our study demonstrates that NN-based covariate modeling allows for automation of the otherwise time-consuming task of identifying which of available covariates to include in the model, and what functional mappings from these covariates to PK model parameters to consider in the model search. Additional to this saving in modeler effort, the NN-based model obtained in our clinical data set example has PK parameters within a clinically reasonable range, and slightly enhanced predictive precision than a previously published state-of-the-art covariate models for propofol model.

Understanding hemoglobin contribution to high-dose methotrexate disposition-population pharmacokinetics in pediatric patients with hematological malignancies.

The aim of the present study was to develop a population pharmacokinetic model for methotrexate (MTX) during high-dose treatment (HDMTX) in pediatric patients with acute lymphoblastic leukemia (ALL) and non-Hodgkin's lymphoma (NHL) and to describe the influence of variability factors. The study included 50 patients of both sexes (aged 1-18years) who received 3 or 5g/m2 of HDMTX. A nonlinear mixed effect modeling approach was applied for data analysis. Parameter estimation was performed by first-order conditional estimation method with interaction (FOCEI), whereas stepwise covariate modeling was used to assess variability factors. The final model is a two-compartment model that incorporates the effect of body surface area and the influence of hemoglobin and serum creatinine on MTX clearance (CL). Population pharmacokinetic values for a typical subject were estimated at 5.75 L/h/m2 for clearance (CL), 21.3 L/m2 for volume of the central compartment (V1), 8.2 L/m2 for volume of the peripheral compartment (V2), and 0.087 L/h/m2 for intercompartmental clearance (Q). According to the final model, MTX CL decreases with increasing serum creatinine, whereas a positive effect was captured for hemoglobin. A difference of almost 32% in MTX CL was observed among patients' hemoglobin values reported in the study. The developed population pharmacokinetic model can contribute to the therapy optimization during HDMTX in pediatric patients with ALL and NHL. In addition to renal function and body weight, it describes the influence of hemoglobin on CL, allowing better understanding of its contribution to the disposition of HDMTX.

Comprehensive modelling of prostate cancer progression: integrating continuous and binary biomarkers with event time data and missing covariates

Longitudinal studies involve recording observations at scheduled visits or time points for individuals until a predetermined event, like reaching satisfactory tumor shrinkage in cancer studies. Furthermore, dropout in longitudinal studies leads to incomplete data, which significantly increases the risk of bias. An amended joint shared-random effects model (SREM) is proposed for mixed continuous and binary longitudinal measurements and a time-to-event (TTE) outcome, incorporating missing covariates. In the proposed model, a conditional model is applied for the mixed continuous and binary longitudinal outcomes; a mixed effect model is considered for the continuous longitudinal outcome. For the binary longitudinal outcome, given the continuous longitudinal outcome, a logistic mixed effect model is considered. These models share common random effects with the model for the event time outcome. The model formulation is based on Bayesian statistical thinking via Markov Chain Monte Carlo (MCMC). The proposed joint modelling is applied to contribute to the understanding of the progression of prostate cancer (PCa) by considering a generalized linear mixed effects model for time-varying covariates that incorporate ignorable missingness. The association between prostate-specific antigen (PSA) with alkaline phosphatase (ALP) and tumor status has been studied with mixed conclusions.

Risk Modeling of Time-Varying Covariates Using an Ensemble of Survival Trees: Predicting Future Cancer Events

The challenge of survival prediction is ubiquitous in medicine, but only a handful of methods are available for survival prediction based on time-varying data. Here we propose a novel method for this problem, using a random forest of survival trees for left-truncated and right-censored data. We demonstrate the advantage of our method on prediction of breast cancer and prostate gland cancer risk among healthy individuals by analyzing routine laboratory measurements, vital signs and age. We analyze electronic medical records of 20,317 healthy individuals who underwent routine checkups and identified those who later developed cancer. In cross-validation, our method predicted future prostate and breast cancers six months before diagnosis with an area under the ROC curve of 0.62±0.05 and 0.6±0.03 respectively, outperforming standard random forest, random survival forest, cox-regression model, dynamic deep-hit and a single survival tree. Our work proposes a new framework for survival risk prediction in time-varying data and our results suggest that computational analysis of data on healthy individuals can improve the detection of those at risk of future cancer development.

Population Pharmacokinetic Modeling of Cotadutide: A Dual Agonist Peptide of Glucagon-Like Peptide and Glucagon Receptors Administered to Participants with Type II Diabetes Mellitus, Chronic Kidney Disease, Obesity and Non-Alcoholic Steatohepatitis.

Cotadutide is a dual glucagon-like peptide-1 (GLP-1) and glucagon (GCG) receptor agonist peptide. The objective of this analysis was to develop a population pharmacokinetic (popPK) model of cotadutide, and to identify any potential effect on the PK from intrinsic and extrinsic covariates. The popPK analysis utilized a non-linear mixed-effects modeling approach using the data from 10 clinical studies in different participant categories following once-daily subcutaneous dose administration ranging from 20 to 600μg. Additionally, the covariates affecting cotadutide exposure were quantified, and the model performance was evaluated through the prediction-corrected visual predictive checks. A one-compartment model with first-order absorption and elimination adequately described the data as confirmed via visual predictive check plots and parameter plausibility. The mean values for cotadutide apparent clearance (CL/F), apparent volume of distribution (V/F), absorption rate constant (Ka), and half-life were 1.05 L/h, 20.0 L, 0.38 h-1, and 13.3 hours, respectively. Covariate modeling identified body weight, alanine transaminase, albumin, anti-drug antibody (ADA) titer values, formulation strength and injection device, and participant categories as significant covariates on PK parameters, where ADAs have been identified to decrease cotadutide clearance. The model demonstrated that a 150-kg participant was estimated to have 30% lower for both AUC and Cmax and a 66 kg participant was estimated to have 35% higher for both AUC and Cmax relative to a reference individual with a median weight of 96 kg. A popPK model was developed for cotadutide with cotadutide clinical data, and the impact of the statistically significant covariates identified was not considered clinically meaningful. The popPK model will be used to evaluate exposure-response relationships for cotadutide clinical data.

Impact of lactoferrin supplementation on cotrimoxazole pharmacokinetics: A preliminary clinical investigation.

Cotrimoxazole, a commonly prescribed antibiotic, has substantial resistance, especially in Indonesia, with its uropathogenic resistance reaching 67% in 2017. Although cotrimoxazole has been suggested to be co-administered with lactoferrin to enhance its antibacterial effectiveness and this practice has been widely adopted since the Covid-19 pandemic, the impact of lactoferrin on the pharmacokinetics of cotrimoxazole remains relatively unknown. This study aims to conduct a preliminary clinical investigation into the impact of lactoferrin supplementation on the pharmacokinetics of cotrimoxazole, focusing on the elimination rate and excretion of unchanged drug in urine. This study employed a blinded, cross-over, single-dose pharmacokinetics investigation, which included five healthy volunteers as participants. In the initial period, the first group received cotrimoxazole (80 mg trimethoprim and 400 mg sulfamethoxazole) along with a lactoferrin-containing supplement, while the second group only received cotrimoxazole. Subsequently, after a washout period, the conditions were reversed. Urine sampling was conducted at intervals from 0 to 24 hours post-medication, and drug levels in the urine were determined using high-performance liquid chromatography. The population-based pharmacokinetic analysis revealed that the optimal model was the one-compartment model with first-order elimination and proportional residual error. The findings show that the administration of lactoferrin-containing supplements did not significantly influence the covariate model and, therefore, did not alter the pharmacokinetics parameter of cotrimoxazole in urine with a single administration, implying that lactoferrin did not cause drug interaction problems when given simultaneously.

Parametric frailty model based analysis of survival data with time-varying risk factors

In the follow-up study, a covariate changing over time is known as a time-varying covariate. By using the frailty model, we can analyze the effect of such a variable on survival time. When heterogeneity is present, the statistical analysis of the survival data without considering frailty gives a less accurate estimate of parameters. The proportional hazard (PH) assumption of the Cox regression model for time-varying covariates does not hold. The PH assumption was tested using Schoenfeld residuals, and a p-value of less than 0.05 was deemed statistically significant. The covariates' p-value is less than 0.05, which suggests that they do not meet the PH assumption and shows that the covariate is time-varying. This article aims to illustrate how to carry out statistical analysis by using frailty models in the presence of time-varying covariates and to compare their performance. Two frailty models, namely the Gamma frailty model and the inverse-Gaussian frailty model with log-normal and Weibull as initial hazards, were used in this study. AIC and BIC were used to compare the performance of the models. An application to hospital readmission data for patients with colorectal cancer is illustrated.

Grouping dynamics of the northernmost population of blackbuck (Antilope cervicapra) in Krishnasaar Conservation Area, Nepal

Blackbuck, Antilope cervicapra, are group-living grazers found in a wide range of habitats, from dry grasslands to open forests. Krishnasaar Conservation Area (KrCA) in Bardia, Nepal is home to the northernmost population of blackbuck. This population lives in close proximity to humans. As a population in an extreme of the species’ range and experiencing direct and indirect human influences, KrCA is suitable to study less explored facets of herbivore ecology and behavior. One such less explored behavior of herbivores is their grouping (herding) behavior. Grouping is commonly seen as an anti-predator response and could also be used in the context of anthropogenic threat. In this study we investigated herding behavior of blackbuck and ecological and anthropogenic factors that might influence the herding. To record the grouping behavior of blackbuck in the different habitat types, we used scan sampling methods for groups. A group was defined by including all individuals that are within 50 m of another individual. The influence of ecological and anthropogenic factors on herding pattern was assessed through simple regression modelling. We scanned animals for 89 hours and each hour had seven observations. The mean group size was 20.56 ±1.97 (mean ± SE). More than two-thirds of animals observed were female. We found larger herd sizes in the morning compared with herd sizes in the afternoon and evening. Our observations also showed that mixed herds were significantly larger in size than male-only, and female-only herds in both core and settlement zones. Our modelling of covariates predicted group type to be the single most influential factor influencing herd size. As herding behavior of animals reflect associated risk and resource factors, the finding of this work can be helpful in managing this isolated threatened population of blackbuck.

Properties of the full random-effect modeling approach with missing covariate data.

During drug development, a key step is the identification of relevant covariates predicting between-subject variations in drug response. The full random effects model (FREM) is one of the full-covariate approaches used to identify relevant covariates in nonlinear mixed effects models. Here we explore the ability of FREM to handle missing (both missing completely at random (MCAR) and missing at random (MAR)) covariate data and compare it to the full fixed-effects model (FFEM) approach, applied either with complete case analysis or mean imputation. A global health dataset (20421 children) was used to develop a FREM describing the changes of height for age Z-score (HAZ) over time. Simulated datasets (n = 1000) were generated with variable rates of missing (MCAR) covariate data (0%-90%) and different proportions of missing (MAR) data condition on either observed covariates or predicted HAZ. The three methods were used to re-estimate model and compared in terms of bias and precision which showed that FREM had only minor increases in bias and minor loss of precision at increasing percentages of missing (MCAR) covariate data and performed similarly in the MAR scenarios. Conversely, the FFEM approaches either collapsed at 70% of missing (MCAR) covariate data (FFEM complete case analysis) or had large bias increases and loss of precision (FFEM with mean imputation). Our results suggest that FREM is an appropriate approach to covariate modeling for datasets with missing (MCAR and MAR) covariate data, such as in global health studies.

Marginal Distribution Fitting Method for Modelling Flood Extremes on a River Network

This study utilized a max-stable process (MSP) model with a dependence structure defined via a non-Euclidean distance metric, with the goal of modelling extreme flood data on a river network. The dataset was composed of mean daily discharge observations from 22 United States Geological Survey streamflow gaging stations for river basins in Missouri and Arkansas. The analysis included the application of the elastic-net penalty to automatically build spatially varying trend surfaces to model the marginal distributions. The dependence model accounted for the river distance between hydrologically connected gaging sites and the hydrologic distance, defined as the Euclidean distance between the centers of site’s associated drainage areas, for all stations. Modelling the marginal distributions and spatial dependence among the extremes are two key components for spatially modelling extremes. Among the 16 covariates evaluated for marginal fitting, 7 were selected to spatially model the generalized extreme value (GEV) location parameter (for each gaging station’s contributing drainage basin, its outlet elevation, centroid x coordinate, centroid elevation, area, average basin width, elevation range, and median land surface slope). The three covariates selected for the GEV scale parameter included the area, average basin width, and median land surface slope. The GEV shape parameter was assumed to be constant throughout the entire study area. Comparisons of estimates obtained from the spatial covariate model with their corresponding “at-site” estimates resulted in computed values of 0.95, 0.95, 0.94 and 0.85, 0.84, 0.90 for the coefficient of determination, Nash–Sutcliffe efficiency, and Kling–Gupta efficiency for the GEV location and scale parameters, respectively. Brown–Resnick MSP models were fit to independent multivariate events extracted from a set of common discharge data, transformed to unit Fréchet margins while considering different permutations of the non-Euclidean dependence model. Each of the fitted model’s log-likelihood values indicated improved fits when using hydrologic distance rather than Euclidean distance. They also demonstrated that accounting for flow-connected dependence and anisotropy further improved model fit. In this study, the results from both parts were illustrative; however, further research with larger datasets and more heterogeneous systems is recommended.

Modelling the progression of illicit substance use patterns from real-world evidence.

To investigate an innovative pharmacometrics approach that addresses the challenges of using real-world evidence to model the progression of illicit substance use. The modelling strategy analysed real-world data from the National Longitudinal Study of Adolescent to Adult Health (AddHealth) survey using survival analyses and differential equations. Respondents were categorized into drug-naïve, active users and nonusers. The transitions between categories were modelled using interval-censored parametric survival analysis. The resulting hazard rate functions were used as time-dependent rate constants in a differential equation system. Covariate models for sex and depression status were assessed. AddHealth enrolled 6504 American teenagers (median age 16 years, range 11-21 years); this cohort was followed with five interviews over a 22-year period; the median age at the last interview was 38 years (range 34-45 years). The percentages of illicit drug users at Interviews 1-5 were 7.7%, 5.9%, 15.8%, 21.4% and 0.98%, respectively. The generalized gamma distribution emerged as the preferred model for the survival functions for transitions between categories. Age-dependent prevalence was obtained from the differential equation system. Active drug use was more prevalent in males, increased in adolescence and college years, peaked at 24 years, and decreased to low levels by 35 years. Depression, which was more frequent in females, increased the drug-naïve-active user transition rates but not the active user-nonuser and nonuser-active user transition rates. The evidence did not support an interaction between sex and depression. The model provided a satisfactory approximation for the age-dependent progression of illicit substance use from preadolescence to early middle age.

Determinants and Clinical Impact of Time to Plasma Exchange in Patients with Immune Thrombotic Thrombocytopenic Purpura

Background: Immune thrombotic thrombocytopenic purpura (iTTP) is a hematologic emergency characterized by severe acquired deficiency in the von Willebrand factor-cleaving metalloprotease ADAMTS13 and thrombotic microangiopathy, resulting in end organ damage and death if untreated. Plasma exchange (PLEX) is vital to the management of iTTP and has dramatically improved survival in this disorder since the 1980s. Guidelines recommend initiation of PLEX within 4-8 hours of presentation. However, despite the importance of prompt medical intervention, determinants of the total interval between symptom onset and initiation of PLEX (S2P) and time from presentation at the pheresis center to PLEX (door to PLEX, D2P) remain unclear. Further, the impact of S2P and D2P on clinical outcomes in iTTP have not been evaluated. Objective: Identify factors that predict S2P and D2P in patients with iTTP and assess the clinical impacts of S2P and D2P. Methods: We retrospectively evaluated cases in the Harvard TMA Research Collaborative (HaTRC), a curated single-center rare disease registry containing all consecutive iTTP cases seen between 2004 and 2022 at the three main Harvard teaching hospitals. We restricted our modeling analyses to patients who resided <350 km from downtown Boston. S2P was defined as time from patient-reported symptom onset until initiation of PLEX. D2P was defined as the time from the first complete blood count specimen drawn at a HaTRC hospital until the initiation of PLEX. Determinants of S2P and D2P were identified using multiple linear regression modeling of covariates derived from the HaTRC registry. The impacts of S2P and D2P on mortality and the composite of mortality and need for ICU admission were assessed using multiple logistic regression modeling. Results: A total of 155 unique patients comprising 243 separate iTTP episodes were recorded ( Figure 1). Analyses were limited to patients who resided <350 km from downtown Boston (n = 147 patients, 227 cases). The median (IQR) age was 40 (29-53) years. The cohort was 76% female, 63% of European ancestry, and 45% of episodes were relapse presentations. The median (IQR) S2P for the entire cohort was 4.3 (1.8-7.5) days, and the median D2P was 8.4 (5.0-16.0) hours. Excluding relapse presentations, crude S2P correlated with distance from Boston (Spearman r = 0.216, 95% CI: 0.029-0.389, p=0.02). After adjusting for age, sex, race, and LDH, we found that higher platelet count at presentation (β=0.039, 95% CI: 0.00047 to 0.077, p=0.047) and increased distance from Boston (β=0.030, 95% CI: 0.007 to 0.053, p=0.011) were associated with longer S2P, while patents with relapse presentations experienced a shorter S2P (β=-1.9, 95% CI: -3.7 to -0.12, p=0.037) ( Table 1). By contrast, increased D2P was significantly associated with male sex (β=7.43, 95% CI: 1.128 to 13.73, p=0.021) and higher platelet count (β=0.16, 95% CI: 0.41 to 0.27, p=0.008) but not with distance from Boston or relapse status. Longer S2P or D2P times were not associated with increased mortality, hospital LOS, or the composite of mortality and need for ICU admission; however, there was a non-significant trend towards increased LOS with relapse presentations (p=0.06) and longer D2P times (p=0.09). Conclusions: Our data suggest that iTTP patients who live farther from the hospital where they ultimately receive PLEX, those with higher platelet counts at presentation, and those presenting for the first time with iTTP experience a longer time to initiation of care. After hospital presentation, the determinants of a longer time to PLEX initiation shift to male sex and higher platelet count, both of which may be associated with lower clinical suspicion for iTTP and delayed diagnosis. These findings should be externally validated in an independent dataset.

Understanding Ability and Reliability Differences Measured with Count Items: The Distributional Regression Test Model and the Count Latent Regression Model

In psychology and education, tests (e.g., reading tests) and self-reports (e.g., clinical questionnaires) generate counts, but corresponding Item Response Theory (IRT) methods are underdeveloped compared to binary data. Recent advances include the Two-Parameter Conway-Maxwell-Poisson model (2PCMPM), generalizing Rasch’s Poisson Counts Model, with item-specific difficulty, discrimination, and dispersion parameters. Explaining differences in model parameters informs item construction and selection but has received little attention. We introduce two 2PCMPM-based explanatory count IRT models: The Distributional Regression Test Model for item covariates, and the Count Latent Regression Model for (categorical) person covariates. Estimation methods are provided and satisfactory statistical properties are observed in simulations. Two examples illustrate how the models help understand tests and underlying constructs.

Monitoring the process mean under the Bayesian approach with application to hard bake process

This study introduces the Bayesian adaptive exponentially weighted moving average (AEWMA) control chart within the framework of measurement error, examining two separate loss functions: the squared error loss function and the linex loss function. We conduct an analysis of the posterior and posterior predictive distributions utilizing a conjugate prior. In the presence of measurement error (ME), we employ a linear covariate model to assess the control chart's effectiveness. Additionally, we explore the impacts of measurement error by investigating multiple measurements and a method involving linearly increasing variance. We conduct a Monte Carlo simulation study to assess the control chart's performance under ME, examining its run length profile. Subsequently, we offer a specific numerical instance related to the hard-bake process in semiconductor manufacturing, serving to verify the functionality and practical application of the suggested Bayesian AEWMA control chart when confronted with ME.

Population pharmacokinetics of mirvetuximab soravtansine in patients with folate receptor-α positive ovarian cancer: The antibody-drug conjugate, payload and metabolite.

Mirvetuximab soravtansine is a first-in-class antibody-drug conjugate recently approved for the treatment of folate receptor-α positive ovarian cancer. The aim of this study was to develop a population pharmacokinetic model to describe the concentration-time profiles of mirvetuximab soravtansine, the payload (DM4) and a metabolite (S-methyl-DM4). Mirvetuximab soravtansine was administered intravenously from 0.15 to 7mg/kg to 543 patients with predominantly platinum-resistant ovarian cancer in 3 clinical studies, and the plasma drug concentrations were analysed using a nonlinear mixed-effects modelling approach. Stepwise covariate modelling was performed to identify covariates. We developed a semi-mechanistic population pharmacokinetic model that included linear and nonlinear routes for the elimination of mirvetuximab soravtansine and a target compartment for the formation and disposition of the payload and metabolite in tumour cells. The clearance and volume of the central compartment were 0.0153 L/h and 2.63 L for mirvetuximab soravtansine, 8.83 L/h and 3.67 L for DM4, and 2.04 L/h and 6.3L for S-methyl-DM4, respectively. Body weight, serum albumin and age were identified as statistically significant covariates. Exposures in patients with renal or hepatic impairment and who used concomitant cytochrome P450 (CYP) 3A4 inhibitors were estimated. There is no need for dose adjustment due to covariate effects for mirvetuximab soravtansine administered at the recommended dose of 6mg/kg based on adjusted ideal body weight. Dose adjustment is not required for patients with mild or moderate renal impairment, mild hepatic impairment, or when concomitant weak and moderate CYP3A4 inhibitors are used.

Feasibility of brain age predictions from clinical T1-weighted MRIs

An individual's brain predicted age minus chronological age (brain-PAD) obtained from MRIs could become a biomarker of disease in research studies. However, brain age reports from clinical MRIs are scant despite the rich clinical information hospitals provide. Since clinical MRI protocols are meant for specific clinical purposes, performance of brain age predictions on clinical data need to be tested. We explored the feasibility of using DeepBrainNet, a deep network previously trained on research-oriented MRIs, to predict the brain ages of 840 patients who visited 15 facilities of a health system in Florida. Anticipating a strong prediction bias in our clinical sample, we characterized it to propose a covariate model in group-level regressions of brain-PAD (recommended to avoid Type I, II errors), and tested its generalizability, a requirement for meaningful brain age predictions in new single clinical cases. The best bias-related covariate model was scanner-independent and linear in age, while the best method to estimate bias-free brain ages was the inverse of a scanner-independent and quadratic in brain age function. We demonstrated the feasibility to detect sex-related differences in brain-PAD using group-level regression accounting for the selected covariate model. These differences were preserved after bias correction. The Mean-Average Error (MAE) of the predictions in independent data was ∼8 years, 2–3 years greater than reports for research-oriented MRIs using DeepBrainNet, whereas an R2 (assuming no bias) was 0.33 and 0.76 for the uncorrected and corrected brain ages, respectively. DeepBrainNet on clinical populations seems feasible, but more accurate algorithms or transfer-learning retraining is needed.