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Data Analysis with Competing Risks and Intermediate States explains
when and how to use models and techniques for the analysis of
competing risks and intermediate states. It covers the most recent
insights on estimation techniques and discusses in detail how to
interpret the obtained results. After introducing example studies
from the biomedical and epidemiological fields, the book formally
defines the concepts that play a role in analyses with competing
risks and intermediate states. It addresses nonparametric
estimation of the relevant quantities. The book then shows how to
use a stacked data set that offers great flexibility in the
modeling of covariable effects on the transition rates between
states. It also describes three ways to quantify effects on the
cumulative scale. Each chapter includes standard exercises that
reflect on the concepts presented, a section on software that
explains options in SAS and Stata and the functionality in the R
program, and computer practicals that allow readers to practice
with the techniques using an existing data set of bone marrow
transplant patients. The book's website provides the R code for the
computer practicals along with other material. For researchers with
some experience in the analysis of standard time-to-event data,
this practical and thorough treatment extends their knowledge and
skills to the competing risks and multi-state settings. Researchers
from other fields can also easily translate individuals and
diseases to units and phenomena from their own areas.
Data Analysis with Competing Risks and Intermediate States explains
when and how to use models and techniques for the analysis of
competing risks and intermediate states. It covers the most recent
insights on estimation techniques and discusses in detail how to
interpret the obtained results. After introducing example studies
from the biomedical and epidemiological fields, the book formally
defines the concepts that play a role in analyses with competing
risks and intermediate states. It addresses nonparametric
estimation of the relevant quantities. The book then shows how to
use a stacked data set that offers great flexibility in the
modeling of covariable effects on the transition rates between
states. It also describes three ways to quantify effects on the
cumulative scale. Each chapter includes standard exercises that
reflect on the concepts presented, a section on software that
explains options in SAS and Stata and the functionality in the R
program, and computer practicals that allow readers to practice
with the techniques using an existing data set of bone marrow
transplant patients. The book's website provides the R code for the
computer practicals along with other material. For researchers with
some experience in the analysis of standard time-to-event data,
this practical and thorough treatment extends their knowledge and
skills to the competing risks and multi-state settings. Researchers
from other fields can also easily translate individuals and
diseases to units and phenomena from their own areas.
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