Dropout Model Research Articles

H∞ control for interval type-2 fuzzy singularly perturbed systems with multi-node round-robin protocol and packet dropouts

The H∞ control of the interval type-2 (IT2) fuzzy singularly perturbed systems (SPSs) with packet losses is studied in this paper. To alleviate data collision, a multi-node round-robin protocol (RRP) is adopted to decide the communication sequences between the sensors and controller. With the help of multi-node RRP, only partial sensor nodes have the priority to access the network channels at each instant. Meanwhile, a Bernoulli packet dropout model is used to further analyze the occurrence of packet dropouts during data transmission. This article's main research results and contributions consist of two parts: (1) Firstly, by combining multi-node RRP, Bernoulli packet loss model and mismatched membership functions, an IT2 fuzzy controller dependent on scheduling state information is designed, under which the control performance of the system can be effectively enhanced. (2) Secondly, by establishing a new Lyapunov functional related to singular perturbation parameter, the system stability conditions can be obtained and ill-conditioned issue is alleviated. Eventually, the feasibility of developed method is validated by the Van der Pol circuit system example.

GDnet-IP: Grouped Dropout-Based Convolutional Neural Network for Insect Pest Recognition

Lightweight convolutional neural network (CNN) models have proven effective in recognizing common pest species, yet challenges remain in enhancing their nonlinear learning capacity and reducing overfitting. This study introduces a grouped dropout strategy and modifies the CNN architecture to improve the accuracy of multi-class insect recognition. Specifically, we optimized the base model by selecting appropriate optimizers, fine-tuning the dropout probability, and adjusting the learning rate decay strategy. Additionally, we replaced ReLU with PReLU and added BatchNorm layers after each Inception layer, enhancing the model’s nonlinear expression and training stability. Leveraging the Inception module’s branching structure and the adaptive grouping properties of the WeDIV clustering algorithm, we developed two grouped dropout models, the iGDnet-IP and GDnet-IP. Experimental results on a dataset containing 20 insect species (15 pests and five beneficial insects) demonstrated an increase in cross-validation accuracy from 84.68% to 92.12%, with notable improvements in the recognition rates for difficult-to-classify species, such as Parnara guttatus Bremer and Grey (PGBG) and Papilio xuthus Linnaeus (PXLL), increasing from 38% and 47% to 62% and 93%, respectively. Furthermore, these models showed significant accuracy advantages over standard dropout methods on test sets, with faster training times compared to four conventional CNN models, highlighting their suitability for mobile applications. Theoretical analyses of model gradients and Fisher information provide further insight into the grouped dropout strategy’s role in improving CNN interpretability for insect recognition tasks.

Modeling informative dropout in longitudinal data: A joint model approach

In medical studies, a disease’s progress is often monitored through indicators that signify the improved or worsened condition of the patient. These are known as longitudinal biomarkers, which are observed along with an event of importance. Together, they form the framework of joint modeling, which has a longitudinal process and an inherently associated time-to-event process. In clinical studies, the change in biomarkers is often monitored in the form of a change in the patient’s plasma level after a drug is administered to the patient. Again, in such studies, patients also withdraw from the trials prematurely or at a later phase, thus giving rise to dropouts. In most cases, this dropout is not random (Missing Not at Random). A joint model has been considered to incorporate this informative dropout in longitudinal response. To demonstrate this approach, a one-compartmental pharmacokinetic (PK) nonlinear mixed-effects model consisting of time-dependent parameters has been used in this work. The dropout mechanism has been introduced using a proportional hazard model. A Bayesian model framework is adopted to study the model’s performance through detailed simulation. A PK study on the drug Divalproex subject to an informative dropout model has been discussed. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 97-110.

Leak Detection in Urban Hydraulic Systems Using the K-BiLSTM-Monte Carlo Dropout Model

Utility companies lose approximately 35 liters of water for every 100 produced due to incorrect, illegal connections and the poor condition of pipes. This study develops an intelligent model to detect leaks using the Kalman filter, BiLSTM neural networks, and the Monte Carlo Dropout algorithm. Using data from the Empresa de Acueductos y Alcantarillados de Bogotá (EAAB), Colombia, autocorrelation analysis, PCA, cluster analysis, ADF and Durbin-Watson tests, Hurst exponent, spectral analysis, and wavelet transform were performed. Then, Kalman filtering techniques were applied, and a BiLSTM architecture controlled with Monte Carlo dropout was implemented. The results showed an accuracy of 87.48% in training and 80.48% in validation. Temporal analysis revealed a stationary behavior in the flow series, and the decrease in spectral intensity around 0.25 Hz was related to pressure perturbations caused by leaks. A detailed evaluation of pressure and flow signals identified leak patterns with high precision, demonstrating the effectiveness of the wavelet spectrogram in detecting energy disturbances. The novelty of the study lies in the integration of advanced artificial intelligence and combinatorial optimization techniques to improve water resource management, allowing early and accurate detection of leaks, significantly improving compared to traditional methods. Doi: 10.28991/CEJ-2024-010-07-01 Full Text: PDF

An exploratory view into allelic drop-out of sequenced autosomal STRs.

As massively parallel sequencing is implemented in forensic genetics, an understanding of sequence data must accompany these advancements, that is, accurate modeling of data for proper statistical analysis. Allelic drop-out, a common stochastic effect seen in genetic data, is often modeled in statistical analysis of STR results. This proof-of-concept study sequenced several serial dilutions of a standard sample ranging from 4 ng to 7.82 pg to evaluate allelic drop-out trends on a select panel of autosomal STRs using the ForenSeq™ DNA Signature Prep Kit, Primer Set A on the Illumina MiSeq FGx. Parameters assessed included locus, profile, and run specific information. A majority of the allelic drop-out occurred in DNA concentrations less than 31.25 pg. Statistical results indicated a need for locus-specific modeling based on STR descriptors, like simple versus compound repeat patterns. No correlation was seen between average read count of scored alleles and allelic drop-out at a locus. A statistical correlation was observed between the amount of allelic drop-out and the starting amount of DNA in a sample, average read count of a sample, and total read count generated on a flow cell. This study supports using common allelic drop-out factors used in fragment length analysis on sequenced STRs while including additional locus, sample, and run specific information. Results demonstrate multiple factors that can be considered when developing probability of allelic drop-out models for sequenced autosomal STRs including locus-specific analysis, total read count of a profile, and total read count sequenced on a flow cell.

Handling missingness and specify the dropout models in haematocrit levels over time in patients who received renal graft

Chronic Kidney Disease (CKD) or renal failure is a public global health problem with an estimated prevalence of as 8 to 16% worldwide. This study was conducted in order to Specify Dropout Models the haematocrit levels over time in renal patients after their transplant. This is a longitudinal study that consisted of 1160 patients who received a renal transplant. Level of Haematocrit level as a response, time (log (years)), gender and age of the patients just to mention a few were as a covariate. Different statistical methods, such as dropout models were employed. Direct likelihood and MI based approach of Linear Mixed Model revealed that all the covariates included in the final models were significant. It was observed that the evolution of haematocrit levels follows a quartic time trend and the effect of time on the evolution varies according to gender and age. The effect of time followed a significant 5th power time trend under MI GEE and quadratic time trend under WGEE. However, both models are valid under MAR. In addition, age, gender and rejection symptoms have a significant effect on the evolution of haematocrit levels in renal patients, however their effect varies with time.

Perceived stress and well-being in doctoral students: effects on program satisfaction and intention to quit

ABSTRACT Stress is a common negative emotion in students. Given the stress associated with doctoral studies, it is crucial to examine the influence of stress on well-being, program satisfaction, and retention in doctoral programs. This study examined stress-related issues and their relationships with intention to quit in a sample of 2,486 students enrolled in doctoral programs representing 38 disciplines. Participants completed a web-based questionnaire including sociodemographic and self-report measures assessing perceived stress, emotional, social, and psychological well-being, as well as program satisfaction and intention to quit. We tested three hypotheses based on Lazarus and Folkman’s transactional theory of stress and emotions and Núñez-Regueiro’s stress process model of school dropout. The results of Structural Equation Modeling (SEM) supported the hypotheses of the study and showed that perceived stress is negatively associated with emotional, social, and psychological well-being. The most significant finding from this study is that perceived stress, directly and indirectly contributes to lower program satisfaction in doctoral students and a stronger intention to quit. These study findings underscore the need for departments to actively support students in completing their dissertations by establishing explicit expectation norms.

Development of Clinical Trial Simulation Tools for Alzheimer’s Disease through the Critical Path for Alzheimer’s Disease (CPAD) Consortium

AbstractBackgroundOptimized clinical trial design remains a major challenge in drug development for Alzheimer’s Disease AD). To advance our understanding of the underlying pathophysiology of disease and help optimize clinical trial design, CPAD developed a clinical trial simulation (CTS) tool for the pre‐dementia stages of Alzheimer’s disease, using CDR‐SB as the primary endpoint. The tool is based on a disease progression model integrating baseline hippocampal volume, the effect of patient drop‐out and relevant sources of variability. The CTS was enhanced using data from contemporary clinical trials, adding fluid and imaging biomarkers of amyloid (Aβ) and tau pathology. Also included are dropout models, a placebo effect model, as well as drug‐effect models. Positron Emission Tomography (PET) measures of tau deposition, leveraging ongoing collaborative efforts to develop a standardized quantification scale across different tau tracers, will also be incorporated, to develop the first disease progression models utilizing a harmonized tau‐PET scale.MethodData were harmonized and both linear and non‐linear mixed effects models were developed for multiple endpoints, including demographics, time from and to diagnosis, genetic status (APOE4). Monte Carlo simulations were performed to compare the statistical power by sample size in trials with and without enrichment using relevant covariates. A longitudinal mixed‐effects model was fit with longitudinal tau and neurodegeneration outcomes. Cognition and biomarkers were explored in a multivariate model.ResultAs of October 2022, CPAD’s clinical trial repository contains over 7,000 unique participants that have baseline CDR‐SB measurement, 4,000+ participants with baseline Aβ measures (fluid or PET), and 2,000+ participants with baseline measures of amyloid, tau (e.g., cerebrospinal fluid p‐tau and/or tau‐PET), and neurodegeneration (e.g., MRI or neurofilament light). With these data, we modeled longitudinal trajectories of cognitive decline using baseline levels of amyloid, tau, and neurodegeneration. Future work will add models of longitudinal biomarker trajectories.ConclusionThe disease progression models will serve as the basis for a clinical trial simulation tool to facilitate design of contemporary clinical trials based on a quantitative understanding of the biological progression of key pathological features, amyloid and tau deposition, of AD.

Bayesian second-order sensitivity of longitudinal inferences to non-ignorability: an application to antidepressant clinical trial data.

Incomplete data is a prevalent complication in longitudinal studies due to individuals' drop-out before intended completion time. Currently available methods via commercial software for analyzing incomplete longitudinal data at best rely on the ignorability of the drop-outs. If the underlying missing mechanism was non-ignorable, potential bias arises in the statistical inferences. To remove the bias when the drop-out is non-ignorable, joint complete-data and drop-out models have been proposed which involve computational difficulties and untestable assumptions. Since the critical ignorability assumption is unverifiable based on the observed part of the sample, some local sensitivity indices have been proposed in the literature. Specifically, Eftekhari Mahabadi (Second-order local sensitivity to non-ignorability in Bayesian inferences. Stat Med 2018;59:55-95) proposed a second-order local sensitivity tool for Bayesian analysis of cross-sectional studies and show its better performance for handling bias compared with the first-order ones. In this paper, we aim to extend this index for the Bayesian sensitivity analysis of normal longitudinal studies with drop-outs. The index is driven based on a selection model for the drop-out mechanism and a Bayesian linear mixed-effect complete-data model. The presented formulas are calculated using the posterior estimation and draws from the simpler ignorable model. The method is illustrated via some simulation studies and sensitivity analysis of a real antidepressant clinical trial data. Overall, the numerical analysis showed that when repeated outcomes are subject to missingness, regression coefficient estimates are nearly approximated well by a linear function in the neighbourhood of MAR model, but there are a considerable amount of second-order sensitivity for the error term and random effect variances in Bayesian linear mixed-effect model framework.

Predictive Model of Student Dropout Based on Logistic Regression

Student desertion is a phenomenon that has spread significantly in many higher education institutions in Ecuador. The objective of the research was to develop a predictive model of student dropout based on multiple binary logistic regression, with the purpose of detecting possible dropouts. The methodology used consists of three phases: Phase 1: Analysis of variables; Phase 2: Formulation of the mathematical model; and Phase 3: Evaluation. For the estimation of the coefficients of the model, the SPSS tool was obtained. After the creation of the predictive model, it was concluded that the most significant variables that contribute to the diagnosis of dropout are marital status, age, gender, Note2s, and Note1s. It is also evident that students have a higher risk of dropping out if they are married and lower risk if they are single or divorced. Finally it was concluded that gender is a factor that directly influences dropout; male students are more likely to drop out than females. Keywords: logistic regression, predictive model, desertion. Resumen La deserción estudiantil es un fenómeno que se ha extendido significativamente en gran cantidad de instituciones educativas de nivel superior en el Ecuador. El objetivo de la investigación fue desarrollar un modelo predictivo de deserción estudiantil basado en la regresión logística binaria múltiple, con el propósito de detectar a posibles desertores. La metodología utilizada consta de tres fases: Fase1: Análisis de variables. Fase2: Formulación del modelo matemático. Fase3: Evaluación. Para la estimación de los coeficientes del modelo se utilizó la herramienta SPSS. Posterior a la creación del modelo predictivo se llegó a concluir que las variables más significativas que aportan al diagnóstico de la deserción son estado civil, edad, género Nota2s y Nota1s, además se evidencia que los estudiantes tienen mayor riesgo de deserción si están casados y menor riesgo si están solteros o divorciados, finalmente se concluye, que el género es un factor que influye directamente en la deserción, los estudiantes masculinos son más propensos a desertar que los femeninos. Palabras Clave: regresión logística, modelo predictivo, deserción.

Derivation and Clinical Utility of Safety Targets for Linezolid-Related Adverse Events in Drug-Resistant Tuberculosis Treatment.

Long-term usage of linezolid can result in adverse events such as peripheral neuropathy, anemia and thrombocytopenia. Therapeutic drug monitoring data from 75 drug-resistant tuberculosis patients treated with linezolid were analyzed using a time-to-event (TTE) approach for peripheral neuropathy and anemia and indirect response modelling for thrombocytopenia. Different time-varying linezolid pharmacokinetic exposure indices (AUC0-24h,ss, Cav, Cmax and Cmin) and patient characteristics were investigated as risk factors. A treatment duration shorter than 3 months was considered dropout and was modelled using a TTE approach. An exposure-response relationship between linezolid Cmin and both peripheral neuropathy and anemia was found. The exposure index which best described the development of thrombocytopenia was AUC0-24h. The final TTE dropout model indicated an association between linezolid Cmin and dropout. New safety targets for each adverse event were proposed which can be used for individualized linezolid dosing. According to the model predictions at 6 months of treatment, a Cmin of 0.11 mg/L and 1.4 mg/L should not be exceeded to keep the cumulative probability to develop anemia and peripheral neuropathy below 20%. The AUC0-24h should be below 111 h·mg/L or 270 h·mg/L to prevent thrombocytopenia and severe thrombocytopenia, respectively. A clinical utility assessment showed that the currently recommended dose of 600 mg once daily is safer compared to a 300 mg BID dosing strategy considering all four safety endpoints.

Robust Packetized MPC for Networked Systems Subject to Packet Dropouts and Input Saturation With Quantized Feedback.

This article develops a robust packetized predictive control framework to deal with the quantized-feedback control problem of networked systems subject to Markovian packet dropouts and input saturation. In the proposed framework, the Markov chain model of packet dropout is established from the link of the controller to the actuator. To deal with the quantized measurements, a robust packetized predictive control method is presented with a quantized-feedback law. The problem of unreliable transmission is addressed by proposing a packet dropout compensation strategy with a forgetting factor. An augmented Markovian jump system model is established to take the packet dropouts into account. The synthesis of packetized predictive control is then developed by minimizing a worst case cost function with respect to the model uncertainties. The recursive feasibility of the proposed controller design problem and the mean-square stability of the closed-loop systems are proved, respectively. The proposed packetized predictive control method is demonstrated by simulating a four-tank process system.

Approaches on improving privacy and communication efficiency of FedFTG

The FedFTG plug-in can effectively solve the problem of knowledge forgetting caused by the server-side direct aggregation model in Federated Learning. But FedFTG runs the risk of compromising customer privacy, as well as additional transmission costs. Therefore, this paper introduces methods to enhance the privacy and communication efficiency of FedFTG, they are: Mixing Neural Network Layers method which can avoid various kinds of inference attack, Practical Secure Aggregation strategy which uses cryptography to encrypt transmitted data; The Federated Dropout model which focuses on reducing the downward communication pressure, and the Deep Gradient Compression method that can substantially compress the gradient. Experimental results show that, MixNN can ensure the privacy protection without affecting the accuracy of the model; Practical Secure Aggregation saves the communication cost when dealing with large data vector size while protecting the privacy; Federated Dropout reduces communication consumption by up to 28; DGC can compress the gradient by 600 while maintaining the same accuracy. Therefore, if these methods are used in FedFTG, its privacy and communication efficiency will be greatly improved, which will make distributed training more secure and convenient for users, and also make it easier to realize joint learning training on mobile devices.

Lessons learned from the student dropout patterns on COVID‐19 pandemic: An analysis supported by machine learning

AbstractDuring the COVID‐19 pandemic, the challenges associated with the transition from face‐to‐face to emergency remote education increased concerns about student dropout. Aligned with this concern, this study investigates the impact of the pandemic on the dropout patterns of 3371 undergraduate students from a Brazilian institution. Using data mining and machine learning techniques, we developed predictive dropout models based on student data preceding and succeeding the onset of the pandemic. Through the interpretation and comparison of these models and with the support of statistical and graphical analyses, we identify that the patterns persistently indicate that young students in their initial semesters, characterized by lower income, academic performance, and interaction, remain most susceptible to dropping out. Despite the pandemic leading to an enhanced predictive capability of data regarding student interaction within the virtual learning environment, our analysis revealed a lack of significant variation in dropout patterns. Institutionally, this indicates that a considerable number of dropouts likely encountered challenges in adapting to higher education, both before and throughout the pandemic. Practitioner notesWhat is already known about this topic The challenges posed by emergency remote learning, implemented during the COVID‐19 pandemic, may exacerbate the dropout problem and change the patterns involved in this phenomenon. Despite being widely used to identify dropout profiles and/or predict at‐risk students, data mining and machine learning techniques have been little explored in the investigation of changes associated with the pandemic context. What this paper adds We employ data mining and machine learning techniques to construct predictive and interpretable dropout models for the pre‐ and during‐pandemic contexts of a Brazilian institution. Comparing these models, we investigate the impacts of the pandemic on dropout patterns. The pandemic and the shift to emergency remote learning have an enhanced predictive capability of data regarding student interaction within the virtual learning environment. Throughout the pandemic, there was limited variation observed in dropout patterns, consistently highlighting young students in their initial semesters with lower income, academic performance and levels of interaction. Implications for practice and/or policy This study urges the inclusion of interactional student data in future dropout prediction research, capitalizing on the enhanced predictive power attained through the widespread adoption of virtual learning environments. Institutionally, the dropout patterns from before and during the pandemic suggest that students may be facing difficulties in adapting to higher education. In addition to the need to intensify preventive actions, this work indicates the need to conduct a study specifically targeting first‐semester students to understand their needs better and redesign preventive policies.

A Real-Time Predictive Model for Identifying Course Dropout in Online Higher Education

Course dropout is a concern in online higher education, mainly in first-year courses when different factors negatively influence the learners' engagement leading to an unsuccessful outcome or even dropping out from the university. The early identification of such potential at-risk learners is the key to intervening and trying to help them before they decide to drop out. This paper focuses on this challenging problem by providing a predictive dropout model with distinctive characteristics from previous approaches. First, the identification is in real time by providing a daily dropout prediction. Second, a temporal window of variable size is defined to evaluate the likelihood of being a dropout learner at the activity level. Such contributions will serve as a basis for designing and applying intervention mechanisms to reverse the course dropout at-risk situation. The predictive model and the temporal window have been evaluated on data from an authentic online learning setting in two first-year undergraduate courses. We show the accuracy of correctly identifying at-risk learners within activities and the model performance to detect actual course dropout learners.

Assessment of ibrutinib scheduling on leukocyte, lymph node size and blood pressure dynamics in chronic lymphocytic leukemia through pharmacokinetic-pharmacodynamic models.

Ibrutinib is a Bruton tyrosine kinase (Btk) inhibitor for treating chronic lymphocytic leukemia (CLL). It has also been associated with hypertension. The optimal dosing schedule for mitigating this adverse effect is currently under discussion. A quantification of relationships between systemic ibrutinib exposure and efficacy (i.e., leukocyte count and sum of the product of perpendicular diameters [SPD] of lymph nodes) and hypertension toxicity (i.e., blood pressure), and their association with overall survival is needed. Here, we present a semi-mechanistic pharmacokinetic-pharmacodynamic modeling framework to characterize such relationships and facilitate dose optimization. Data from a phase Ib/II study were used, including ibrutinib plasma concentrations to derive daily 0-24-h area under the concentration-time curve, leukocyte count, SPD, survival, and blood pressure measurements. A nonlinear mixed effects modeling approach was applied, considering ibrutinib's pharmacological action and CLL cell dynamics. The final framework included (i) an integrated model for SPD and leukocytes consisting of four CLL cell subpopulations with ibrutinib inhibiting phosphorylated Btk production, (ii) a turnover model in which ibrutinib stimulates an increase in blood pressure, and (iii) a competing risk model for dropout and death. Simulations predicted that the approved dosing schedule had a slightly higher efficacy (24-month, progression-free survival [PFS] 98%) than de-escalation schedules (24-month, average PFS ≈ 97%); the latter had, on average, ≈20% lower proportions of patients with hypertension. The developed modeling framework offers an improved understanding of the relationships among ibrutinib exposure, efficacy and toxicity biomarkers. This framework can serve as a platform to assess dosing schedules in a biologically plausible manner.

Observer‐based sampled‐data control for networked systems with consecutive packet dropouts

AbstractThis paper investigates the observer‐based sampled‐data control problem for networked systems with consecutive packet dropouts. A new consecutive packet dropout model is established to describe the phenomenon of packet dropout in both the sensor‐controller (S‐C) and controller‐actuator (C‐A) channels. First, the considered system with observer‐based control scheme is studied in the discrete‐time domain by using equivalent discretization transformation. In order to simplify subsequent analysis, matrix exponential computation is employed to transform the discrete‐time system into a more tractable form. Then, the proposed new consecutive packet dropout model is taken into consideration to establish the stochastic mean square exponential stability condition. Furthermore, a novel two‐step synthesis approach is used to design the observer‐based controller. Finally, the effectiveness of the proposed observer‐based sampled‐data control approach is verified using a numerical example. In addition, the advantages of the proposed approach are validated through comparisons.

Classification model for student dropouts using machine learning: A case study

Information and communication technologies have been fulfilling a highly relevant role in the different fields of knowledge, addressing problems in various disciplines; there is an increased capacity to identify patterns and anomalies in an organization's data using data mining; In this context, the study aimed to develop a classification model for student dropout, applying machine learning with the autoML method of the H2O.ai framework; the dimensionality of the socioeconomic and academic characteristics has been taken into account, with the purpose that the directors make reasonable decisions to counteract the abandonment of the students in the study programs. The methodology used was of a technological type, purposeful level, incremental innovation, temporal scope, and synchronous; data collection was prospective. For this, a 20-item questionnaire was applied to 237 students enrolled in the master's degree programs in the education of the Graduate School. The research resulted in a supervised machine learning model, Gradient Reinforcement Machine (GBM), to classify student dropout, thus identifying the main associated factors that influence dropout, obtaining a Gini coefficient of 92.20%, AUC of 96.10% and a LogLoss of 24.24% representing a model with efficient performance.

#4049 NEFECON® TREATMENT LIKELY MODULATES DOWNSTREAM PATHWAYS OF KIDNEY INFLAMMATION AND FIBROSIS IN IGA NEPHROPATHY

Abstract Background and Aims The lifetime risk of kidney failure in patients with IgA nephropathy (IgAN) is substantial. Since the original description of the disease, over 50 years ago, there has been little progress in how we manage this important cause of kidney failure in young adults. KDIGO 2021 Clinical Practice Guidelines recommend goal-directed supportive care as the treatment with the strongest evidence base for the management of all patients with IgAN. The NEFIGAN trial (NCT01738035) tested the safety and efficacy of a novel targeted-release formulation of budesonide (Nefecon®), designed to deliver budesonide to the gut-associated lymphoid tissue-rich distal ileum in patients with IgAN in addition to optimised supportive care. The trial comprised a 6-month run-in, 9-month treatment, and a 3-month follow-up phase. Forty-eight patients received Nefecon® 16 mg/day, 51 patients received Nefecon® 8 mg/day, and 50 patients received placebo. The headline result of the study was that Nefecon® 16 mg/day, added to optimised RAS blockade, reduced proteinuria and stabilised eGFR in patients with IgAN. These findings have now been replicated in the NefIgArd study, which reported in 2021 and provided the basis for the recent FDA and EMA approval of Nefecon® as a treatment for patients with IgAN at high risk of progressive disease. In this study, we determined the composition of urinary proteins from patients treated with placebo and 16 mg of Nefecon® in the NEFIGAN trial using liquid chromatography with tandem mass spectrometry (LC-MS/MS). Method Urine samples from 18 patients from each of the placebo and 16-mg arms of the NEFIGAN trial collected at the start of treatment (SOT) and end of treatment (EOT) were analysed. Patients were only included if they had received at least 8 months of treatment and their urine sample was taken up to 2 days after the completion of tapering. Urine samples were treated to remove abundant albumin and immunoglobulins. The remaining proteins were desalted and then precipitated with 5% trichloroacetic acid and washed twice with ice-cold acetone. The air-dried pellets were dissolved in 45 µL of 100 mM triethylammonium bicarbonate buffer, reduced with 2 mM TCEP (tris(2-carboxyethyl)phosphine), and alkylated with 15 mM chloroacetamide. 500 ng of Sequencing Grade Trypsin was added for enzymatic digestion, carried out overnight at 37°C. The samples were dried to completeness and re-solubilised in 10 µL of MS sample buffer (3% acetonitrile, 0.1% formic acid). LC-MS/MS analysis was performed on an Q Exactive mass spectrometer equipped with a Digital PicoView source and coupled to a nanoAcquity UPLC. For protein identification and quantification, raw data were processed with FragPipe (V16) having at least two peptides per protein. Protein intensities were reported in the combined_protein.txt file generated by the FragPipe: These were Log_2-transformed and internally normalised against a group of peptides found in all samples, The normalised protein abundance at SOT was subtracted from the abundance at EOT for each patient, A probabilistic dropout model was fitted to the data to estimate fold changes between the treatment and placebo groups at EOT, The proteins were ranked using the t-statistic and a gene-set enrichment analysis was performed to determine the gene sets significantly affected by the treatment compared with the placebo group. Results Gene-ontology analysis revealed that treatment with 16 mg of Nefecon® led to a significant enrichment of multiple pathways (n = 57) involved in several processes previously shown to be important in the pathogenesis of kidney injury in IgAN. These included epigenetic pathways (n = 13), microvesicle formation (n = 3), kidney remodelling (n = 9), and regulation of local immune and inflammatory responses (n = 32). Conclusion We have previously shown that 16 mg of Nefecon® for 9 months positively impacts on multiple biomarkers of IgAN severity. These urine proteomic data support the positive impact of Nefecon® on downstream pro-inflammatory and profibrotic pathways within the kidneys. These data will be validated in the biomarker analyses currently underway as part of the NefIgArd study.

Bayesian pattern-mixture models for dropout and intermittently missing data in longitudinal data analysis.

Valid inference can be drawn from a random-effects model for repeated measures that are incomplete if whether the data are missing or not, known as missingness, is independent of the missing data. Data that are missing completely at random or missing at random are two data types for which missingness is ignorable. Given ignorable missingness, statistical inference can proceed without addressing the source of the missing data in the model. If the missingness is not ignorable, however, recommendations are to fit multiple models that represent different plausible explanations of the missing data. A popular choice in methods for evaluating nonignorable missingness is a random-effects pattern-mixture model that extends a random-effects model to include one or more between-subjects variables that represent fixed patterns of missing data. Generally straightforward to implement, a fixed pattern-mixture model is one among several options for assessing nonignorable missingness, and when it is used as the sole model to address nonignorable missingness, understanding the impact of missingness is greatly limited. This paper considers alternatives to a fixed pattern-mixture model for nonignorable missingness that are generally straightforward to fit and encourage researchers to give greater attention to the possible impact of nonignorable missingness in longitudinal data analysis. Patterns of both monotonic and non-monotonic (intermittently) missing data are addressed. Empirical longitudinal psychiatric data are used to illustrate the models. A small Monte Carlo data simulation study is presented to help illustrate the utility of such methods.