title: Nonparametric and semiparametric trend analysis for stratified recurrence times.
abstract: Recurrent event data are frequently encountered in longitudinal follow-up studies when the occurrences of multiple events are considered as the major outcomes. Suppose that the recurrent events are of the same type and the variable of interest is the recurrence time between successive events. In many applications, the distributional pattern of recurrence times can be used as an index for the progression of a disease. Such a distributional pattern is important for understanding the natural history of a disease or for confirming long-term treatment effect. In this article, we discuss and define the comparability of recurrence times. Nonparametric and semiparametric methods are developed for testing trend of recurrence time distributions and estimating trend parameters in regression models. The construction of the methods is based on comparable recurrence times from stratified data. A real data example is presented to illustrate the use of methodology.
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205, USA. mcwang@jhsph.edu
authors.forename: M C
authors.initials: MC
authors.lastname: Wang
authors.forename: Y Q
authors.initials: YQ
authors.lastname: Chen
mesh_terms: D001699:Biometry / Q000379:methods*; D018450:Disease Progression; D005500:Follow-Up Studies; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D012008:Recurrence; D018709:Statistics, Nonparametric; D013997:Time Factors; D016896:Treatment Outcome