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Research Paper | Statistics | Nepal | Volume 3 Issue 6, June 2014 | Popularity: 6.9 / 10
Accelerated Failure Time Models: An Application in the Survival of Acute Liver Failure Patients in India
Shankar Prasad Khanal, V. Sreenivas, Subrat K. Acharya
Abstract: Accelerated Failure Time (AFT) models can be used for the analysis of time to event data to estimate the effects of covariates on acceleration/deceleration of the survival time. The effect of the covariate is measured through a log-linear model taking logarithm of the survival time as the outcome or dependent variable. Hence; the effect of covariate is multiplicative on time scale; and the results of AFT models may be easier to interpret as the covariate effects are directly expressed in terms of time ratio (TR). Some AFT models are applied to the data on time to death of hospitalized Acute Liver Failure (ALF) patients in All India Institute of Medical Sciences; New Delhi; India to identify the prognostic factors. This type of study is being carried out for the first time in Indian population using retrospective data of ALF patients using AFT models.
Keywords: Accelerated failure time models, acute liver failure, time ratio, time to event data
Edition: Volume 3 Issue 6, June 2014
Pages: 161 - 166
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Statistics, India, Volume 8 Issue 5, May 2019
Pages: 35 - 39The Implementation of Total Quality Management (TQM) as a Function of Improving Quality in Educational Institutions
Rallabandi Srinivasu, G.V.S.R. Anjaneyulu
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Statistics, Kenya, Volume 8 Issue 5, May 2019
Pages: 66 - 69Survival Analysis of Cancer Patients Using Weibull Parametric Model
Wilson Kiprotich Chepkech, Joel Cheruiyot Chelule, Ayubu Anapapa, Herbert Imboga
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Statistics, India, Volume 9 Issue 3, March 2020
Pages: 1201 - 1204Customer Relationship Management (CRM) Viewed by and Analysed by Probability Distribution
Kamlesh L Patel, Dr. Sanjay G Raval, Dr. S R Patel
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Statistics, Congo, Volume 8 Issue 4, April 2019
Pages: 85 - 95Modeling Tax Revenues Using Kernel Approach Case Study: North Kivu Province (Democratic Republic of Congo) Tax Revenues Time Series Forecasting
Byamungu Wanguwabo David