105 related articles for article (PubMed ID: 31829432)
1. G-estimation of structural nested restricted mean time lost models to estimate effects of time-varying treatments on a failure time outcome.
Hagiwara Y; Shinozaki T; Matsuyama Y
Biometrics; 2020 Sep; 76(3):799-810. PubMed ID: 31829432
[TBL] [Abstract][Full Text] [Related]
2. Structural Nested Cumulative Failure Time Models to Estimate the Effects of Interventions.
Picciotto S; Hernán MA; Page JH; Young JG; Robins JM
J Am Stat Assoc; 2012; 107(499):. PubMed ID: 24347749
[TBL] [Abstract][Full Text] [Related]
3. Augmented and doubly robust G-estimation of causal effects under a Structural nested failure time model.
Mertens K; Vansteelandt S
Biometrics; 2018 Jun; 74(2):472-480. PubMed ID: 28742252
[TBL] [Abstract][Full Text] [Related]
4. Estimating curvilinear time-varying treatment effects: Combining g-estimation of structural nested mean models with time-varying effect models for longitudinal causal inference.
Loh WW
Psychol Methods; 2024 Feb; ():. PubMed ID: 38358680
[TBL] [Abstract][Full Text] [Related]
5. Within-Person Variability Score-Based Causal Inference: A Two-Step Estimation for Joint Effects of Time-Varying Treatments.
Usami S
Psychometrika; 2023 Dec; 88(4):1466-1494. PubMed ID: 35982380
[TBL] [Abstract][Full Text] [Related]
6. Using generalized linear models to implement g-estimation for survival data with time-varying confounding.
Seaman SR; Keogh RH; Dukes O; Vansteelandt S
Stat Med; 2021 Jul; 40(16):3779-3790. PubMed ID: 33942919
[TBL] [Abstract][Full Text] [Related]
7. Estimation of controlled direct effects in time-varying treatments using structural nested mean models: application to a primary prevention trial for coronary events with pravastatin.
Shinozaki T; Matsuyama Y; Ohashi Y
Stat Med; 2014 Aug; 33(18):3214-28. PubMed ID: 24706589
[TBL] [Abstract][Full Text] [Related]
8. Semiparametric estimation of structural nested mean models with irregularly spaced longitudinal observations.
Yang S
Biometrics; 2022 Sep; 78(3):937-949. PubMed ID: 33870495
[TBL] [Abstract][Full Text] [Related]
9. Adjusting for time-varying confounders in survival analysis using structural nested cumulative survival time models.
Seaman S; Dukes O; Keogh R; Vansteelandt S
Biometrics; 2020 Jun; 76(2):472-483. PubMed ID: 31562652
[TBL] [Abstract][Full Text] [Related]
10. SENSITIVITY ANALYSIS FOR UNMEASURED CONFOUNDING IN COARSE STRUCTURAL NESTED MEAN MODELS.
Yang S; Lok JJ
Stat Sin; 2018 Oct; 28(4):1703-1723. PubMed ID: 30853756
[TBL] [Abstract][Full Text] [Related]
11. Structural Nested Mean Models to Estimate the Effects of Time-Varying Treatments on Clustered Outcomes.
He J; Stephens-Shields A; Joffe M
Int J Biostat; 2015 Nov; 11(2):203-22. PubMed ID: 26115504
[TBL] [Abstract][Full Text] [Related]
12. Fitting Marginal Structural and G-Estimation Models Under Complex Treatment Patterns: Investigating the Association Between De Novo Vitamin D Supplement Use After Breast Cancer Diagnosis and All-Cause Mortality Using Linked Pharmacy Claim and Registry Data.
Madden JM; Leacy FP; Zgaga L; Bennett K
Am J Epidemiol; 2020 Mar; 189(3):224-234. PubMed ID: 31673702
[TBL] [Abstract][Full Text] [Related]
13. Estimating long-term treatment effects in observational data: A comparison of the performance of different methods under real-world uncertainty.
Newsome SJ; Keogh RH; Daniel RM
Stat Med; 2018 Jul; 37(15):2367-2390. PubMed ID: 29671915
[TBL] [Abstract][Full Text] [Related]
14. Structural accelerated failure time models for survival analysis in studies with time-varying treatments.
Hernán MA; Cole SR; Margolick J; Cohen M; Robins JM
Pharmacoepidemiol Drug Saf; 2005 Jul; 14(7):477-91. PubMed ID: 15660442
[TBL] [Abstract][Full Text] [Related]
15. Estimation of the causal effects of time-varying treatments in nested case-control studies using marginal structural Cox models.
Takeuchi Y; Hagiwawa Y; Komukai S; Matsuyama Y
Biometrics; 2024 Jan; 80(1):. PubMed ID: 38465985
[TBL] [Abstract][Full Text] [Related]
16. Time-varying effect moderation using the structural nested mean model: estimation using inverse-weighted regression with residuals.
Almirall D; Griffin BA; McCaffrey DF; Ramchand R; Yuen RA; Murphy SA
Stat Med; 2014 Sep; 33(20):3466-87. PubMed ID: 23873437
[TBL] [Abstract][Full Text] [Related]
17. Semiparametric estimation of structural failure time models in continuous-time processes.
Yang S; Pieper K; Cools F
Biometrika; 2020 Mar; 107(1):123-136. PubMed ID: 33162561
[TBL] [Abstract][Full Text] [Related]
18. Instrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models.
Shi J; Swanson SA; Kraft P; Rosner B; De Vivo I; Hernán MA
BMC Med Res Methodol; 2021 Nov; 21(1):258. PubMed ID: 34823502
[TBL] [Abstract][Full Text] [Related]
19. Analyzing medical costs with time-dependent treatment: The nested g-formula.
Spieker A; Roy J; Mitra N
Health Econ; 2018 Jul; 27(7):1063-1073. PubMed ID: 29663579
[TBL] [Abstract][Full Text] [Related]
20. Relation between three classes of structural models for the effect of a time-varying exposure on survival.
Young JG; Hernán MA; Picciotto S; Robins JM
Lifetime Data Anal; 2010 Jan; 16(1):71-84. PubMed ID: 19894116
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]