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A regression model for the conditional probability of a competing event: application to monoclonal gammopathy of unknown significance

Authors


Arthur Allignol, Freiburg Center for Data Analysis and Modelling, University of Freiburg, Eckerstrasse 1, Freiburg 79104, Germany.
E-mail: arthur.allignol@fdm.uni-freiburg.de

Abstract

Summary.  Competing risks are classically summarized by the cause-specific hazards and the cumulative incidence function. To obtain a full understanding of the competing risks, these identifiable quantities should be viewed simultaneously for all events. Another available quantity is the conditional probability of a competing risk, which is defined as the cumulative probability of having failed from a particular cause given that no other (competing) events have occurred. When one event is of a particular interest, this quantity provides useful insights, as it displays a probability adjusted on the other competing events. In certain applications, this interpretation may be preferable to that for the cumulative incidence function in quantifying cause-specific cumulative failure probabilities. The use of the conditional probability has been limited by the lack of a regression modelling strategy. We apply recently developed regression methodology to the conditional probability function and illustrate, by using a data set on patients suffering from monoclonal gammopathy of unknown significance, the insights that are gained from this methodology.

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