caa a22 4500 605476179 CHVBK 20210128100355.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10985-015-9321-4 doi (NATIONALLICENCE)springer-10.1007/s10985-015-9321-4 Penalised logistic regression and dynamic prediction for discrete-time recurrent event data [Elektronische Daten] [Entisar Elgmati, Rosemeire Fiaccone, R. Henderson, John Matthews] We consider methods for the analysis of discrete-time recurrent event data, when interest is mainly in prediction. The Aalen additive model provides an extremely simple and effective method for the determination of covariate effects for this type of data, especially in the presence of time-varying effects and time varying covariates, including dynamic summaries of prior event history. The method is weakened for predictive purposes by the presence of negative estimates. The obvious alternative of a standard logistic regression analysis at each time point can have problems of stability when event frequency is low and maximum likelihood estimation is used. The Firth penalised likelihood approach is stable but in removing bias in regression coefficients it introduces bias into predicted event probabilities. We propose an alterative modified penalised likelihood, intermediate between Firth and no penalty, as a pragmatic compromise between stability and bias. Illustration on two data sets is provided. Springer Science+Business Media New York, 2015 Additive model nationallicence Event history nationallicence Logistic regression nationallicence Penalised likelihood nationallicence Elgmati Entisar Department of Statistics, Tripoli University, Tripoli, Libya aut Fiaccone Rosemeire Department of Statistics, Universidade Federal da Bahia, Salvador, Brazil aut Henderson R. School of Mathematics and Statistics, Newcastle University, Newcastle, UK aut Matthews John School of Mathematics and Statistics, Newcastle University, Newcastle, UK aut Lifetime Data Analysis Springer US; http://www.springer-ny.com 21/4(2015-10-01), 542-560 1380-7870 21:4<542 2015 21 10985 https://doi.org/10.1007/s10985-015-9321-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10985-015-9321-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Elgmati Entisar Department of Statistics, Tripoli University, Tripoli, Libya aut NATIONALLICENCE 700 1- Fiaccone Rosemeire Department of Statistics, Universidade Federal da Bahia, Salvador, Brazil aut NATIONALLICENCE 700 1- Henderson R. School of Mathematics and Statistics, Newcastle University, Newcastle, UK aut NATIONALLICENCE 700 1- Matthews John School of Mathematics and Statistics, Newcastle University, Newcastle, UK aut NATIONALLICENCE 773 0- Lifetime Data Analysis Springer US; http://www.springer-ny.com 21/4(2015-10-01), 542-560 1380-7870 21:4<542 2015 21 10985