Lifetime Model Research Articles

Odd log-logistic generalised Lindley distribution with properties and applications

In this paper, a new three-parameter lifetime model, called the odd log-logistic generalised Lindley distribution, is introduced. Some structural properties of the new distribution including ordinary and incomplete moments, quantile and generating functions and order statistics are obtained. The new density function can be expressed as a linear mixture of exponetiated Lindley densities. Different methods are discussed to estimate the model parameters and a simulation study is carried out to show the performance of the new distribution. The importance and flexibility of the new model are also illustrated empirically by means of two real data sets. Finally, Bayesian analysis and Gibbs sampling are performed based on the two real data sets.

Damage-Based Lifetime Modeling for Power Electronic Devices

Lifetime modeling is an essential tool for ensuring the reliability of systems. The purpose is to estimate the time before the power electronic device failure so that downtime can be reduced and costly failures can be avoided in industry. This paper will first quantify the cumulative damage in the power cycling test using Junction Temperature Swing and Maximum Junction Temperature, and then formulate the cumulative damage-based lifetime model of power electronic devices. This model assumes that the lifetime is linear to the inverse of the cumulated damage, and shows superior performance in experiments compared with the well-known LESIT model.

Comparison of the Diagnostic Accuracy of Mammogram-based Deep Learning and Traditional Breast Cancer Risk Models in Patients Who Underwent Supplemental Screening with MRI.

Background Access to supplemental screening breast MRI is determined using traditional risk models, which are limited by modest predictive accuracy. Purpose To compare the diagnostic accuracy of a mammogram-based deep learning (DL) risk assessment model to that of traditional breast cancer risk models in patients who underwent supplemental screening with MRI. Materials and Methods This retrospective study included consecutive patients undergoing breast cancer screening MRI from September 2017 to September 2020 at four facilities. Risk was assessed using the Tyrer-Cuzick (TC) and National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT) 5-year and lifetime models as well as a DL 5-year model that generated a risk score based on the most recent screening mammogram. A risk score of 1.67% or higher defined increased risk for traditional 5-year models, a risk score of 20% or higher defined high risk for traditional lifetime models, and absolute scores of 2.3 or higher and 6.6 or higher defined increased and high risk, respectively, for the DL model. Model accuracy metrics including cancer detection rate (CDR) and positive predictive values (PPVs) (PPV of abnormal findings at screening [PPV1], PPV of biopsies recommended [PPV2], and PPV of biopsies performed [PPV3]) were compared using logistic regression models. Results This study included 2168 women who underwent 4247 high-risk screening MRI examinations (median age, 54 years [IQR, 48-60 years]). CDR (per 1000 examinations) was higher in patients at high risk according to the DL model (20.6 [95% CI: 11.8, 35.6]) than according to the TC (6.0 [95% CI: 2.9, 12.3]; P < .01) and BCRAT (6.8 [95% CI: 2.9, 15.8]; P = .04) lifetime models. PPV1, PPV2, and PPV3 were higher in patients identified as high risk by the DL model (PPV1, 14.6%; PPV2, 32.4%; PPV3, 36.4%) than those identified as high risk with the TC (PPV1, 5.0%; PPV2, 12.7%; PPV3, 13.5%; P value range, .02-.03) and BCRAT (PPV1, 5.5%; PPV2, 11.1%; PPV3, 12.5%; P value range, .02-.05) lifetime models. Conclusion Patients identified as high risk by a mammogram-based DL risk assessment model showed higher CDR at breast screening MRI than patients identified as high risk with traditional risk models. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae in this issue.

Study on Lifetime Modeling of IGBT Modules Considering Electric Frequency Influence Mechanism

Affected by the randomness of the wind speed, the output electric frequency (0 20Hz) of the rotor-side converter does not match the thermal time constant (about 100ms) of IGBT modules, which causes the different junction temperature stress. It leads that it is difficult for the traditional lifetime model to reflect the influence of multi-time constant thermal load and result in inaccurate reliability evaluation. Aiming at the characteristics of wind turbine-side converters bearing thermal loads with multiple time constants, this paper studies the influence mechanism of electric frequency on the reliability of IGBT modules through a multi-physics simulation model and power cycling tests, and an improved lifetime model considering the influence of frequency parameters is proposed. Finally, an example analysis of the reliability evaluation of the DFIG rotor side converter is carried out. This paper is intended for the health management of converter systems and is supposed to be in service reliably.

Bayesian and non-Bayesian analysis with MCMC algorithm of stress-strength for a new two parameters lifetime model with applications

A parallel system is one of the special redundant systems that industrial systems frequently use to increase reliability and prevent unexpected failures. In this paper, a new two-parameter model called the Poisson Rayleigh distribution (PRD) is studied. Some of its statistical properties are given. Particularly, we emphasize the study of the stress–strength (SS) reliability parameter, R = p(Y &amp;lt; X), when X and Y have a PRD. Maximum likelihood, maximum product spacing, and Bayesian strategies are utilized to estimate the parameters. Maximum likelihood, maximum product spacing, and Bayesian techniques for R are computed. To assess how each estimation method performs, a simulation study is conducted. In order to demonstrate the adaptability of the suggested model, its goodness of fit for the PRD comparison with other models is demonstrated by application to real datasets. Finally, the SS model for the PRD was applied with two applications of real data depicting the failure times for two types of electrical insulators and pertaining to customer wait times at two banks.

Degradation Diagnostics of Lithium-Ion Batteries Cycled Under Varying Conditions

Lithium-ion (Li-ion) battery cell degradation is a complex process driven primarily by how the cells are used. Stress factors like temperature, charging and discharging rates, and the depth of discharge all influence the rate and type of degradation modes cells experience. Understanding the possible degradation modes and their severity under a given usage profile can help to optimize cell design, manufacturing, and control. In addition, elucidating the relationship between different degradation modes and their effects on cell capacity fade can help improve battery lifetime modeling strategies. In this study, we examine non-intrusive degradation diagnostics based on differential voltage analysis (half-cell curve fitting) and confirm the existence of certain degradation modes and mechanisms by destructively analyzing specific cells from a newly generated battery aging dataset of 245 nickel-manganese-cobalt/graphite (NMC) cells cycled under varying rates and depths of discharge. The results help to establish the link between the various aging stress factors (charging and discharging rates and depth of discharge), the measurable voltage vs. capacity data, and the observed capacity fade trends. We also investigate the impact of dominant degradation modes on battery lifetime and combine the destructive analyses with cycle aging data to understand the relationship between the rate of early-life degradation and total cell lifetime.

Efficient Ultra‐Broadband Phosphors for High-Power Near-infrared LED and night vision imaging system sources

Broadband near-infrared (NIR)-emitting materials have critical applications in biology, medical detection, and NIR night-vision. However, developing NIR phosphors with promising optical properties and excellent thermal stability is a great challenge for research. In this work, a novel NIR phosphor Cr3+ and Yb3+ doped CaSc2O4 (CSO) is an evolving NIR emitting phosphor with decent optical properties, boosted emission intensity, and thermal stability that makes it a promising candidate with potential applications in NIR-LED and infrared night-vision. The implications of the fundamental electronic structure of CSO, including valence-band (VB) and conduction-band (CB) orbital character, are investigated. Under blue light excitation, CSO:Cr3+ is settled with pure broadband NIR emissions (max = 920 nm, FWHM (full width at half maximum) = ∼310 nm) and good thermal stability. The occupation of two selectable lattice sites of Cr3+ in CSO is discussed in detail. Furthermore, emission enhancements of samples are done by Cr3+ and Yb3+ codoping, effectively broadening FWHM to 351 nm and reducing thermal quenching. The energy transfer (ET) routes in CSO:Cr3+,Yb3+ are investigated based on PL lifetime and comprehensive kinetic ET model, revealing that the PL enhancements by Cr3+ and Yb3+ codoping mainly originate from the ET from Cr3+ to Yb3+ emitters with more efficient and thermally stable. Finally, an InGaN-LED device encapsulated using CSO:0.01Cr3+ and CSO:0.01Cr3+,0.01 Yb3+ phosphor was fabricated. The fabricated NIR pc-LEDs are excellent candidates for NIR light sources in biological imaging and NIR night vision.

Lifetime prediction and reliability analysis for aluminum electrolytic capacitors in EV charging module based on mission profiles

The charging modules of Electric vehicles (EVs) always run in a complex and variable state. As the weakness in the reliable operation of charging modules, the accurate lifetime prediction of aluminum electrolytic capacitors (Al-caps) is important for the later maintenance and reliability design. The hotspot temperature calculation method and lifetime model limit the accuracy of aluminum electrolytic capacitors lifetime prediction methods, which cannot meet the increasing requirements for reliability. In order to solve the problems above, this paper has proposed a hotspot temperature calculation method based on the ripple current with frequency characteristics and the cooling conditions on the heat generation and thermal conductivity of the capacitors. Furthermore, the lifetime model under reference voltage has been constructed with 3D surface fitting toolbox, which describes the trends of capacitor lifetime with ambient temperature and hotspot temperature under the constant voltage condition. Considering the variation of voltage, the multiple lifetime model of capacitor is established with a voltage correction coefficient. With the proposed method, it can be realized about the real-time lifetime prediction of capacitors under multiple operating profiles such as ripple current, thermal dissipation conditions, ambient temperature and operating voltage. Finally, the effectiveness of the proposed method is verified with the annual profiles of a 30 kW EV charging module.

Statistical analysis of the Gompertz-Makeham model using adaptive progressively hybrid Type-II censoring and its applications in various sciences

Gompertz-Makeham (GomMak) lifetime model has been popularly utilized in explaining human mortality, actuarial tables, growth models, and others. This study focuses on the inference problem of the model parameters and some life parameters, such as the survival (or reliability) and failure rate functions of the GomMak distribution, in the presence of a sample produced from an adaptive progressively hybrid Type-II strategy. To acquire estimates of the unknown objectives, the maximum likelihood and Bayesian estimation approaches are considered. Asymptotic confidence intervals for the same unknown parameters are also estimated based on the observed Fisher information. According to the assumption of gamma intensity introductions, under squared error loss, Bayes’ estimates of various parameters cannot be explicitly expressed. To handle this issue, the Metropolis-Hastings technique is used, in turn, to evaluate the complex Bayes estimates and produce their highest-posterior density interval estimates. Comprehensive numerical examinations, in terms of four-accuracy metrics, namely: root mean squared-error, mean relative absolute-biases, interval lengths, and interval coverage-probabilities, are done to evaluate the supplied estimates. To suggest the best progressive scenario, various optimal criteria are estimated through the offered frequentist estimates. Finally, three applications from the natural sciences—engineering, chemistry, and physics—are utilized to show the utility of the presented methodologies and the possibility of adjusting research aims to real-world problems. Real-world data results stated that the GomMak model provides a useful explanation for various practical situations in the presence of such controlled data.

Lindley’s method and MCMC technique for estimating the exponentiated Kies lifetime model parameters and reliability characteristics using adaptive type-II progressive censoring with application

In this paper, statistical inferences for the parameters, reliability, and failure rate functions (as parameters) of the exponentiated Kies distribution are accomplished using maximum likelihood and both Lindley and Markov chain Monte Carlo techniques, under adaptive Type-II progressive censoring. Depending on the Fisher information matrix and the delta method, we find the approximate confidence intervals for the parameters and the reliability and failure rate functions, respectively. Furthermore, the samples generated from Markov chains Monte Carlo are used to generate credible intervals for the parameters, reliability, and failure rates functions. Symmetric and asymmetric loss functions are also used to find Bayes estimators using Markov chain Monte Carlo and Lindley techniques. Comparisons between the two techniques of evaluation for different estimators under different sizes of data sets are conducted. Finally, a real-life example is applied to clarify the results of the used techniques.

Optimal sizing and lifetime investigation of second life lithium-ion battery for grid-scale stationary application

The renewable energy sources (RESs) integration into the grid system aims to solve the problem of power shortage and satisfy the increasing demand with the production of surplus energy. However, the intermittent nature of these RESs (solar and wind) is a challenge to integrate with the grid system without the deployment of mitigating solutions. The re-use of first life-end-of-life (FL_EoL) electric vehicle batteries known as second life batteries (SLBs) is therefore proposed as a reliable solution to resolve this problem, satisfying the techno-economic requirements of stationary applications. Though various studies performed on the technical viability evaluation of SLBs, most of them have not considered the field data of existing stationary plants and were found to be limited with simulation-based aging results. On the other hand, few of the studies have performed the second life aging test with limited cycles considering the end of test conditions rather than using the cells reached to their end of first life criteria. Therefore, in this paper, the prolonged cycling aging of SLBs is conducted (both cell-level and module-level aging), focusing on aging characteristics of SLBs by using a real-life renewable power smoothing profile extracted from an existing photovoltaic grid-connected system (PVGCS) installed in Ethiopia. Prior to the aging characterization of SLBs, assessment of the selected stationary application requirement, optimal sizing of the storage battery and cycling profile definition are performed using advanced moving average ramp rate controller (MARRC) algorithm. The proposed method of MARRC is used to determine the optimal SLBs capacity by analyzing the relationship between ramp-rate, initial battery capacity and window sizes of moving average control method in terms of time series. The algorithm addresses the main objectives of reducing unnecessary battery cycling, mitigating ramp-rate violations and meeting minimum storage requirements for the second-life application. From the result of the battery sizing study, the desired optimal battery capacity and the permissible ramp-rate limit below 10 %/minute is achieved. Moreover, the aging characterization and lifetime model validation results with a root mean square error (RMSE) of 1.5 % shows that, the technical performance of the SLBs is found to be promising with an efficiency of >90 % interpreted in terms of the service year that the SLBs can provide. Therefore, SLBs can be regarded as a viable solution for integrating RESs with the grid system for the power variability smoothing application.

Nonconducting RF and DC Hot Carrier Stresses in 14/16-nm FinFETs for RF Power Amplifiers

Hot carrier reliability under nonconducting (NC) RF and dc stresses is measured and modeled on 14/16-nm FinFETs used for RF PAs. The impact of stress on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textbf{I}-\textbf{V}$</tex-math> </inline-formula> and RF parameters is examined. RF parameter stress response suggests that degradations are located near the drain end of the channel within the pinch-off region. For classic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit{V}_{\text{gs}}=\text{0}$</tex-math> </inline-formula> V OFF-state RF stress, quasi-static-approximation (QSA) significantly underestimates degradation, necessitating measurement-based lifetime modeling. At near-threshold <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit{V}_{\text{gs}}$</tex-math> </inline-formula> , a condition of interest for our PAs, the degradation shows significant die-to-die variations dominated by variations of the subthreshold channel current that initiates the hot carriers. Modeling accounting for the subthreshold channel current variations shows that the near-threshold RF stress is approximately quasi-static. The results show that these FinFETs provide enough margins against NC RF stress for the intended PA applications.

Electron Lifetimes Measured at LEO: Comparison With RBSP Estimates and Pitch Angle Resolved Lifetimes

AbstractElectron lifetimes are important for understanding the dominant loss processes of radiation belt electrons to the atmosphere and for accurate radiation belt modeling. We estimate electron lifetimes from the precipitating population measured in Low Earth Orbit (LEO) from the Polar Orbiting Environmental Satellites (POES). We compare our estimates to previous estimates from the Radiation Belt Storm Probes (RBSP) by Claudepierre et al. (2020b, https://doi.org/10.1029/2019GL086053). We also present the first complete pitch angle resolved lifetimes in the radiation belts. Quasi‐linear theory predicts the pitch angle distribution decays uniformly, therefore if steady‐state decay is realized, POES and RBSP should measure similar lifetimes. Lifetime estimates from LEO are shown to be in good agreement with those from Van Allen Probes in the outer belt and pitch angle resolved lifetimes indicate that steady‐state decay is realized. However, a systematic slight overestimation of the POES lifetimes reveal that the POES instruments may suffer from bremsstrahlung contamination. At L = 2–3, no decay intervals were identified in the POES electron fluxes and the near loss cone RBSP electron fluxes. We show that at these L‐shells, where lifetimes are long, there is an apparent decoupling of the perpendicular and parallel flux. Large pitch angle anisotropy and processes that affect low pitch angle electrons are two explanations for this apparent decoupling. Electron lifetime models that use pitch angle independent lifetimes, derived from the equatorially mirroring electron flux, likely underestimate the amount of precipitating flux and may not capture the dynamics of low equatorial pitch angle electrons at L &lt; 3.

A GEE approach for the semiparametric accelerated lifetime model with multivariate interval-censored data

A GEE approach for the semiparametric accelerated lifetime model with multivariate interval-censored data

A review on isothermal rotating bending fatigue failure: Microstructural and lifetime modeling of wrought and additive manufactured alloys

AbstractThe cumulative effect of many incidents that are brought about by an increase in temperature establishes an environment in which premature failure (including fatigue failure) becomes a challenging issue. Isothermal rotating bending fatigue (IT‐RBF) testing may simulate industrial components' high temperatures and rotating environments. This state‐of‐the‐art review paper covers the current research on IT‐RBF failure in wrought and additive‐manufactured alloys, focusing on microstructural and lifetime models. The article emphasizes the need of using microstructural information in fatigue life models to better represent complex material structure‐failure behavior associations. Additive‐manufactured alloys contain unique microstructural characteristics and processing‐induced defects making fatigue modeling difficult. The paper concludes with implications for industrial fatigue‐resistant alloy development. It emphasizes the necessity for a multidisciplinary approach that integrates materials science, mechanics, and data science to optimize these materials under cyclic loads. The review concludes by proposing future research and innovation in this subject.

Analysis of WE Parameters of Life Using Adaptive-Progressively Type-II Hybrid Censored Mechanical Equipment Data

A new two-parameter weighted-exponential (WE) distribution, as a beneficial competitor model to other lifetime distributions, namely: generalized exponential, gamma, and Weibull distributions, is studied in the presence of adaptive progressive Type-II hybrid data. Thus, based on different frequentist and Bayesian estimation methods, we study the inferential problem of the WE parameters as well as related reliability indices, including survival and failure functions. In frequentist setups, besides the standard likelihood-based estimation, the product of spacing (PS) approach is also taken into account for estimating all unknown parameters of life. Making use of the delta method and the observed Fisher information of the frequentist estimators, approximated asymptotic confidence intervals for all unknown parameters are acquired. In Bayes methodology, from the squared-error loss with independent gamma density priors, the point and interval estimates of the unknown parameters are offered using both joint likelihood and the product of spacings functions. Because a closed solution to the Bayes estimators is not accessible, the Metropolis–Hastings sampler is presented to approximate the Bayes estimates and also to create their associated highest interval posterior density estimates. To figure out the effectiveness of the developed approaches, extensive Monte Carlo experiments are implemented. To highlight the applicability of the offered methodologies in practice, one real-life data set consisting of 30 failure times of repairable mechanical equipment is analyzed. This application demonstrated that the offered WE model provides a better fit compared to the other eight lifetime models.

Cost-Effectiveness of Osteoporosis Opportunistic Screening Using Computed Tomography in China

ObjectivesUnderutilization and insufficient availability of dual-energy X-ray absorptiometry (DXA) in diagnosing osteoporosis in China could be changed by adopting unindicated quantitative computed tomography. We aimed to assess the cost-effectiveness of quantitative computed tomography (QCT) as a screening tool for osteoporosis in China. MethodsA Markov microsimulation model was developed to assess the long-term costs and quality-adjusted life-years (QALYs) saved associated with 2 examinations as opportunistic screening for osteoporosis in a general population without prior histories of fracture. The diagnostic performance of both examinations was incorporated into the model. In lifetime modeling, opportunistically screened people may face the risk of experiencing hip, vertebral, and wrist fractures depending on their osteoporosis, age, and sex. Model parameters were informed by published literature. ResultsThe base-case result showed that QCT was associated with higher costs ($6054 vs $5883) and higher benefits (10.081 vs 10.071 QALYs) in comparison with DXA, making QCT a cost-effective option for opportunistic screening (incremental cost-effectiveness ratio of US $16 430/QALY). Screening with QCT led to fewer fractures over the lifetime simulation: for every 10 000 people screened, 129 fractures (32 hip, 78 vertebral, and 19 wrist fractures) could be avoided because of the early initiation of antiosteoporotic treatment. ConclusionsUsing QCT to screen people for osteoporosis is more cost-effective than standard practice in China, where access to DXA is minimal. This finding could support opportunistic osteoporosis screening using QCT in other countries with similar status.

Survival Analysis of the PRC Model from Adaptive Progressively Hybrid Type-II Censoring and Its Engineering Applications

A new two-parameter statistical model, obtained by compounding the generalized-exponential and exponential distributions, called the PRC lifetime model, is explored in this paper. This model can be easily linked to other well-known six-lifetime models; namely the exponential, log-logistic, Burr, Pareto and generalized Pareto models. Adaptive progressively hybrid Type-II censored strategy, used to increase the efficiency of statistical inferential results and save the total duration of a test, has become widely used in various sectors such as medicine, biology, engineering, etc. Via maximum likelihood and Bayes inferential methodologies, given the presence of such censored data, the challenge of estimating the unknown parameters and some reliability time features, such as reliability and failure rate functions, of the PRC model is examined. The Markov-Chain Monte Carlo sampler, when the model parameters are assumed to have independent gamma density priors, is utilized to produce the Bayes’ infer under the symmetric (squared-error) loss of all unknown subjects. Asymptotic confidence intervals as well as the highest posterior density intervals of the unknown parameters and the unknown reliability indices are also created. An extensive Monte Carlo simulation is implemented to investigate the accuracy of the acquired point and interval estimators. Four various optimality criteria, to select the best progressive censored design, are used. To demonstrate the applicability and feasibility of the proposed model in a real-world scenario, two data sets from the engineering sector; one based on industrial devices and the other on aircraft windshield, are analyzed. Numerical evaluations showed that the PRC model furnishes a superior fit compared to seven other models in the literature, including: alpha-power exponential, log-logistic, Nadarajah–Haghighi, generalized-exponential, Weibull, gamma and exponential lifetime distributions. The findings demonstrate that, in order to obtain the necessary estimators, the Bayes’ paradigm via Metropolis–Hastings sampler is recommended compared to its competitive likelihood approach.

Individual lifetime benefit from low-dose colchicine in patients with chronic coronary artery disease.

Low-dose colchicine reduces cardiovascular risk in patients with coronary artery disease (CAD), but absolute benefits may vary between individuals. This study aimed to assess the range of individual absolute benefits from low-dose colchicine according to patient risk profile. The European Society of Cardiology (ESC) guideline-recommended SMART-REACH model was combined with the relative treatment effect of low-dose colchicine and applied to patients with CAD from the Low-Dose Colchicine 2 (LoDoCo2) trial and the Utrecht Cardiovascular Cohort-Second Manifestations of ARTerial disease (UCC-SMART) study (n = 10 830). Individual treatment benefits were expressed as 10-year absolute risk reductions (ARRs) for myocardial infarction, stroke, or cardiovascular death (MACE), and MACE-free life-years gained. Predictions were also performed for MACE plus coronary revascularization (MACE+), using a new lifetime model derived in the REduction of Atherothrombosis for Continued Health (REACH) registry. Colchicine was compared with other ESC guideline-recommended intensified (Step 2) prevention strategies, i.e. LDL cholesterol (LDL-c) reduction to 1.4 mmol/L and systolic blood pressure (SBP) reduction to 130 mmHg. The generalizability to other populations was assessed in patients with CAD from REACH North America and Western Europe (n = 25 812). The median 10-year ARR from low-dose colchicine was 4.6% [interquartile range (IQR) 3.6-6.0%] for MACE and 8.6% (IQR 7.6-9.8%) for MACE+. Lifetime benefit was 2.0 (IQR 1.6-2.5) MACE-free years, and 3.4 (IQR 2.6-4.2) MACE+-free life-years gained. For LDL-c and SBP reduction, respectively, the median 10-year ARR for MACE was 3.0% (IQR 1.5-5.1%) and 1.7% (IQR 0.0-5.7%), and the lifetime benefit was 1.2 (IQR 0.6-2.1) and 0.7 (IQR 0.0-2.3) MACE-free life-years gained. Similar results were obtained for MACE+ and in American and European patients from REACH. The absolute benefits of low-dose colchicine vary between individual patients with chronic CAD. They may be expected to be of at least similar magnitude to those of intensified LDL-c and SBP reduction in a majority of patients already on conventional lipid-lowering and blood pressure-lowering therapy.

Exploring New Horizons: Advancing Data Analysis in Kidney Patient Infection Rates and UEFA Champions League Scores Using Bivariate Kavya–Manoharan Transformation Family of Distributions

In survival analyses, infections at the catheter insertion site among kidney patients using portable dialysis machines pose a significant concern. Understanding the bivariate infection recurrence process is crucial for healthcare professionals to make informed decisions regarding infection management protocols. This knowledge enables the optimization of treatment strategies, reduction in complications associated with infection recurrence and improvement of patient outcomes. By analyzing the bivariate infection recurrence process in kidney patients undergoing portable dialysis, it becomes possible to predict the probability, timing, risk factors and treatment outcomes of infection recurrences. This information aids in identifying the likelihood of future infections, recognizing high-risk patients in need of close monitoring, and guiding the selection of appropriate treatment approaches. Limited bivariate distribution functions pose challenges in jointly modeling inter-correlated time between recurrences with different univariate marginal distributions. To address this, a Copula-based methodology is presented in this study. The methodology introduces the Kavya–Manoharan transformation family as the lifetime model for experimental units. The new bivariate models accurately measure dependence, demonstrate significant properties, and include special sub-models that leverage exponential, Weibull, and Pareto distributions as baseline distributions. Point and interval estimation techniques, such as maximum likelihood and Bayesian methods, where Bayesian estimation outperforms maximum likelihood estimation, are employed, and bootstrap confidence intervals are calculated. Numerical analysis is performed using the Markov chain Monte Carlo method. The proposed methodology’s applicability is demonstrated through the analysis of two real-world data-sets. The first data-set, focusing on infection and recurrence time in kidney patients, indicates that the Farlie–Gumbel–Morgenstern bivariate Kavya–Manoharan–Weibull (FGMBKM-W) distribution is the best bivariate model to fit the kidney infection data-set. The second data-set, specifically that related to UEFA Champions League Scores, reveals that the Clayton Kavya–Manoharan–Weibull (CBKM-W) distribution is the most suitable bivariate model for fitting the UEFA Champions League Scores. This analysis involves examining the time elapsed since the first goal kicks and the home team’s initial goal.