IMPLEMENTASI MODEL ACCELERATED FAILURE TIME (AFT) BERDISTRIBUSI LOG-LOGISTIK PADA PASIEN PENYAKIT JANTUNG BAWAAN

Log-Logistic Accelerated Failure Time (AFT) model is survival analysis that is used when the survival time follows Log-Logistic distribution. Log-Logistic AFT model can be used to estimate survival time, survival function, and hazard function. Log-Logistic AFT model was formed by regressing covariates linierly against the log of survival time. Regression coefficients are estimated using maximum likelihood method. This study uses data from Atrial Septal Defect (ASD) patients, which is a congenital disease with a hole in the wall that separates the top of two chambers of the heart by using sensor type III. Survival time as the response variable, that is the time from patient was diagnosed with ASD until the first relapse and uses age, gender, treatment status (catheterization/surgery), defect size that is the size of the hole in the heart terrace, pulmonary hypertension status, and pain status as predictor variables. The result showed that variable gender, treatment status, defect size, pulmonary hypertension status, and pain status affect the first recurrence of ASD patients, so it is found that category of female, untreated patient, defect size ≥12mm, having pulmonary hypertension, having chest pain tend to have first recurrence sooner than the other category.

IMPLEMENTASI MODEL ACCELERATED FAILURE TIME (AFT) BERDISTRIBUSI LOG-LOGISTIK PADA PASIEN PENYAKIT JANTUNG BAWAAN

Log-Logistic Accelerated Failure Time (AFT) model is survival analysis that is used when the survival time follows Log-Logistic distribution. Log-Logistic AFT model can be used to estimate survival time, survival function, and hazard function. Log-Logistic AFT model was formed by regressing covariates linierly against the log of survival time. Regression coefficients are estimated using maximum likelihood method. This study uses data from Atrial Septal Defect (ASD) patients, which is a congenital disease with a hole in the wall that separates the top of two chambers of the heart by using sensor type III. Survival time as the response variable, that is the time from patient was diagnosed with ASD until the first relapse and uses age, gender, treatment status (catheterization/surgery), defect size that is the size of the hole in the heart terrace, pulmonary hypertension status, and pain status as predictor variables. The result showed that variable gender, treatment status, defect size, pulmonary hypertension status, and pain status affect the first recurrence of ASD patients, so it is found that category of female, untreated patient, defect size ≥12mm, having pulmonary hypertension, having chest pain tend to have first recurrence sooner than the other category.

Survival Analysis of Duration of exclusive breastfeeding in Ethiopia: Comparison of Proportional Hazard model and Accelerated Failure Time models

Introduction Early cessation of EBF has the short and long term effect for the welfare of infants including the life-long impacts of poor school performance, reduced productivity, and impaired intellectual development. Objective of the study: the main objective of this study was to compare the performance of CPH model and AFT models in analyzing EBF data in Ethiopia, 2016 EDHS. Specifically, the study aimed to identify the major predictor variables of the duration of EBF based on 2016 EDHS data.Methodology: The secondary data is obtained from Ethiopian Demographic and Health Survey (EDHS), 2016. The outcome variable of this study was the duration of EBF in month. To achieve the objective of the study, descriptive survival analysis like the median survival time, Kaplan Meier survival estimate and log-rank test were used to compare the estimated survival probability among different levels of predictor variables at 5 percent significant level. The Cox proportional hazard regression and Accelerated failure time model were fitted and their results were compared using model comparison criterion such as AIC, BIC.Results: of 1092 interviewed mothers, 15.3 % of them were discontinued EBF and 84.7% of them were exclusively breastfed (censored). The estimated median duration of EBF was 3 months. Based on estimated Kaplan Meier survival curve and log-rank test, it was found that there was a statistically significant difference in survivor experience of discontinuing EBF over each duration with respect to place of delivery, maternal education, husband education, mode of delivery and employment status. The fitted CPH and AFT model indicated that mode of delivery, wealth index, and employment status was found as significant predictors of EBF duration. Moreover, comparatively Weibull AFT model performed better in analyzing EBF data. According to the fitted model, mothers who were in poor wealth index category and who gave birth by cesarean shortens the duration of EBF by 16% and 29% respectively. On the other hand, employed mothers were improved the duration of EBF by 26%. Conclusion: Weibull AFT model is performed better in analyzing EBF data. A mother who was unemployed, poor wealth index, and gave birth by cesarean shortens the duration of EBF than their counterparts. Therefore, special emphasis should be given for mothers who are unemployed, who are economically poor, and give birth by cesarean to improve the duration of EBF.

Generalized Topp-Leone-Weibull AFT Modelling: A Bayesian Analysis with MCMC Tools using R and Stan

The generalized Topp-Leone-Weibull (GTL-W) distribution is a generalization of Weibull distribution which is obtained by using generalized Topp-Leone (GTL) distribution as a generator and considering Weibull distribution as a baseline distribution. Weibull distribution is a widely used survival model that has monotone- increasing or decreasing hazard. But it cannot accommodate bathtub shaped and unimodal shaped hazards. As a survival model, GTL-W distribution is more flexible than the Weibull distribution to accommodate different types of hazards. The present study aims at fitting GTL-W model as an accelerated failure time (AFT) model to censored survival data under Bayesian setting using R and Stan languages. The GTL-W AFT model is compared with its sub-model and the baseline model. The Bayesian model selection criteria LOOIC and WAIC are applied to select the best model.

Simulating sedimentary burial cycles – Part 2: Elemental-based multikinetic apatite fission-track interpretation and modelling techniques illustrated using examples from northern Yukon

Compositionally dependent apatite fission track (AFT) annealing is a common but underappreciated cause for age dispersion in detrital AFT samples. We present an interpretation and modelling strategy that exploits multikinetic AFT annealing to obtain thermal histories that can provide more detail and better resolution compared to conventional methods. We illustrate our method using a Permian and a Devonian sample from the Yukon, Canada, both with complicated geological histories and long residence times in the AFT partial annealing zone. Effective Cl values (eCl; converted from rmr0 values), derived from detailed apatite elemental data, are used to define AFT statistical kinetic populations with significantly different total annealing temperatures (~110–245 °C) and ages that agree closely with the results of age mixture modelling. These AFT populations are well-resolved using eCl values but exhibit significant overlap with respect to the conventional parameters, Cl content or Dpar. Elemental analyses and measured Dpar for Phanerozoic samples from the Yukon and Northwest Territories confirm that Dpar has low precision and that Cl content alone cannot account for the compositional and associated kinetic variability observed in natural samples. An inverse multikinetic AFT model, AFTINV, is used to obtain thermal history information by simultaneously modelling multiple kinetic populations as distinct thermochronometers with different temperature sensitivities. A nondirected Monte Carlo scheme generates a set of statistically acceptable solutions at the 0.05 significance level and then these solutions are updated to the 0.5 level using a controlled random search (CRS) learning algorithm. The smoother, closer-fitting CRS solutions allow for a more consistent assessment of the eCl values and thermal history styles that are needed to satisfy the AFT data. The high-quality Devonian sample (39 single grain ages and 202 track lengths) has two kinetic populations that require three cycles of heating and cooling (each subsequent event of lower intensity) to obtain close-fitting solutions. The younger and more westerly Permian sample with three kinetic populations only records the latter two heating events. These results are compatible with known stratigraphic and thermal maturity constraints and the QTQt software produces similar results. Model results for these and other samples suggest that elemental-derived eCl values are accurate within the range, 0–0.25 apfu (rmr0 values of 0.73–0.84), which encompasses most of the data from annealing experiments. Outside of this range, eCl values for more exotic compositions may require adjustment relative to better constrained apatite compositions when trying to fit multiple kinetic populations. Our results for natural and synthetic samples suggest that an element-based multikinetic approach has great potential to increase the temperature range and resolution of thermal histories dramatically relative to conventional AFT thermochronology.

Modeling the recovery time of patients with coronavirus disease 2019 using an accelerated failure time model

Objective To identify factors associated with recovery time from coronavirus disease 2019 (COVID-19). Methods In this retrospective study, data for patients with COVID-19 were obtained between 21 June and 30 August 2020. An accelerated failure time (AFT) model was used to identify covariates associated with recovery time (days from hospital admission to discharge). AFT models with different distributions (exponential, log-normal, Weibull, generalized gamma, and log-logistic) were generated. Akaike’s information criterion (AIC) was used to identify the most suitable model. Results A total of 730 patients with COVID-19 were included (92.5% recovered and 7.5% censored). Based on its low AIC value, the log-logistic AFT model was the best fit for the data. The covariates affecting length of hospital stay were oxygen saturation, lactate dehydrogenase, neutrophil-lymphocyte ratio, D-dimer, ferritin, creatinine, total leucocyte count, age > 80 years, and coronary artery disease. Conclusions The log-logistic AFT model accurately described the recovery time of patients with COVID-19.

The impact of inventory productivity on new venture survival

PurposeThis paper explores the relationship between inventory productivity and the likelihood of venture survival and then examines how financial constraints moderate the inventory productivity–survival linkage.Design/methodology/approachAccelerated failure time (AFT) model is employed to study the link between inventory productivity and venture survival by using small- and medium-sized enterprise (SME) data from Chinese Annual Survey of Industrial Firms (CASIF) database over the period 1999–2007.FindingsThe paper demonstrates a converse U-curve relation between inventory productivity and venture survival. Additionally, financial constraints as the moderator weaken the marginal effect of inventory productivity on venture survival.Practical implicationsManagers should pay more attention to the important inventory performance indicator: inventory productivity. In the context of prominent financing difficulties, managers should be rapid to adjust the competitive strategy and optimize the internal production process according to the inherent nature of risks in a friction environment, and thus generate resources that enterprises cannot raise in the financial market.Originality/valueThis study may be the first to practically investigate the role of inventory productivity on venture survival and the moderating effect of financing constraints on this relationship. It adds to abundant articles as regards the interface between operation management and venture survival by exploring how financial constraints moderate the inventory productivity–survival linkage.

Prevalence of premature ovarian insufficiency and its determinants in Iranian populations: Tehran lipid and glucose study

Background Premature ovarian insufficiency (POI) considered as a concerning health issue for women of reproductive age. In this study we aim to estimate the prevalence of POI and assessing the influential factors. Methods Data was obtained from Tehran lipid and glucose study (TLGS). All eligible post-menarcheal female participants of the TLGS, ages 20–65, were recruited (n = 6521). Participants were followed for the event of menopause, and age at menopause was recorded. Kaplan Meier analysis was applied to estimate mean and median for age at menopause. Weibull accelerated failure time survival regression model (AFT), was applied to assess influential determinants of POI. Conditional probability approach was used to provide estimation for prevalence of POI. Results In this population-based study, the prevalence of POI (menopause age < 40 years) and early menopause (menopause age < 45 years) were estimated 3.5% and 24.6%, respectively. AFT model showed that in comparison to normal weight women, time to menopause was decreased by − 0.09 year (95% CI − 0.27, − 0.01, p = 0.023) and − 0.03 year (95% CI − 0.05, − 0.02, p = 0.000) in underweight and overweight women, respectively. Moreover, time to natural menopause was increased by 0.12 year (95% CI 0.07 to 0.17, p = 0.000) in women used oral contraceptives for > 6 months. Conclusion About one quartile of Iranian women experienced menopause at an age less than 45, especially the non-normal weight ones; this high prevalence is a critical public health concerns that needs to be addressed by health policy makers.