Landmarking
Analysis using Landmark Models
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. To find out more about the methods in this package, please see https://isobelbarrott.github.io/Landmarking/articles/Landmarking.
- Version1.0.0
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release02/15/2022
Documentation
Team
Isobel Barrott
Jessica Barrett
Show author detailsRolesAuthorRuth Keogh
Show author detailsRolesContributorMichael Sweeting
Show author detailsRolesContributorDavid Stevens
Show author detailsRolesContributor
Insights
Last 30 days
This package has been downloaded 188 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,614 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 23, 2024 with 27 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports8 packages
- Suggests4 packages