Journal article
Modeling time-varying exposure using inverse probability of treatment weights
- Abstract:
-
For estimating the causal effect of treatment exposure on the occurrence of adverse events, inverse pro bability weights (IPW) can be used in marginal structural models to correct for time-dependent confounding. The R package ipw allows IPW estimation by modeling the relationship between the exposure and confounders via several regression models, among which is the Cox model. For right-censored data and time-dependent exposures such as treatment switches, the ipw package allows a singl...
Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Authors
Funding
French National Agency for Medicines and Health Products Safety
More from this funder
Bibliographic Details
- Publisher:
- Wiley Publisher's website
- Journal:
- Biometrical Journal Journal website
- Volume:
- 60
- Issue:
- 2
- Pages:
- 323-332
- Publication date:
- 2017-12-17
- Acceptance date:
- 2017-10-30
- DOI:
- EISSN:
-
1521-4036
- ISSN:
-
0323-3847
Item Description
- Keywords:
- Pubs id:
-
pubs:829605
- UUID:
-
uuid:8c5c3021-4975-473d-83d6-9fa120702124
- Local pid:
- pubs:829605
- Source identifiers:
-
829605
- Deposit date:
- 2018-03-16
Terms of use
- Copyright holder:
- WILEY‐VCH Verlag GmbH and Co
- Copyright date:
- 2017
- Notes:
- © 2017 WILEY‐VCH Verlag GmbH and Co. KGaA, Weinheim. This is the accepted manuscript version of the article. The final version is available online from Wiley at: https://doi.org/10.1002/bimj.201600223
Metrics
If you are the owner of this record, you can report an update to it here: Report update to this record