Weibull Accelerated Failure Time Model Research Articles

Assessing the survival time of hospitalized patients in Eastern Ethiopia during 2019–2020 using the Bayesian approach: A retrospective cohort study

AbstractBackground and AimsSevere acute malnutrition remains a significant health challenge, particularly in low‐ and middle‐income countries. The aim of this study was to determine the survival time of under‐five children with severe acute malnutrition.MethodsA retrospective cohort study was conducted at a hospital, focusing on under‐five children with severe acute malnutrition. The study included 322 inpatients admitted to the Chiro hospital in Chiro, Ethiopia, between September 2019 and August 2020, whose data was obtained from medical records. Survival functions were analysed using Kaplan‒Meier plots and log‐rank tests. The survival time of severe acute malnutrition was further analysed using the Cox proportional hazards model and Bayesian parametric survival models, employing integrated nested Laplace approximation methods.ResultsAmong the 322 patients, 118 (36.6%) died as a result of severe acute malnutrition. The estimated median survival time for inpatients was found to be 2 weeks. Model selection criteria favored the Bayesian Weibull accelerated failure time model, which demonstrated that age, body temperature, pulse rate, nasogastric (NG) tube usage, hypoglycemia, anemia, diarrhea, dehydration, malaria, and pneumonia significantly influenced the survival time of severe acute malnutrition.ConclusionsThis study revealed that children below 24 months, those with altered body temperature and pulse rate, NG tube usage, hypoglycemia, and comorbidities such as anemia, diarrhea, dehydration, malaria, and pneumonia had a shorter survival time when affected by severe acute malnutrition under the age of five. To reduce the death rate of children under 5 years of age, it is necessary to design community management for acute malnutrition to ensure early detection and improve access to and coverage for children who are malnourished.

Modeling the impact of driving aggression on lane change performance Measures: Steering compensatory Behavior, lane change execution duration and crash probability

The current study aimed to investigate the influence of driving aggression on lane change behavior. Drivers belonging to three levels of aggressive driving styles (aggressive, moderately aggressive, and non-aggressive) participated in lane change experiments, and 232 lane change maneuvers were recorded. Steering behavior during lane change maneuvers was investigated using three steering performance measures, including the standard deviations of steering angle, steering reversal rates, and maximum steering swerve. Three separate generalized estimating equation models were developed to analyze the effects of aggressive driving behavior on the standard deviation of steering angle, steering reversal rates, and maximum steering swerve. A Weibull accelerated failure time model with clustered heterogeneity was developed to analyze the effect of aggressive driving behavior on lane change execution duration. Additionally, to model the crash probability during lane change, a random effects logistic regression model was developed. The findings indicate that aggressive and moderately aggressive drivers completed lane changes 59% and 38% faster, respectively, compared to non-aggressive drivers. This implies a propensity for aggressive and moderately aggressive drivers to execute lane changes more swiftly than non-aggressive drivers. Aggressive drivers increased their standard deviation of steering angle, steering reversal rates, and maximum steering swerves by 180%, 60%, and 62%, respectively, compared to non-aggressive drivers. This implies that aggressive drivers engaged in extreme steering behavior as a compensatory measure for their reckless driving in order to avoid a collision during lane change maneuvering. However, despite their vigorous compensatory steering behavior, the crash rate (number of crashes observed) increased by 5.10 and 2.83 times for aggressive and moderately aggressive drivers, respectively, compared to non-aggressive drivers. The findings have implications for developing driving assistance systems and lane change simulation models.

Association between blood pressure and new onset of chronic kidney disease in non-diabetic Japanese adults: A population-based longitudinal study from 1998 to 2023

Background and aimsHypertension is a risk factor for developing chronic kidney disease (CKD). Studies of adult participants in the USA reported that hypertension increased the risk of developing CKD even in the non-diabetic population. However, studies in non-diabetic populations are limited and additional studies in other races are required. This study aimed to examine the relationship between hypertension and the development of CKD in non-diabetic Asian adults. Methods and resultsIn this longitudinal study, non-diabetic Japanese adults who took annual checkups from 1998 to 2023 were included. CKD was defined as <60 mL/min/1.73 m2, and hypertension was classified into four levels according to the guidelines of the American College of Cardiology/American Heart Association. The Weibull accelerated failure time model was selected because the proportional hazards assumption was violated. Of the 7363 (men: 40.3%) people in the final cohort, 2498 (men: 40.1%) developed CKD after a mean follow-up of 7.99 years. Elevated blood pressure (BP) and hypertension stage 2 had a 9% (95% confidence interval [CI]: 1%–16%) and 11% (95% CI: 5%–17%) shorter survival time to CKD onset, respectively, than normal BP. Hypertension stage 1 also had a shorter survival to CKD onset by point estimate, but all 95% CIs crossed 1 in all models. ConclusionsIn a relatively healthy Asian population without diabetes, controlling BP to an appropriate range reduces the risk of developing CKD.

Assessing the survival time of women with breast cancer in Northwestern Ethiopia: using the Bayesian approach

BackgroundDespite the significant weight of difficulty, Ethiopia's survival rate and mortality predictors have not yet been identified. Finding out what influences outpatient breast cancer patients' survival time was the major goal of this study.MethodsA retrospective study was conducted on outpatients with breast cancer. In order to accomplish the goal, 382 outpatients with breast cancer were included in the study using information obtained from the medical records of patients registered at the University of Gondar referral hospital in Gondar, Ethiopia, between May 15, 2016, and May 15, 2020. In order to compare survival functions, Kaplan-Meier plots and the log-rank test were used. The Cox-PH model and Bayesian parametric survival models were then used to examine the survival time of breast cancer outpatients. The use of integrated layered Laplace approximation techniques has been made.ResultsThe study included 382 outpatients with breast cancer in total, and 148 (38.7%) patients died. 42 months was the estimated median patient survival time. The Bayesian Weibull accelerated failure time model was determined to be suitable using model selection criteria. Stage, grade 2, 3, and 4, co-morbid, histological type, FIGO stage, chemotherapy, metastatic number 1, 2, and >=3, and tumour size all have a sizable impact on the survival time of outpatients with breast cancer, according to the results of this model. The breast cancer outpatient survival time was correctly predicted by the Bayesian Weibull accelerated failure time model.ConclusionsCompared to high- and middle-income countries, the overall survival rate was lower. Notable variables influencing the length of survival following a breast cancer diagnosis were weight loss, invasive medullar histology, comorbid disease, a large tumour size, an increase in metastases, an increase in the International Federation of Gynaecologists and Obstetricians stage, an increase in grade, lymphatic vascular space invasion, positive regional nodes, and late stages of cancer. The authors advise that it is preferable to increase the number of early screening programmes and treatment centres for breast cancer and to work with the public media to raise knowledge of the disease's prevention, screening, and treatment choices.

Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

Longitudinal time-to-event analysis is a statistical method to analyze data where covariates are measured repeatedly. In survival studies, the risk for an event is estimated using Cox-proportional hazard model or extended Cox-model for exogenous time-dependent covariates. However, these models are inappropriate for endogenous time-dependent covariates like longitudinally measured biomarkers, Carcinoembryonic Antigen (CEA). Joint models that can simultaneously model the longitudinal covariates and time-to-event data have been proposed as an alternative. The present study highlights the importance of choosing the baseline hazards to get more accurate risk estimation. The study used colon cancer patient data to illustrate and compare four different joint models which differs based on the choice of baseline hazards [piecewise-constant Gauss-Hermite (GH), piecewise-constant pseudo-adaptive GH, Weibull Accelerated Failure time model with GH & B-spline GH]. We conducted simulation study to assess the model consistency with varying sample size (N = 100, 250, 500) and censoring (20 %, 50 %, 70 %) proportions. In colon cancer patient data, based on Akaike information criteria (AIC) and Bayesian information criteria (BIC), piecewise-constant pseudo-adaptive GH was found to be the best fitted model. Despite differences in model fit, the hazards obtained from the four models were similar. The study identified composite stage as a prognostic factor for time-to-event and the longitudinal outcome, CEA as a dynamic predictor for overall survival in colon cancer patients. Based on the simulation study Piecewise-PH-aGH was found to be the best model with least AIC and BIC values, and highest coverage probability(CP). While the Bias, and RMSE for all the models showed a competitive performance. However, Piecewise-PH-aGH has shown least bias and RMSE in most of the combinations and has taken the shortest computation time, which shows its computational efficiency. This study is the first of its kind to discuss on the choice of baseline hazards.

Modeling Survival Time to Death among Stroke Patients at Jimma University Medical Center, Southwest Ethiopia: A Retrospective Cohort Study.

Stroke is a life-threatening condition that occurs due to impaired blood flow to brain tissues. Every year, about 15 million people worldwide suffer from a stroke, with five million of them suffering from some form of permanent physical disability. Globally, stroke is the second-leading cause of death following ischemic heart disease. It is a public health burden for both developed and developing nations, including Ethiopia. This study is aimed at estimating the time to death among stroke patients at Jimma University Medical Center, Southwest Ethiopia. A facility-based retrospective cohort study was conducted among 432 patients. The data were collected from stroke patients under follow-up at Jimma University Medical Center from January 1, 2016, to January 30, 2019. A log-rank test was used to compare the survival experiences of different categories of patients. The Cox proportional hazard model and the accelerated failure time model were used to analyze the survival analysis of stroke patients using R software. An Akaike's information criterion was used to compare the fitted models. Of the 432 stroke patients followed, 223 (51.6%) experienced the event of death. The median time to death among the patients was 15 days. According to the results of the Weibull accelerated failure time model, the age of patients, atrial fibrillation, alcohol consumption, types of stroke diagnosed, hypertension, and diabetes mellitus were found to be the significant prognostic factors that contribute to shorter survival times among stroke patients. The Weibull accelerated failure time model better described the time to death of the stroke patients' data set than other distributions used in this study. Patients' age, atrial fibrillation, alcohol consumption, being diagnosed with hemorrhagic types of stroke, having hypertension, and having diabetes mellitus were found to be factors shortening survival time to death for stroke patients. Hence, healthcare professionals need to thoroughly follow the patients who pass risk factors. Moreover, patients need to be educated about lifestyle modifications.

Epigenome-wide methylation and progression to high-grade cervical intraepithelial neoplasia (CIN2+): a prospective cohort study in the United States

BackgroundMethylation levels may be associated with and serve as markers to predict risk of progression of precancerous cervical lesions. We conducted an epigenome-wide association study (EWAS) of CpG methylation and progression to high-grade cervical intraepithelial neoplasia (CIN2 +) following an abnormal screening test.MethodsA prospective US cohort of 289 colposcopy patients with normal or CIN1 enrollment histology was assessed. Baseline cervical sample DNA was analyzed using Illumina HumanMethylation 450K (n = 76) or EPIC 850K (n = 213) arrays. Participants returned at provider-recommended intervals and were followed up to 5 years via medical records. We assessed continuous CpG M values for 9 cervical cancer-associated genes and time-to-progression to CIN2+. We estimated CpG-specific time-to-event ratios (TTER) and hazard ratios using adjusted, interval-censored Weibull accelerated failure time models. We also conducted an exploratory EWAS to identify novel CpGs with false discovery rate (FDR) < 0.05.ResultsAt enrollment, median age was 29.2 years; 64.0% were high-risk HPV-positive, and 54.3% were non-white. During follow-up (median 24.4 months), 15 participants progressed to CIN2+. Greater methylation levels were associated with a shorter time-to-CIN2+ for CADM1 cg03505501 (TTER = 0.28; 95%CI 0.12, 0.63; FDR = 0.03) and RARB Cluster 1 (TTER = 0.46; 95% CI 0.29, 0.71; FDR = 0.01). There was evidence of similar trends for DAPK1 cg14286732, PAX1 cg07213060, and PAX1 Cluster 1. The EWAS detected 336 novel progression-associated CpGs, including those located in CpG islands associated with genes FGF22, TOX, COL18A1, GPM6A, XAB2, TIMP2, GSPT1, NR4A2, and APBB1IP.ConclusionsUsing prospective time-to-event data, we detected associations between CADM1-, DAPK1-, PAX1-, and RARB-related CpGs and cervical disease progression, and we identified novel progression-associated CpGs.ImpactMethylation levels at novel CpG sites may help identify individuals with ≤CIN1 histology at higher risk of progression to CIN2+ and inform risk-based cervical cancer screening guidelines.

Predictors of 1-year enteral autonomy in children with intestinal failure: A descriptive retrospective cohort study.

The International Intestinal Failure Registry (IIFR) is an international consortium to study intestinal failure (IF) outcomes in a large contemporary pediatric cohort. We aimed to identify predictors of early (1-year) enteral autonomy. We included IIFR pilot phase patients. IF was defined by a parenteral nutrition need for at least 60 days due to a primary gastrointestinal etiology. The primary outcome was time to enteral autonomy achievement. We built a mixed-effects Weibull accelerated failure time model with random effects by center to analyze variables associated with enteral autonomy achievement with a primary outcome of time ratio (TR). We included 189 patients (82% with short bowel syndrome) representing 11 international centers. Cumulative incidence of early enteral autonomy was 51.6%, and death was 6.5%. In multivariable analysis, ostomy presence (TR, 2.63; 95% CI, 1.41-4.90) was associated with increased time to enteral autonomy achievement, and Asian/Indian (TR, 0.28; 95% CI, 0.10-0.81) and Pacific Islander race (TR, 0.34; 95% CI, 0.13-0.90) were associated with decreased time to enteral autonomy achievement. In a second model in the subset with measured percentage of bowel length remaining, ostomy presence (TR, 4.21; 95% CI, 1.90-9.33) was associated with increased time to enteral autonomy achievement, whereas greater percentage of bowel remaining (TR, 0.96; 95% CI, 0.94-0.98) was associated with decreased time to enteral autonomy achievement. Minimizing bowel resection at initial surgery and establishing bowel continuity by ostomy reversal can effectively decrease the time to early enteral autonomy achievement in children with IF.

A population-based longitudinal study on glycated hemoglobin levels and new-onset chronic kidney disease among non-diabetic Japanese adults

Chronic kidney disease (CKD) is a major global public health problem. Recent studies reported that diabetes and prediabetes are risk factors for developing CKD; however, the exact glycated hemoglobin (HbA1c) cut-off value for prediabetes remains controversial. In this study, we aimed to examine the relationship between HbA1c levels and subsequent CKD development in greater detail than previous studies. Longitudinal data of annual checkups of 7176 Japanese non-diabetic people (male: 40.4%) from 1998 to 2022 was analyzed. HbA1c values were categorized into < 5.0%, 5.0–5.4%, 5.5–5.9%, and 6.0–6.4%. CKD was defined as an estimated glomerular filtration rate < 60 ml/min/1.73 m2. The descriptive statistics at study entry showed that higher HbA1c values were associated with male, older, overweight or obese, hypertensive, or dyslipidemic people. During a mean follow-up of 7.75 person-years, 2374 participants (male: 40.0%) developed CKD. The Weibull accelerated failure time model was selected because the proportional hazards assumption was violated. The adjusted time ratios of developing CKD for HbA1c levels of 5.5–5.9% and 6.0–6.4% compared with 5.0–5.4% were 0.97 (95% confidence interval: 0.92–1.03) and 1.01 (95% confidence interval: 0.90–1.13), respectively. There was no association between HbA1c in the prediabetic range and subsequent CKD development.

Time to first childbirth and its predictors among reproductive-age women in Ethiopia: survival analysis of recent evidence from the EDHS 2019.

Being a mother for the first time is the most significant event in a woman's life. "Age at first birth" refers to a mother's age in years when she gives birth to her first child. The age of first childbirth has physical, economic, and social implications. However, little is known about this issue in Ethiopia. Thus, this study sought to determine the time to first childbirth and its predictors at a national level. Data were extracted from the 2019 Ethiopia Demographic and Health Survey using STATA version 17 software. A total of 8,885 weighted reproductive-age women (15-49 years) were included in this study. A Kaplan-Meier survivor curve was generated to estimate the time of first childbirth. A log-rank test was used to compare the difference in survival curves. Akaike information criteria and Bayesian information criteria were calculated to select the appropriate survival model for the data. The Weibull accelerated failure time model with no frailty distribution was used to identify significant predictors. The overall median survival time to first childbirth was 18 years. The significant predictors of time to first childbirth were the educational level of the mother [primary education (ϕ = 1.036, 95% CI: 1.011, 1.063), secondary and above education (ϕ = 1.154, 95% CI: 1.118, 1.191)], knowledge of any contraceptive method [know at least one (ϕ = 1.051, 95% CI: 1.006, 1.101)], and media exposure (ϕ = 1.048, 95% CI: 1.011, 1.086). The median survival time to first childbirth was 18 years, which is lower than the optimal age for first childbirth (late 20 s and early 30 s). The timing of first childbirth in Ethiopia is mainly influenced by the educational level of women, knowledge of contraceptive methods, and exposure to media. Thus, exposing women to educational materials and other awareness-creation campaigns regarding the consequences of early first childbirth and strategies to improve women's knowledge of contraceptive methods is highly recommended.

Interval-Censored Regression with Non-Proportional Hazards with Applications

Proportional hazards models and, in some situations, accelerated failure time models, are not suitable for analyzing data when the failure ratio between two individuals is not constant. We present a Weibull accelerated failure time model with covariables on the location and scale parameters. By considering the effects of covariables not only on the location parameter, but also on the scale, a regression should be able to adequately describe the difference between treatments. In addition, the deviance residuals adapted for data with the interval censored and the exact time of failure proved to be satisfactory to verify the fit of the model. This information favors the Weibull regression as an alternative to the proportional hazards models without masking the effect of the explanatory variables.

Misclassification simulation extrapolation method for a Weibull accelerated failure time model.

The problem of misclassification in covariates is ubiquitous in survival data and often leads to biased estimates. The misclassification simulation extrapolation method is a popular method to correct this bias. However, its impact on Weibull accelerated failure time models has not been studied. In this paper, we study the bias caused by misclassification in one or more binary covariates in Weibull accelerated failure time models and explore the use of the misclassification simulation extrapolation in correcting for this bias, along with its asymptotic properties. Simulation studies are carried out to investigate the numerical properties of the resulting estimator for finite samples. The proposed method is then applied to colon cancer data obtained from the cancer registry at Memorial Sloan Kettering Cancer Center.

Examining chronic disease onset across varying age groups of Indian adults using competing risk analysis

In low-and-middle-income countries, people develop chronic diseases at a younger age, leading to health-and-economic loss. Estimates of the age of onset of chronic disease provide evidence for policy intervention, but in the Indian context, evidence is limited. The present study aims to explore the onset of seven chronic diseases across adults and the elderly, along with the prognostic factors of chronic disease onset. Using Wave 1 data of the Longitudinal Ageing Study in India (LASI), we estimated the statistical distributions, the median age at onset, and Loglogistic and Weibull accelerated failure time model to understand the onset of seven medically diagnosed self-reported chronic diseases across age groups. We also obtained the sub-distribution hazard ratio (SHR) from the Fine-Gray model to determine the risk of contracting selected chronic diseases in a competing risk setup. The seven chronic diseases– hypertension, diabetes, lung disease, heart disease/stroke, arthritis, neurological disease, and cancer– were developing early, especially in individuals aged 45–54 and 55–64. Arthritis risk was higher in rural areas, and physically active adults and elderly were 1.32 times (95% CI 1.12–1.56) more likely to develop heart disease/stroke. The emerging evidence of the early onset of neurological diseases in middle-aged adults (i.e., among the 45–54 age group) reminds us of the need to reinforce a balance between the physical and mental life of individuals. The early onset of chronic diseases in the independent and working-age category (45–54 years) can have many social and economic implications. For instance, it can create a greater healthcare burden when these individuals grow older with these diseases. Further, disease-specific interventions would be helpful in reducing future chronic disease burden.

Accelerated Failure Time Models with Applications to Endometrial Cancer Survival Data

The objective of this study is to determine the significant predictors of endometrial cancer using accelerated failure time models (AFTM). We have demonstrated the applications of AFTM viz. Exponential, Weibull, Log-normal, Log-logistic, Gompertz, Gamma and Generalized Gamma AFTM, as an alternative of Cox proportional hazard model. Data for the analysis was collected from Acharya Harihar Post Graduate Institute of Cancer (AHPGIC), Cuttack, Odisha during the period 2016–20. Based on the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) value, the Weibull AFTM has been chosen as the best fitted AFT model. The predictors such as age, comorbidity, tumor size, isolated para-aortic and adnexa have been found as significant predictors (p-value &lt; 0.05) to explain the survival of endometrial cancer patients. Hence, by optimizing different treatments, based on such prognostic factors plays an important role in managing endometrial cancer at an early stage.

Response to Treatment, Racial and Ethnic Disparity, and Survival in Patients With Breast Cancer Undergoing Neoadjuvant Chemotherapy in the US

With the increasing delivery of neoadjuvant chemotherapy (NACT) for patients with breast cancer in the US, it is important to know whether there is differential response to NACT by race and ethnicity and the potential long-term outcomes. To examine whether there were any racial and ethnic differences in pathologic complete response (pCR) rate following NACT and, if so, whether they varied by molecular subtype and were associated with survival. A retrospective cohort study was conducted including patients with stage I to III breast cancer diagnosed between January 2010 and December 2017 who underwent surgery and received NACT; median follow-up was 5.8 years, and data analysis was conducted from August 2021 to January 2023. Data were obtained from the National Cancer Data Base, a nationwide, facility-based, oncology data set that captures approximately 70% of all newly diagnosed cases of breast cancer in the US. Pathologic complete response, defined as ypT0/Tis ypN0, was modeled using logistic regression. Racial and ethnic differences in survival were analyzed using a Weibull accelerated failure time model. Mediation analysis was conducted to measure whether racial and ethnic differences in the pCR rate affect survival. The study included 107 207 patients (106 587 [99.4%] women), with a mean (SD) age of 53.4 (12.1) years. A total of 5009 patients were Asian or Pacific Islander, 18 417 were non-Hispanic Black, 9724 were Hispanic, and 74 057 were non-Hispanic White. There were significant racial and ethnic differences in pCR rates, but the differences were subtype-specific. In hormone receptor-negative (HR-)/erb-b2 receptor tyrosine kinase 2 (ERBB2; formerly HER2 or HER2/neu)-positive (ERBB2+) subtype, Asian and Pacific Islander patients achieved the highest pCR rate (56.8%), followed by Hispanic (55.2%) and non-Hispanic White (52.3%) patients with the lowest pCR rate seen in Black patients (44.8%). In triple-negative breast cancer, Black patients had a lower pCR rate (27.3%) than other racial and ethnic groups (all >30%). In HR+/ERBB2- subtype, Black patients had a higher pCR rate (11.3%) than other racial/ethnic groups (all ≤10%). In mediation analysis, racial and ethnic differences in achieving pCR after NACT could explain approximately 20% to 53% of the subtype-specific survival differences across racial and ethnic groups. In this cohort study of patients with breast cancer receiving NACT, Black patients had a lower pCR rate for triple-negative and HR-/ERBB2+ breast cancer but a higher pCR rate for HR+/ERBB2- diseases, whereas Asian and Pacific Islander patients had a higher pCR rate for HR-/ERBB2+ diseases. Tumor grade and ERBB2 copy number could account for some of these within-subtype disparities, but further studies are warranted. Inability to achieve a pCR can mediate in part, but not entirely, the worse survival outcomes experienced by Black patients.

Comparison of 2021 CKD-EPI Equations for Estimating Racial Differences in Preemptive Waitlisting for Kidney Transplantation.

Wait time for kidney transplantation can accrue when GFR is ≤20 ml/min. We examined whether using the race-free 2021 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations to guide preemptive waitlisting could attenuate racial differences in accruable preemptive wait time. Our retrospective cohort study included Black or White Chronic Renal Insufficiency Cohort (CRIC) participants who were theoretically eligible for waitlist registration. We used Weibull accelerated failure time models to determine the association between race (Black or White) and time to kidney failure from the qualifying visit when the eGFR by creatinine or creatinine-cystatin C 2021 CKD-EPI equations fell to ≤20 ml/min per 1.73 m2. We then tested for differences in the time ratios from models using the 2021 creatinine- or creatinine-cystatin C-based CKD-EPI equation through a bootstrapping approach. By the creatinine equation, 472 CRIC participants were theoretically eligible for waitlist registration, and potential preemptive wait time was similar for Black versus White participants (time ratio, 1.05; 95% confidence interval, 0.81 to 1.35). The median wait time by the creatinine equation that could be accrued for Black participants was 23 versus 22 months in White participants. By the creatinine-cystatin C equation, 441 CRIC participants were eligible, and potential wait time was 20% shorter (95% confidence interval, 0.62 to 1.02) for Black than White participants. The median wait time that could be accrued for Black participants was 21 versus 26 months for White participants when using the creatinine-cystatin C equation. Using bootstrapping, the ratio of the time ratio of the models using the creatinine versus creatinine-cystatin C equation was statistically significantly different (ratio of the time ratios = 1.31 with 95% confidence interval, 1.06 to 1.62). Use of the 2021 creatinine-based CKD-EPI equation to determine preemptive waitlist eligibility reduced racial differences in preemptive wait time accrual more than use of the creatinine-cystatin C 2021 CKD-EPI equation within a theoretical context.

PARAMETRIC AND SEMI-PARAMETRIC SURVIVAL MODELS WITH APPLICATION TO HYPERTENSION DATA

Correlated survival data with possible censoring are frequently encountered in survival analysis.When there are dependencies among observed survival times, conventional Cox proportionalhazards model (CPHM) and Accelerated Failure Time (AFT) models that assumes independentresponses may not be appropriate. In this study, we compare the performance of parametric andsemi-parametric survival models with application to clinical data. Specifically, the AFT modeland the CPHM with and without Random effect were compared. Data on hypertension wascollected from Federal Medical Centre Keffi and General Hospital Nasarawa for the period offive years (2016 – 2020). The results from the analysis revealed that the Weibull AFT modelwith Gamma Random effect distribution had the least AIC and BIC values indicating that itoutperformed the other models considered in this study. Hence, the interpretation of the resultswas based on the most efficient model. Based on our results, it was found that hypertensionpatient that were giving drugs on the visit to the hospital has longer survival time compared tothose that were not giving drugs. Also, Hypertension patient with blood group AB and Obesedhave lesser survival time as compared to those with blood group o+ and normal weightrespectively. The study recommend that health expert can use the Weibull AFT model withGamma Random effect for predicting the risk factors of Hypertension especially when the dataare correlated.Keywords: AFT, CPHM, Hazard, Hypertension, Survival,

Detection of multiple change points in a Weibull accelerated failure time model using sequential testing

AbstractWith improvements to cancer diagnoses and treatments, incidences and mortality rates have changed. However, the most commonly used analysis methods do not account for such distributional changes. In survival analysis, change point problems can concern a shift in a distribution for a set of time‐ordered observations, potentially under censoring or truncation. We propose a sequential testing approach for detecting multiple change points in the Weibull accelerated failure time model, since this is sufficiently flexible to accommodate increasing, decreasing, or constant hazard rates and is also the only continuous distribution for which the accelerated failure time model can be reparameterized as a proportional hazards model. Our sequential testing procedure does not require the number of change points to be known; this information is instead inferred from the data. We conduct a simulation study to show that the method accurately detects change points and estimates the model. The numerical results along with real data applications demonstrate that our proposed method can detect change points in the hazard rate.

Fatty liver disease and changes in dense breasts in pre- and postmenopausal women: the Kangbuk Samsung Health Study.

While increased breast density is a risk factor for breast cancer, the effect of fatty liver disease on breast density is unknown. We investigated whether fatty liver is a risk factor for changes in breast density over ~ 4years of follow-up in pre- and postmenopausal women. This study included 74,781 middle-aged Korean women with mammographically determined dense breasts at baseline. Changes in dense breasts were identified by more screening mammograms during follow-up. Hepatic steatosis (HS) was measured using ultrasonography. Flexible parametric proportional hazards models were used to determine the adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs), and a Weibull accelerated failure time model (AFT) was used to determine the time ratios (TRs) and 95% CIs. During a median follow-up of 4.1years, 4022 women experienced resolution of the dense breasts. The association between HS and dense breast resolution differed by the menopause status (P for interaction < 0.001). After adjusting for body mass index and other covariates, the aHRs (95% CI) for dense breast resolution comparing HS to non-HS were 0.81 (0.70-0.93) in postmenopausal women, while the association was converse in premenopausal women with the corresponding HRs of 1.30 (1.18-1.43). As an alternative approach, the multivariable-adjusted TR (95% CI) for dense breast survival comparing HS to non-HS were 0.81 (0.75-0.87) and 1.19 (1.06-1.33) in premenopausal and postmenopausal women, respectively. The association between HS and changes in dense breasts differed with the menopause status. HS increased persistent dense breast survival in postmenopausal women but decreased it in premenopausal women.

Examining the driver-pedestrian interaction at pedestrian crossings in the connected environment: A Hazard-based duration modelling approach

• The Weibull AFT model with gamma frailty is suitable to model response times. • Drivers maintain a higher safety margin with pedestrians in the connected environment. • The connected environment modifies the braking behavior of young drivers more than older drivers. • The connected environment modifies the braking behavior of male drivers more than female drivers. • Drivers in the connected environment select a slower driving speed at pedestrian crossings. The availability of advisory warnings via Vehicle-to-Vehicle and Vehicle-to-Infrastructure communication in the connected environments is expected to gradually increase over the next few years. Much of the research on advisory warning systems have examined driving behaviour in response to unexpected driving hazards; however, very little research has been conducted on common driving interactions such as interacting with pedestrians at pedestrian crossings. Therefore, the aim of this study is to investigate the effects of the connected environment on driving behaviour at pedestrian crossings. The connected environment was designed within the CARRS-Q advanced driving simulator. A combination of auditory (beep sound) and imagery message was simultaneously displayed on the windscreen to advise the driver on the presence of a pedestrian entering from a sidewalk. Seventy-eight licensed drivers drove the simulator in two driving conditions, namely, baseline and connected environment. The participants were 18–65 years old, and a third of them were females. Drivers' response to the driving aids and the braking behaviour were analysed in the latent response phase and the observable response phase, and the corresponding response times were modelled using the hazard-based duration modelling approach. In particular, this study applied the Weibull accelerated failure time model with shared frailty accounting for multiple observations from the same driver. Results showed that the time taken to respond to the pedestrian in the latent response phase was longer when the advisory warning was provided to the drivers, but the corresponding time in the observable response phase was shorter, indicating that drivers take an informed decision in the connected environment. Moreover, the safety margin—measured in terms of time-to-collision—was higher in the connected environment than the traditional driving environment, indicating a safer behavioural adaptation towards the connected environment.