scholarly journals Using the Weibull accelerated failure time regression model to predict time to health events

2018 ◽  
Author(s):  
Enwu Liu ◽  
Karen Lim
Keyword(s):  
Regression Model ◽  
Failure Time ◽  
Prediction Method ◽  
Accelerated Failure Time ◽  
Related Event ◽  
Health Events ◽  
Aft Model ◽  
Worked Example ◽  
Health Related ◽  
Accelerated Failure

AbstractWe describe a statistical method protocol to use a Weibull accelerated failure time (AFT) model to predict time to a health-related event. This prediction method is quite common in engineering reliability research but rarely used for medical predictions such as survival time. A worked example for how to perform the prediction using a published dataset is included.

Efficient parameter estimation for multivariate accelerated failure time model via the quadratic inference functions method

2019 ◽  
Vol 08 (04) ◽  
pp. 1950013
Author(s):  
Liya Fu ◽  
Zhuoran Yang ◽  
Mingtao Zhao ◽  
Yan Zhou
Keyword(s):  
Correlation Matrix ◽  
Marginal Distribution ◽  
Failure Time ◽  
Correlation Structure ◽  
Accelerated Failure Time Model ◽  
Accelerated Failure Time ◽  
Aft Model ◽  
Accelerated Failure ◽  
Correlation Parameters ◽  
Quadratic Inference Functions

A popular approach, generalized estimating equations (GEE), has been applied to the multivariate accelerated failure time (AFT) model of the clustered and censored data. However, this method needs to estimate the correlation parameters and calculate the inverse of the correlation matrix. Meanwhile, the efficiency of the parameter estimators is low when the correlation structure is misspecified and/or the marginal distribution is heavy-tailed. This paper proposes using the quadratic inference functions (QIF) with a mixture correlation structure to estimate the coefficients in the multivariate AFT model, which can avoid estimating the correlation parameters and computing the inverse matrix of the correlation matrix. Moreover, the estimator derived from the QIF is consistent and asymptotically normal. Simulation studies indicate that the proposed method outperforms the method based on GEE when the marginal distribution has a heavy tail. Finally, the proposed method is used to analyze a real dataset for illustration.

scholarly journals COMPARISON OF STRATIFIED WEIBULL MODEL AND WEIBULL ACCELERATED FAILURE TIME (AFT) MODEL IN THE ANALYSIS OF CERVICAL CANCER SURVIVAL

2016 ◽  
Vol 78 (6-4) ◽  
Author(s):  
Nurliyana Juhan ◽  
Nuradhiathy Abd Razak ◽  
Yong Zulina Zubairi ◽  
Nyi Nyi Naing ◽  
Che Haziqah Che Hussin ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Survival Time ◽  
Median Survival ◽  
Distant Metastasis ◽  
Median Survival Time ◽  
Failure Time ◽  
Weibull Model ◽  
Accelerated Failure Time ◽  
Aft Model ◽  
Accelerated Failure

Cervical cancer is the fourth most common cancer affecting women worldwide, after breast, colorectal, and lung cancers with 528 000 new cases every year. It is also the fourth most common cause of cancer death with 266 000 deaths in 2012 among women worldwide. In Malaysia it remains to be a great concern among clinicians; yet published works on survival of cervical cancer patients are somewhat limited. In this study, two survival regression models which are parametric Stratified Weibull model and Weibull Accelerated Failure Time (AFT) model are considered as the alternative and improvement of the well-known Cox proportional hazard model to evaluate the prognostic factor that effect on survival of patients with cervical cancer. Comparisons were made to find the best model. Data were taken from Hospital University Science Malaysia (HUSM) over a period of 12 years. From the analyses it was found that the AFT model was the most appropriate. The AFT model has shown that the median survival time for patient at stage III & IV (14 months) is about one third that of those at stages I & II (40 months) for the same distant metastasis group. While, the median survival time for patient with distant metastasis (17 months) is half that of those without distant metastasis (34 months) for the same stage group.

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

2022 ◽  
Vol 10 (4) ◽  
pp. 518-531
Author(s):  
Dwi Nooriqfina ◽  
Sudarno Sudarno ◽  
Rukun Santoso
Keyword(s):  
Pulmonary Hypertension ◽  
Survival Time ◽  
Failure Time ◽  
Defect Size ◽  
Accelerated Failure Time ◽  
Hypertension Status ◽  
Aft Model ◽  
Pain Status ◽  
Accelerated Failure ◽  
First Recurrence

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. 

scholarly journals Alkaline Water and Longevity: A Murine Study

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Massimiliano Magro ◽  
Livio Corain ◽  
Silvia Ferro ◽  
Davide Baratella ◽  
Emanuela Bonaiuto ◽  
...  
Keyword(s):  
Survival Data ◽  
Failure Time ◽  
Control Group ◽  
Accelerated Failure Time ◽  
Survival Functions ◽  
Alkaline Water ◽  
Aft Model ◽  
Accelerated Failure ◽  
Second Year ◽  
Aging Factor

The biological effect of alkaline water consumption is object of controversy. The present paper presents a 3-year survival study on a population of 150 mice, and the data were analyzed with accelerated failure time (AFT) model. Starting from the second year of life, nonparametric survival plots suggest that mice watered with alkaline water showed a better survival than control mice. Interestingly, statistical analysis revealed that alkaline water provides higher longevity in terms of “deceleration aging factor” as it increases the survival functions when compared with control group; namely, animals belonging to the population treated with alkaline water resulted in a longer lifespan. Histological examination of mice kidneys, intestine, heart, liver, and brain revealed that no significant differences emerged among the three groups indicating that no specific pathology resulted correlated with the consumption of alkaline water. These results provide an informative and quantitative summary of survival data as a function of watering with alkaline water of long-lived mouse models.

scholarly journals Subgroup Identification for Time-to-event Data Based on AFT Model

2019 ◽  
Author(s):  
Sheng-li An ◽  
Pei Kang ◽  
Ying-xin Liu ◽  
Fu-qiang Huang
Keyword(s):  
False Discovery Rate ◽  
Treatment Effect ◽  
Change Point ◽  
Adaptive Design ◽  
Failure Time ◽  
Accelerated Failure Time ◽  
Aft Model ◽  
Subgroup Identification ◽  
False Discovery ◽  
Accelerated Failure

Abstract Considering the problem of identifying subgroup in a randomized clinical trial with respect to survival time, we present an analysis strategy to find subgroup of enhanced treatment effect. We fit univariate accelerated failure time (AFT) models with covariate-treatment interactions to identify predictive covariates. The false discovery rate is controlled by Benjamini-Hochberg procedure. Then a composite score conversion is employed to transform the set of identified covariates for each patient into a univariate score. To classify patient subgroups, a change-point algorithm is applied to searching for the threshold cutoff instead of using the median. Moreover, we adopted a biomarker adaptive design to check whether the treatment effect exists within certain subgroup. The simulation results show that the change-point method is remarkably superior to the median cutoff particularly when the subgroup sizes vary considerably. Furthermore, the 2-stage adaptive design has good power properties in detecting treatment effect while the type I error is generally controlled. As an illustration, we apply the proposed methods to an AIDS study. In conclusion, when the sample size is sufficient and the censoring rate is mild, the AFT model combined with change-point algorithm performs well in identifying subgroup. Keywords: accelerated failure time model; adaptive design; change-point algorithm; false discovery rate; precision medicine; subgroup identification

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