Nonparametric change point estimation for survival distributions with a partially constant hazard rate

Alessandra R. Brazzale (), Helmut Küchenhoff (), Stefanie Krügel (), Tobias S. Schiergens (), Heiko Trentzsch () and Wolfgang Hartl ()
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Alessandra R. Brazzale: Università degli Studi di Padova
Helmut Küchenhoff: Ludwig-Maximilians-Universität München
Stefanie Krügel: Ludwig-Maximilians-Universität München
Tobias S. Schiergens: Ludwig-Maximilians-Universität München
Heiko Trentzsch: Ludwig-Maximilians-Universität München
Wolfgang Hartl: Ludwig-Maximilians-Universität München

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2019, vol. 25, issue 2, No 6, 321 pages

Abstract: Abstract We present a new method for estimating a change point in the hazard function of a survival distribution assuming a constant hazard rate after the change point and a decreasing hazard rate before the change point. Our method is based on fitting a stump regression to p values for testing hazard rates in small time intervals. We present three real data examples describing survival patterns of severely ill patients, whose excess mortality rates are known to persist far beyond hospital discharge. For designing survival studies in these patients and for the definition of hospital performance metrics (e.g. mortality), it is essential to define adequate and objective end points. The reliable estimation of a change point will help researchers to identify such end points. By precisely knowing this change point, clinicians can distinguish between the acute phase with high hazard (time elapsed after admission and before the change point was reached), and the chronic phase (time elapsed after the change point) in which hazard is fairly constant. We show in an extensive simulation study that maximum likelihood estimation is not robust in this setting, and we evaluate our new estimation strategy including bootstrap confidence intervals and finite sample bias correction.

Keywords: Change point; Survival; Hazard rate; ICU; Acute phase (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10985-018-9431-x

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