Barrott, I, Barrett, J, Keogh, R, Sweeting, M and Stevens, D. 2022. Landmarking: Analysis using Landmark Models. [Online]. Comprehensive R Archive Network. Available from: https://cran.r-project.org/package=Landmarking
Barrott, I, Barrett, J, Keogh, R, Sweeting, M and Stevens, D. Landmarking: Analysis using Landmark Models [Internet]. Comprehensive R Archive Network; 2022. Available from: https://cran.r-project.org/package=Landmarking
Barrott, I, Barrett, J, Keogh, R, Sweeting, M and Stevens, D (2022). Landmarking: Analysis using Landmark Models. [Data Collection]. Comprehensive R Archive Network. https://cran.r-project.org/package=Landmarking
Description
The landmark approach allows survival predictions to be updated dynamically as new measurements from an individual are recorded. The idea is to set predefined time points, known as "landmark times", and form a model at each landmark time using only the individuals in the risk set. This package allows the longitudinal data to be modelled either using the last observation carried forward or linear mixed effects modelling. There is also the option to model competing risks, either through cause-specific Cox regression or Fine-Gray regression.
Keywords
| Data capture method | Other |
|---|---|
| Date (Date published in a 3rd party system) | 15 February 2022 |
| Language(s) of written materials | English |
| Data Creators | Barrott, I, Barrett, J, Keogh, R, Sweeting, M and Stevens, D |
|---|---|
| LSHTM Faculty/Department | Faculty of Epidemiology and Population Health > Dept of Medical Statistics |
| Participating Institutions | London School of Hygiene & Tropical Medicine, London, United Kingdom |
| Date Deposited | 12 Feb 2020 14:34 |
|---|---|
| Last Modified | 15 Aug 2024 14:50 |
| Publisher | Comprehensive R Archive Network |
