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All statistical methods involve some basic model assumptions, which if violated render results of the analysis dubious. A solution to such a contingency is to seek an appropriate model or to modify the customary model by introducing additional parameters. Both of these approaches are in general cumbersome and demand uncommon expertise. An alternative is to transform the data to achieve compatibility with a well understood and convenient customary model with readily available software. A well known example is the Box-Cox data transformation developed in order to make the normal theory linear model usable even when the assumptions of normality and homoscedasticity are not met.
In the lifetime data analysis including survival data; model appropriateness is determined by the nature of the hazard function and the well known Weibull distribution is the most employed model for the purpose. However, this model, which allows only a small spectrum of monotone hazard rates, is especially inappropriate if the data indicate unimodal or bathtub shaped hazard rates.
In this presentation we will investigate the use of data transformations for the analysis of lifetime data which includes survival
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