Partial-linear single-index Cox regression models with multiple time-dependent covariates
The PLSI-Cox models allow for the evaluation of nonlinear effects of covariates and offer insights into their relative importance and direction. These methods provide a powerful set of tools for analyzing data with multiple time-dependent covariates and survival outcomes, potentially offering valuable insights for both current and future studies.
Authors
- Andrea B. Troxel (ORCID: https://orcid.org/0000-0002-1393-3075)
- Mengling Liu (ORCID: https://orcid.org/0000-0001-9758-8522)
- Myeonggyun Lee (ORCID: https://orcid.org/0000-0002-0064-2770)
- Anna Nolan (ORCID: https://orcid.org/0000-0002-0631-1171)
- Sophia Kwon (ORCID: https://orcid.org/0000-0003-3639-5107)
- George Crowley (ORCID: https://orcid.org/0000-0002-0473-6934)
- David J. Prezant (ORCID: https://orcid.org/0000-0001-9562-0330)
- Theresa Schwartz (ORCID: https://orcid.org/0000-0001-6606-4072)
- Rachel Zeig‐Owens (ORCID: https://orcid.org/0000-0002-8679-2306)
Institutions
- Albert Einstein College of Medicine (US)
- New York City Fire Department (US)
- Montefiore Medical Center (US)
- New York University (US)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1186/s12874-024-02434-9
- Citations
- 1
- Primary Topic
- Air Quality and Health Impacts
- Type
- article
- Field-Weighted Citation Impact
- 0.31
Funders
- American Thoracic Society
- Centers for Disease Control and Prevention
- National Heart, Lung, and Blood Institute
- National Institute of Environmental Health Sciences
- NIH Clinical Center
- National Institute for Occupational Safety and Health