A treatment-specific marginal structural Cox model for the effect of treatment discontinuation.
articifial censoring
causal inference
discontinuation
inverse probability weighting
time-dependent confounding
Journal
Pharmaceutical statistics
ISSN: 1539-1612
Titre abrégé: Pharm Stat
Pays: England
ID NLM: 101201192
Informations de publication
Date de publication:
09 2022
09 2022
Historique:
revised:
07
02
2022
received:
23
01
2021
accepted:
13
03
2022
pubmed:
1
4
2022
medline:
20
9
2022
entrez:
31
3
2022
Statut:
ppublish
Résumé
Patients taking a prescribed medication often discontinue their treatment; however, this may negatively impact their health outcomes. If doctors had statistical evidence that discontinuing some prescribed medication shortened, on average, the time to a clinical event (e.g., death), they could use that knowledge to encourage their patients to stay on the prescribed treatment. We describe a treatment-specific marginal structural Cox model for estimation of the causal effect of treatment discontinuation on a survival endpoint. The effect of treatment discontinuation is quantified by the hazard ratio of the event hazard rate had the population followed the regime "take treatment
Identifiants
pubmed: 35357077
doi: 10.1002/pst.2211
pmc: PMC9481666
mid: NIHMS1799136
doi:
Substances chimiques
Anticoagulants
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
988-1004Subventions
Organisme : NHLBI NIH HHS
ID : T32 HL079896
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES031651
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG066883
Pays : United States
Organisme : NCI NIH HHS
ID : P01 CA142538
Pays : United States
Informations de copyright
© 2022 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.
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