This capability may be useful be useful for researchers investigating time-to-failure as part of a preventive maintenance regime, especially when factors such as the location of a physical asset is known to accelerate or decelerate the time-to-failure. Whereas proportional hazards models assume that the effect of a covariate is to multiply the hazard by some constant, an AFT model assumes that the covariate effects accelerate or decelerate survival by some constant. Parametric models are often regarded as less flexible than non-parametric models but if the outcome variable follows an identifiable distribution, these kinds of procedures can be very powerful. This means it is assumed that the dependent variable follows a specific distribution. Unlike the existing Life Tables, Kaplan-Meier and Cox Regression procedures, the newly added Accelerated Failure Time Model is parametric in nature. Version 29 brings a new addition to the SPSS family of Survival analysis procedures. Version 29 introduces some new analysis procedures and includes more recent versions of R and Python Parametric Accelerated Failure Time (AFT) Models In September this year, IBM released the latest version of SPSS Statistics.
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