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| Recidivism Survival Analysis× | Survival Regression× | |
|---|---|---|
| Fachgebiet≠ | Criminology | Statistik |
| Familie≠ | Survival analysis | Regression model |
| Entstehungsjahr≠ | 1988 | 1980s |
| Urheber≠ | David R. Cox (method); Peter Schmidt & Ann Dryden Witte (criminological application) | Kalbfleisch & Prentice; Cox & Oakes |
| Typ≠ | Time-to-event regression for reoffending | Parametric survival model |
| Wegweisende Quelle≠ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202. DOI ↗ | Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576 |
| Aliasnamen | Time-to-Recidivism Modeling, Recidivism Hazard Modeling, Failure-Time Analysis of Reoffending, Survival Analysis of Reoffending | accelerated failure time model, AFT model, parametric survival model, time-to-event regression |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | Recidivism survival analysis models the time from a release or index event until an individual reoffends, treating reoffending as a time-to-event ('failure') outcome with censoring for those not observed to fail. It applies survival methods — Kaplan-Meier curves, Cox proportional-hazards regression, and split-population models — to answer not just whether someone recidivates but how quickly and what raises or lowers that risk over time. | Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occurred at the end of follow-up) by specifying a parametric distribution for the survival time and estimating covariate effects via maximum likelihood. |
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