Parametric Model Research Articles

Brachial-Ankle Pulse Wave Velocity as a Predictor of Diabetes Development: Elevated Risk Within Normal Range Values in a Low-Risk Population.

Recent studies have suggested that increased brachial-ankle pulse wave velocity (baPWV) is a risk factor for diabetes. Exploring its relationship with insulin resistance is of interest, necessitating further studies across different sexes and age groups. This cohort study involved 119 170 Korean adults with an average age of 39.8 years, none of whom had diabetes at baseline. As part of a health screening, baPWV measurements were taken. Over a median follow-up period of 5.6 years, fasting blood glucose, glycated hemoglobin, insulin levels, and questionnaire responses were collected. The risk of developing diabetes was evaluated using a flexible parametric proportional hazards model with data stratified by sex and age group (<40 versus ≥40 years). During the follow-up period, diabetes was diagnosed in 5966 participants (5.0%). A fully adjusted model found that the hazard ratios for diabetes onset associated with baPWV quartiles Q2 (1171.0-1270.5 cm/s), Q3 (1271.0-1376.0 cm/s), and Q4 (≥1376.5 cm/s) compared with Q1 (<1171.0 cm/s) were 1.06 (95% CI, 0.96-1.17), 1.25 (1.14-1.38), and 1.48 (1.34-1.62), respectively (P for trend <0.001). A significant sex-based interaction was noted in this association, with women showing a higher risk of diabetes development. Furthermore, higher baPWV quartiles were associated with an increased risk of developing insulin resistance, defined as the homeostatic model assessment of insulin resistance. These findings highlight the importance of arterial stiffness, as measured by elevated baPWV, in the development of diabetes and insulin resistance. Notably, this study highlighted a strong association, particularly among women.

Comparison of two parametric models of a perimeter wall fragment with active thermal protection

Comparison of two parametric models of a perimeter wall fragment with active thermal protection

Bayesian Regression Analysis for Dependent Data with an Elliptical Shape

This paper proposes a parametric hierarchical model for functional data with an elliptical shape, using a Gaussian process prior to capturing the data dependencies that reflect systematic errors while modeling the underlying curved shape through a von Mises–Fisher distribution. The model definition, Bayesian inference, and MCMC algorithm are discussed. The effectiveness of the model is demonstrated through the reconstruction of curved trajectories using both simulated and real-world examples. The discussion in this paper focuses on two-dimensional problems, but the framework can be extended to higher-dimensional spaces, making it adaptable to a wide range of applications.

A Machine Learning-Based Parameterized Tropical Cyclone Precipitation Model

AbstractCurrent simulation models considerably underestimate local-scale, short-duration extreme precipitation induced by tropical cyclones (TCs). This problem needs to be addressed to establish active response policies for TC-induced disasters. Taking Shanghai, a coastal megacity, as a study area and based on the observations from 192 meteorological stations in the city during 2005–2018, this study optimized the parameterized Tropical Cyclone Precipitation Model (TCPM) initially designed for TCs at the national scale (China) to the local or regional scales by using machine learning (ML) methods, including the random forest (RF), extreme gradient boosting (XGBoost), and ensemble learning (EL) algorithms. The TCPM-ML was applied for multiple temporal scale hazard assessment. The results show that: (1) The TCPM-ML not only improved TCPM performance for simulating hourly extreme precipitations, but also preserved the physical meaning of the results, contrary to ML methods; (2) Machine learning algorithms enhanced the TCPM ability to reproduce observations, although the hourly extreme precipitations remained slightly underestimated; (3) Best performance was obtained with the XGBoost or EL algorithms. Combining the strengths of both XGBoost and RF, the EL algorithm yielded the best overall performance. This study provides essential model support for TC disaster risk assessment and response at the local and regional scales in China.

Assessing the Impact of Geopolitical Risk on Longevity Bond Pricing: Insights from Bayesian Multivariate Regression

AbstractThis paper investigates the multivariate pricing of coupon longevity bonds (CLBs) using the Fama–French–Lee–Carter (FF–LC) five-vector model in the framework of Bayesian integrated nested Laplace approximation (INLA) in the presence of geopolitical risk (GPR). The variance-covariance and correlation matrices are utilized to capture the interdependence between factors. We prove the generalization of multivariate Bayesian INLA with the basic probability assignment which is utilized as a posterior uncertainty belief associated with the GPR uncertainty category (a rich representation of GPR uncertainty) that is an element of the frame of discernment in the CLB posterior estimation. INLA Bayesian principal component analysis (INLA-BPCA) is applied to the model prediction parameters generating a multivariate normally distributed posterior. The deviance information criterion (DIC) assesses optimal factor selection. The results show that the BPCA posterior gains a feature that allows for a balance between the goodness-of-fit and complexity in hierarchical model selection by incorporating the retained principal components (or the effective number of parameters) from the DIC formula. Furthermore, it is also evident in our results, that the DIC outperforms the Bayesian information criterion (BIC), and Watanabe–Akaike information criterion (WAIC). The DIC is more suitable for Bayesian-based parametric models with high complexity. Lastly, the INLA-BPCA-DIC is applied to select the best longevity factors that yield a low longevity price of risk for insurers and practitioners, to attenuate the risks associated with investing in CLBs in the presence of geopolitical uncertainty shocks.

ІНТЕГРОВАНЕ ПРОЄКТУВАННЯ ХВОСТОВОЇ ЧАСТИНИ ФЮЗЕЛЯЖУ ЛІТАКІВ ТРАНСПОРТНОЇ КАТЕГОРІЇ

For the first time, when designing transport category aircraft with a tail cargo hatch, a method of integrated design and construction of the fuselage tail section has been developed and presented, which, using mathematically described parameters, allows the creation of a particular master geometry of the fuselage tail section for a specifically selected cargo door scheme. The method has been improved to allow the design of transport aircraft not only at the stage of creating a fundamentally new aircraft concept, but also as a means of modifying an existing passenger aircraft type through deep modernization. A systematization and classification of existing types of cargo doors of the transport category aircrafts according to their design features has been developed. A separate group of tail cargo doors has been identified. Five typical schemes of cargo hatches in the tail section of the fuselage have been formed. A universal parametric method for specifying the master geometry of the fuselage tail section has been created, which allows the implementation of any of the above schemes of cargo doors in the fuselage tail section, including a unique one, implemented only on AN aircraft with a rolling ramp. The relationship between the parameters of the cargo door and the transportation characteristics of the aircraft, which depend on the range and types of cargo to be transported, methods of loading and unloading, and airdropung, is described. The methods of three-dimensional parametric modeling of both the tail sections of the fuselage and transport aircraft as a whole were further developed when creating a master geometry, a space distribution model, analytical standards of airframe structural elements, and a model for the complete determination of the geometric parameters of the aircraft using CAD/CAM/CAE/PLM technologies. The presented results have been implemented in the practice of designing transport category aircraft at ANTONOV Company, as well as at the National Aerospace University named after M.E. Zhukovsky “Kharkiv Aviation Institute” in the educational process of training aviation specialists.

Frequency-adjusted borders ordinal forest: A novel tree ensemble method for ordinal prediction.

Ordinal responses commonly occur in psychology, e.g., through school grades or rating scales. Where traditionally parametric statistical models like the proportional odds model have been used, machine learning (ML) methods such as random forest (RF) are increasingly employed for ordinal prediction. With new developments in assessment and new data sources yielding increasing quantities of data in the psychological sciences, such ML approaches promise high predictive performance. As RF does not inherently account for ordinality, several extensions have been proposed. A promising approach lies in assigning optimized numeric scores to the ordinal response categories and using regression RF. However, these optimization procedures are computationally expensive and have been shown to yield only situational benefit. In this work, I propose Frequency-Adjusted Borders Ordinal Forest (fabOF), a novel tree ensemble method for ordinal prediction forgoing extensive optimization while offering improved predictive performance in simulation and an illustrative example of student performance. To aid interpretation, I additionally introduce a permutation variable importance measure for fabOF tailored towards ordinal prediction. When applied to the illustrative example, an interest in higher education, mother's education, and study time are identified as important predictors of student performance. The presented methodology is made available through an accompanying R package.

Development of a Generalized Partition Curve Model for Hydrocyclones

ABSTRACT Partition curves associated with hydrocyclone classification processes have been known to deviate from the typical S-shape by exhibiting asymmetric and non-monotonic behaviors, making their mathematical modeling challenging. In addition to the well-documented phenomenon of fish-hooks at the finer end, the partition curves can also sometimes exhibit fat-tailed behavior at the coarser end of the size spectrum, which has not been extensively highlighted in the literature. This study aimed to develop a parametric model for the partition curves which can systematically and effectively accommodate both these irregularities at the coarser and finer ends. Instead of adopting a piece-wise modeling approach for the task, a continuous model was successfully developed in the form of the addition of three component functions, each affecting different regions and features of the partition curve with a total of 7 fitting parameters. Two of the component functions were modified versions of the cumulative density functions of the Weibull distribution while the third was the modified solution of a single hyperbolic branch having a horizontal and a slant asymptote to accommodate the observed fat-tailed behavior. The model exhibited remarkable fitting performance for both experimentally generated and literature data. An alternative method of defining the separation cut size has also been proposed in this study to overcome the limitations of the classical concept of the d50. It was also shown how the developed model can be utilized for quantitative analysis of the critical aspects of the partition curve such as the cut size, bypass, and the dip point.

Sparse Uniform Traversal of Model Parameter Space for Estimating the Nonlinear Displacement Response of Instrumented Buildings to Earthquakes

ABSTRACTThe displacement response of a building structure to earthquake excitations is crucial for assessing its seismic damage, which is usually accompanied by structural nonlinear behavior. This study proposes an efficient method of quickly estimating the nonlinear displacement responses of seismically damaged buildings. By designing a set of sparsely and uniformly distributed samples in the multi‐dimensional parameter space, this method traverses all these samples to find the best set of parameters for a parametric numerical model of the building that minimizes the error between the simulated and the measured responses. Compared to existing model‐driven methods, the proposed method can efficiently match the high‐dimensional parameters of the assumed parametric model without tedious and less robust iterative optimization, even if the instrumented building sustains severe seismic damage and deviates significantly from its initial state. The shaking table test data of a full‐scale four‐story reinforced concrete moment‐resisting frame structure is used to justify the advantage of the method over the state‐of‐the‐art optimization‐based model‐driven method. The proposed method successfully estimated the structural responses of all the stories of the building with an average error of 4.6% for the maximum inter‐story drift across the five earthquake loading runs. It took only approximately 19 min to complete the calculation on a personal computer, which could be greatly accelerated given more computation cores because the traversal is inherently friendly to parallel computation.

Cholesterol, Triglyceride, and Glucose Levels Across Birth Cohorts in the US

Population mean levels of total cholesterol and fasting triglycerides have decreased substantially in the US in recent decades, but improvements in cardiometabolic health may have slowed among younger cohorts. Conversely, prevalence of diabetes (types 1 and 2) and obesity has increased, especially among adults younger than 65 years. It is unclear how trends in cholesterol, triglyceride, and glucose levels have changed across different birth cohorts and whether adverse trends in obesity are associated with these patterns. To quantify national trends in total cholesterol, fasting triglyceride, and fasting glucose levels among cohorts born between 1920 and 1999 and examine the potential association of these patterns with body mass index (BMI). This serial cross-sectional study used National Health and Nutrition Examination Survey (NHANES) data from the 1999-2000 to 2017-2020 cycles. Data were analyzed between November 1, 2023, and July 31, 2024. Participants included nonpregnant and noninstitutionalized US adults 18 years or older, born between 1920 and 1999, who had data collected from 1999 to 2020. Eight 10-year birth cohorts (from 1920 to 1999). Total cholesterol, fasting triglyceride, and fasting glucose levels and BMI. Quantile regression models reported average marginal effects to quantify mean change in cardiometabolic outcome measures per decade of birth years. Parametric regression models estimated the association of birth cohort with outcomes, assessing BMI as the mediator. Of 52 006 participants weighted to represent 264 664 915 US adults, weighted median age was 46 (IQR, 33-60) years and 50.6% were women. For the 50th percentile of measures, mean difference per 1-decade younger birth cohort was -7.1 (95% CI, -8.2 to -6.1) mg/dL for total cholesterol level, -13.1 (95% CI, -15.1 to -11.1) mg/dL for fasting triglyceride level, and 2.7 (95% CI, 2.3-3.1) mg/dL for fasting glucose level. BMI appeared to attenuate the associations between birth cohort and lipid levels and enhanced the associations between birth cohort and fasting glucose levels. However, up to 80% of the associations between birth cohorts and cardiometabolic outcomes were not mediated through BMI. In this cross-sectional study of 52 006 participants representing 264 664 915 US adults, population-level improvements in total cholesterol and triglyceride levels decelerated and adverse trends in glucose levels accelerated in more recent birth cohorts, which was partially mediated by concurrent increases in BMI. Public health initiatives that target antecedent health behaviors are needed to improve cardiometabolic health across generations.

Comparison of tracer kinetic models in differentiating malignant from normal prostate tissue using dynamic contrast-enhanced MRI

PurposeThe aim of this study was to evaluate the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived kinetic parameters with high spatiotemporal resolution in discriminating malignant from normal prostate tissues.MethodsFifty patients with suspicious of malignant diseases in prostate were included in this study. Regions of interest (ROI) were manually delineated by experienced radiologists. Voxel-wise kinetic parameters were produced with the following tracer kinetic models (TKMs): Tofts model, extended Tofts model (ETM), Brix’s conventional two-compartment model (Brix), adiabatic tissue homogeneity model (ATH), and distributed parameter model (DP). The initial area under the signal-time curve (IAUC) with an uptake integral approach was also included. Mann–Whitney U test and receiver operating characteristic (ROC) curves were used to evaluate the capability of distinguishing tumor lesions from normal tissues. A p-value of 0.05 or less is considered statistically significant. ROI based parameters correlation analysis between DP and ETM were performed.Results624 lesions and 269 normal tissue ROIs were obtained. Thirty parameters were derived from the six kinetic models. Except for PS from Brix, statistically significant differences between lesions and normal tissues (P&amp;lt;0.05) were observed in other parameters.Ve from DP, ATH and Brix and PS from ATH have AUC values less than 0.6 in the ROC analysis. MTT, Vp and PS from DP, Ktrans from ETM and Tofts, E and PS from ATH, IAUC parameters and F from Brix have AUC values larger than 0.8. Ve and Vp from DP and ETM are correlated (r&amp;gt; 0.65). The correlation coefficient between Ktrans from ETM and PS from DP is 0.751.ConclusionMTT, Vp and PS from DP, Ktrans from ETM and Tofts, E and PS from ATH, F from Brix and IAUC parameters can be used to differentiate malignant lesions from normal tissues in the prostate.

Semi-parametric financial risk forecasting incorporating multiple realized measures

A semi-parametric joint Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting framework employing multiple realized measures is developed. The proposed framework extends the realized exponential GARCH model to be semi-parametrically estimated, via a joint loss function, whilst extending existing quantile time series models to incorporate multiple realized measures. A quasi-likelihood is built, employing the asymmetric Laplace distribution that is directly linked to a joint loss function, which enables Bayesian inference for the proposed model. An adaptive Markov Chain Monte Carlo method is used for the model estimation. The empirical section evaluates the performance of the proposed framework with six stock markets from January 2000 to June 2022, covering the period of COVID-19. Three realized measures, including 5-minute realized variance, bi-power variation, and realized kernel, are incorporated and evaluated in the proposed framework. One-step-ahead 1% and 2.5% VaR and ES forecasting results of the proposed model are compared to a range of parametric and semi-parametric models, lending support to the effectiveness of the proposed framework.

Multi-direction vibration isolation via transverse isotropy control of metal rubber

Abstract Metal rubber (MR) is widely used for vibration isolation due to its high damping and designable stiffness, but the isolation efficiency is relatively low in the non-molding direction due to its transverse isotropy. In this paper, the transverse isotropy of MR is controlled by design of manufacturing parameters (wire diameter, spiral diameter and compression ratio), material parameter (relative density) and geometric structure, to improve the isolation efficiency in the non-molding direction. Firstly, the helix unit was adopted to establish the relation between the transverse isotropy of MR material and manufacturing parameters, and manufacturing parameters were designed to control the transverse isotropy of MR material. Next, based on parametric modeling and genetic algorithm, material parameters and geometric structure were optimized to control the transverse isotropy of MR isolator. Finally, the transverse isotropy of the designed MR isolator was tested by quasi-static experiments, and multi-direction vibration isolation performance was verified by random vibration experiments. It is demonstrated that the transverse isotropy is controlled efficiently, and the vibration isolation efficiencies of the proposed MR isolator are all up to 85% under 15-2000Hz broadband random vibration in X, Y and Z directions.&amp;#xD;

Evaluating the Performances of Robust Logistic Regression Models in the Presence of Outliers

Logistic regression models are widely used in the field of medical and behavioral sciences. These models are used to describe the effect of explanatory variables on a binary response variable. The maximum likelihood estimator (MLE) is commonly used to estimate the parameters of logistic regression models due to its efficiency under a parametric model. However, evidence has shown that the MLE is highly sensitive to outlying observations which might affect the parameter estimates. Robust methods are put forward to rectify this problem. This paper investigated the robustness of GM-Mallows and GM-Schweppes as an alternative to the commonly used ordinary logistic regression model in the presence of outliers. The study used a Monte Carlos Simulation, by generating a logistic regression model with Five independent normally distributed covariates. 5% of outliers was contaminated to the data on sample sizes 50, 200 and 400 respectively. The results showed that the GM-Mallows estimator perform best across all metrics having the lowest AIC, BIC, MSE and MAE except for n=50. This suggests that the robust methods, especially GM-Mallows, provide more reliable estimates in the presence of outliers. The finding suggests that if there is presence of outliers’ GM-Mallows appears to be the top choice, where the GM-Schweppes offers a middle ground, providing some robustness with perhaps less extreme adjustments. The ordinary logistic regression might be preferred if simplicity and interpretability are prioritized, and there's confidence that outliers are not a significant issue in the data.

Characterization of Particle Fluidization in a New Type of Fluidized Bed Flotation Unit

ABSTRACTUnderstanding the hydrodynamics of the bed layer in a fluidized bed is essential for optimizing and improving fluidized bed flotation units. This study proposes a fluidized bed flotation column that incorporates auxiliary particles into the gas–liquid two‐phase system to address the shortcomings of traditional flotation units. Stainless steel particles (glass beads), tap water, and compressed air were used as the solid, liquid, and gas phases, respectively. Key factors affecting the bed hydrodynamics (such as bed fluctuation, minimum fluidization velocity, bed expansion, and porosity) in the fluidized column include gas and liquid flow rates as well as the initial bed height. Experimental results show that selecting appropriate filling particles can effectively enhance the fluidization performance of the bed, while appropriately increasing the gas flow rate can achieve fluidization at lower liquid velocities, thus reducing energy consumption during the flotation mineralization process. Theoretical analysis combined with extensive data reveals that the expansion characteristics of the fluidized bed and the use of high‐density filling particles can effectively mitigate bed layer fluctuations and stabilize the flow field environment. Additionally, based on a wide range of data, this study employs model factor analysis, dimensionless analysis, and multiple linear regression analysis to propose a standard dimensionless parameter model for the fluidized bed flotation column, which can effectively predict bed layer fluctuations and expansion characteristics.

Generalized Estimating Equations for Survival Data With Dependent Censoring.

Independent censoring is usually assumed in survival data analysis. However, dependent censoring, where the survival time is dependent on the censoring time, is often seen in real data applications. In this project, we model the vector of survival time and censoring time marginally through semiparametric heteroscedastic accelerated failure time models and model their association by the vector of errors in the model. We show that this semiparametric model is identified, and the generalized estimating equation approach is extended to estimate the parameters in this model. It is shown that the estimators of the model parameters are consistent and asymptotically normal. Simulation studies are conducted to compare it with the estimation method under a parametric model. A real dataset from a prostate cancer study is used for illustration of the new proposed method.

Individualized Prediction for Risk of Recurrence in Stage I/II Melanoma Patients With Negative Sentinel Lymph Node.

Despite the favorable prognosis of AJCC stage I/II melanoma patients, up to 20%-30% will develop metastases. Our objective is to predict long-term risk (probability) of recurrence in early-stage melanoma patients. A Risk Score to predict long-term recurrence was developed using Cox regression based on 2668 patients. Five clinicopathological risk factors were included. The association of the Risk Score with the risk of recurrence was evaluated using parametric models (exponential, Weibull, and Gompertz models) and compared to the Cox model using the Akaike information criterion. The discrimination of the model was measured by time-dependent ROC analyses. A calibration curve was used to evaluate the agreement between predicted and observed recurrence probabilities. The bootstrap adjusted C-index was 0.76 (95% CI, 0.74-0.79) overall and 0.87 (0.83-0.90) and 0.82 (0.78-0.85) at one and two years, respectively, and then remained above 0.70 up to 20 years. The Gompertz model for prediction of survival from the Risk Score showed the best performance and displayed good agreement with the Kaplan-Meier curves. The calibration curve of the Gompertz model showed a good agreement between predicted and observed 2-, 5-, and 10-year risk of recurrence. Population-level analysis demonstrated a significant association of Risk Score with risk of recurrence, with 10-year risks of recurrence of 4.5%, 13.0%, and 33.7% in the first, second, and third tertiles, respectively. We developed a Risk Score to predict long-term risk of recurrence for early-stage melanoma patients. A Gompertz survival model fit to the Risk Score allows for individualized prediction of time-dependent recurrence risk.

Sequential Manipulation Against Rank Aggregation: Theory and Algorithm.

Rank aggregation with pairwise comparisons is widely encountered in sociology, politics, economics, psychology, sports, etc. Given the enormous social impact and the consequent incentives, the potential adversary has a strong motivation to manipulate the ranking list. However, the ideal attack opportunity and the excessive adversarial capability cause the existing methods to be impractical. To fully explore the potential risks, we leverage an online attack on the vulnerable data collection process. Since it is independent of rank aggregation and lacks effective protection mechanisms, we disrupt the data collection process by fabricating pairwise comparisons without knowledge of the future data or the true distribution. From the game-theoretic perspective, the confrontation scenario between the online manipulator and the ranker who takes control of the original data source is formulated as a distributionally robust game that deals with the uncertainty of knowledge. Then we demonstrate that the equilibrium in the above game is potentially favorable to the adversary by analyzing the vulnerability of the sampling algorithms such as Bernoulli and reservoir methods. According to the above theoretical analysis, different sequential manipulation policies are proposed under a Bayesian decision framework and a large class of parametric pairwise comparison models. For attackers with complete knowledge, we establish the asymptotic optimality of the proposed policies. To increase the success rate of the sequential manipulation with incomplete knowledge, a distributionally robust estimator, which replaces the maximum likelihood estimation in a saddle point problem, provides a conservative data generation solution. Finally, the corroborating empirical evidence shows that the proposed method manipulates the results of rank aggregation methods in a sequential manner.

Model-based dimensional NDE from few X-ray radiographs: Application to the evaluation of wall thickness in metallic turbine blades

The extraction of 3D dimensional measurements based on a limited number of 2D X-ray radiographs of a part would offer a significant speed-up of quality control procedures in industry. However, there are challenges with respect to both measurements and uncertainties. This work addresses these challenges by creating an estimated numerical model of the imaged part on which dimensional measurements can be made. The numerical model is chosen as a parametric deformable model that encodes the expected shape variability of the parts resulting from the manufacturing process. The parameters and uncertainties of the numerical model of the imaged part are estimated by the registration of the computed projections of the model and the observed radiographs without the need of any segmentation. The registration requires the model, the initial parameters, and the observed radiographs. The proposed approach is applied to the inspection of turbine blades manufactured by investment casting, and in particular to the measurement of their wall thickness, which is a critical control. The deformable model consists in partitioning the inner ceramic core into multiple subparts, which may undergo a rigid body motion with respect to the master die. Wall thickness measurements are determined from the estimation of these rigid body motions. To assess the reliability of the proposed procedure, a repeatability study is performed. In addition, wall thickness measurements were compared to corresponding measurements from the surface of the metal boundary obtained by X-ray computed tomography. This surface was determined from a reconstructed tomogram using commercial software. Both analyses show that such measurements are reliable and efficient. Furthermore, residual differences between captured and computed projections reveal localized shape deviations from the CAD model, meaning that despite localized model errors, the approach is operable.

Association between education level and access to disease-modifying treatment in patients with multiple sclerosis in France.

We hypothesized that differences in access to disease-modifying treatments (DMTs) could explain the association between socioeconomic status and disability progression in multiple sclerosis (MS). This study aimed to analyze the association between education level and DMT use in France. All patients from OFSEP network with MS onset over 1996-2014 and aged ⩾ 25 years at onset were included. Three time-to-event outcomes were investigated using flexible parametric survival regression models: time from MS onset to first DMT (any) and to platform therapy, and time from platform therapy to switch to high-efficacy therapy. Overall, 7563 patients were included (mean follow-up 12.6 ± 5.9 years). The percentages of patients aged less than 40 years at MS onset and who initiated treatment before the age of 40 years were significantly higher in the groups with a higher education level. The time-to-event outcomes showed no major difference in DMT practices according to education level, except for women who had a significantly shorter time to DMT initiation in medium to very high education level groups versus low, at 5 years from MS clinical onset. Our results suggest that the association between education level and MS disability progression does not solely reflect different therapeutic practices, particularly in men.