Description

This book aims to promote regression methods for analyzing lifetime (or time-to-event) data that are based on a representation of the underlying process, and are therefore likely to offer greater scientific insight compared to purely empirical methods.

In contrast to the rich statistical literature, the regression methods actually employed in lifetime data analysis are limited, particularly in the biomedical field where D. R. Cox's famous semi-parametric proportional hazards model predominates. Practitioners should become familiar with more flexible models. The first hitting time regression models (or threshold regression) presented here represent observed events as the outcome of an underlying stochastic process. One example is death occurring when the patient's health status falls to zero, but the idea has wide applicability - in biology, engineering, banking and finance, and elsewhere. The central topic is the model based on an underlying Wiener process, leading to lifetimes following the inverse Gaussian distribution. Introducing time-varying covariates and many other extensions are considered. Various applications are presented in detail.

• When a mystery virus turns Scrooge's feathers blue, he treks to South America in search of the "Healers of the Andes" — legendary aborigines with a cure for everything. But following Scrooge is the ruthless Argus McSwine, whose quack medicine sales depend on no cures ever being found! Next, Tony Strobl's "Saga of the Debit and Credit Ledger" finds Donald trying to win Scrooge's favor on a stormy coastline treasure hunt. And in Daniel Branca's "Sub-Sub-Zero," a rare stamp search lands the Ducks in trouble with creepy sea-going nomads... and arch-rival Flintheart Glomgold!

1. Introduction to Lifetime Data and Regression Models.

2. First Hitting Time Regression Models.

3. Model Fitting and Diagnostics.

4. Extensions to Inverse Gaussian First Hitting Time Regression Models.

5. Relationship of First Hitting Time? Models to Proportional Hazards and Accelerated Failure Time Models.

6. Applications.

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