Penalised logistic regression and dynamic prediction for discrete-time recurrent event data
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605476179 | ||
| 003 | CHVBK | ||
| 005 | 20210128100355.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20151001xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10985-015-9321-4 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10985-015-9321-4 | ||
| 245 | 0 | 0 | |a Penalised logistic regression and dynamic prediction for discrete-time recurrent event data |h [Elektronische Daten] |c [Entisar Elgmati, Rosemeire Fiaccone, R. Henderson, John Matthews] |
| 520 | 3 | |a 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. | |
| 540 | |a Springer Science+Business Media New York, 2015 | ||
| 690 | 7 | |a Additive model |2 nationallicence | |
| 690 | 7 | |a Event history |2 nationallicence | |
| 690 | 7 | |a Logistic regression |2 nationallicence | |
| 690 | 7 | |a Penalised likelihood |2 nationallicence | |
| 700 | 1 | |a Elgmati |D Entisar |u Department of Statistics, Tripoli University, Tripoli, Libya |4 aut | |
| 700 | 1 | |a Fiaccone |D Rosemeire |u Department of Statistics, Universidade Federal da Bahia, Salvador, Brazil |4 aut | |
| 700 | 1 | |a Henderson |D R. |u School of Mathematics and Statistics, Newcastle University, Newcastle, UK |4 aut | |
| 700 | 1 | |a Matthews |D John |u School of Mathematics and Statistics, Newcastle University, Newcastle, UK |4 aut | |
| 773 | 0 | |t Lifetime Data Analysis |d Springer US; http://www.springer-ny.com |g 21/4(2015-10-01), 542-560 |x 1380-7870 |q 21:4<542 |1 2015 |2 21 |o 10985 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10985-015-9321-4 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10985-015-9321-4 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Elgmati |D Entisar |u Department of Statistics, Tripoli University, Tripoli, Libya |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Fiaccone |D Rosemeire |u Department of Statistics, Universidade Federal da Bahia, Salvador, Brazil |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Henderson |D R. |u School of Mathematics and Statistics, Newcastle University, Newcastle, UK |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Matthews |D John |u School of Mathematics and Statistics, Newcastle University, Newcastle, UK |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Lifetime Data Analysis |d Springer US; http://www.springer-ny.com |g 21/4(2015-10-01), 542-560 |x 1380-7870 |q 21:4<542 |1 2015 |2 21 |o 10985 | ||