122 related articles for article (PubMed ID: 35656809)
1. Bias and Type I error Control in Correcting Treatment Effect for Treatment Switching Using Marginal Structural Models in Phase III Oncology Trials.
Xu J; Liu G; Wang B
J Biopharm Stat; 2022 Nov; 32(6):897-914. PubMed ID: 35656809
[TBL] [Abstract][Full Text] [Related]
2. Improved two-stage estimation to adjust for treatment switching in randomised trials: g-estimation to address time-dependent confounding.
Latimer NR; White IR; Tilling K; Siebert U
Stat Methods Med Res; 2020 Oct; 29(10):2900-2918. PubMed ID: 32223524
[TBL] [Abstract][Full Text] [Related]
3. The net benefit for time-to-event outcome in oncology clinical trials with treatment switching.
Fukuda M; Sakamaki K; Oba K
Clin Trials; 2023 Dec; 20(6):670-680. PubMed ID: 37455538
[TBL] [Abstract][Full Text] [Related]
4. Missing confounding data in marginal structural models: a comparison of inverse probability weighting and multiple imputation.
Moodie EE; Delaney JA; Lefebvre G; Platt RW
Int J Biostat; 2008; 4(1):Article 13. PubMed ID: 22462119
[TBL] [Abstract][Full Text] [Related]
5. Quantitative Bias Analysis for a Misclassified Confounder: A Comparison Between Marginal Structural Models and Conditional Models for Point Treatments.
Nab L; Groenwold RHH; van Smeden M; Keogh RH
Epidemiology; 2020 Nov; 31(6):796-805. PubMed ID: 32826524
[TBL] [Abstract][Full Text] [Related]
6. Two-stage estimation to adjust for treatment switching in randomised trials: a simulation study investigating the use of inverse probability weighting instead of re-censoring.
Latimer NR; Abrams KR; Siebert U
BMC Med Res Methodol; 2019 Mar; 19(1):69. PubMed ID: 30935369
[TBL] [Abstract][Full Text] [Related]
7. Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models.
Keogh RH; Gran JM; Seaman SR; Davies G; Vansteelandt S
Stat Med; 2023 Jun; 42(13):2191-2225. PubMed ID: 37086186
[TBL] [Abstract][Full Text] [Related]
8. Joint calibrated estimation of inverse probability of treatment and censoring weights for marginal structural models.
Yiu S; Su L
Biometrics; 2022 Mar; 78(1):115-127. PubMed ID: 33247594
[TBL] [Abstract][Full Text] [Related]
9. Correcting for Measurement Error in Time-Varying Covariates in Marginal Structural Models.
Kyle RP; Moodie EE; Klein MB; Abrahamowicz M
Am J Epidemiol; 2016 Aug; 184(3):249-58. PubMed ID: 27416840
[TBL] [Abstract][Full Text] [Related]
10. Adjusting for treatment switching in randomised controlled trials - A simulation study and a simplified two-stage method.
Latimer NR; Abrams KR; Lambert PC; Crowther MJ; Wailoo AJ; Morden JP; Akehurst RL; Campbell MJ
Stat Methods Med Res; 2017 Apr; 26(2):724-751. PubMed ID: 25416688
[TBL] [Abstract][Full Text] [Related]
11. Missing Data in Marginal Structural Models: A Plasmode Simulation Study Comparing Multiple Imputation and Inverse Probability Weighting.
Liu SH; Chrysanthopoulou SA; Chang Q; Hunnicutt JN; Lapane KL
Med Care; 2019 Mar; 57(3):237-243. PubMed ID: 30664611
[TBL] [Abstract][Full Text] [Related]
12. The inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic: an unbiased estimator in the presence of independent censoring.
Dong G; Mao L; Huang B; Gamalo-Siebers M; Wang J; Yu G; Hoaglin DC
J Biopharm Stat; 2020 Sep; 30(5):882-899. PubMed ID: 32552451
[TBL] [Abstract][Full Text] [Related]
13. Simulating longitudinal data from marginal structural models using the additive hazard model.
Keogh RH; Seaman SR; Gran JM; Vansteelandt S
Biom J; 2021 Oct; 63(7):1526-1541. PubMed ID: 33983641
[TBL] [Abstract][Full Text] [Related]
14. Adaptive truncated weighting for improving marginal structural model estimation of treatment effects informally censored by subsequent therapy.
Bai X; Liu J; Li L; Faries D
Pharm Stat; 2015; 14(6):448-54. PubMed ID: 26436533
[TBL] [Abstract][Full Text] [Related]
15. On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects.
Blanche PF; Holt A; Scheike T
Lifetime Data Anal; 2023 Apr; 29(2):441-482. PubMed ID: 35799026
[TBL] [Abstract][Full Text] [Related]
16. Super learning to hedge against incorrect inference from arbitrary parametric assumptions in marginal structural modeling.
Neugebauer R; Fireman B; Roy JA; Raebel MA; Nichols GA; O'Connor PJ
J Clin Epidemiol; 2013 Aug; 66(8 Suppl):S99-109. PubMed ID: 23849160
[TBL] [Abstract][Full Text] [Related]
17. Marginal Structural Models for Life-Course Theories and Social Epidemiology: Definitions, Sources of Bias, and Simulated Illustrations.
Gilsanz P; Young JG; Glymour MM; Tchetgen Tchetgen EJ; Eng CW; Koenen KC; Kubzansky LD
Am J Epidemiol; 2022 Jan; 191(2):349-359. PubMed ID: 34668974
[TBL] [Abstract][Full Text] [Related]
18. Adjustment for treatment changes in epilepsy trials: A comparison of causal methods for time-to-event outcomes.
Dodd S; Williamson P; White IR
Stat Methods Med Res; 2019 Mar; 28(3):717-733. PubMed ID: 29117780
[TBL] [Abstract][Full Text] [Related]
19. Dynamic marginal structural modeling to evaluate the comparative effectiveness of more or less aggressive treatment intensification strategies in adults with type 2 diabetes.
Neugebauer R; Fireman B; Roy JA; O'Connor PJ; Selby JV
Pharmacoepidemiol Drug Saf; 2012 May; 21 Suppl 2():99-113. PubMed ID: 22552985
[TBL] [Abstract][Full Text] [Related]
20. Fitting marginal structural models: estimating covariate-treatment associations in the reweighted data set can guide model fitting.
Pullenayegum EM; Lam C; Manlhiot C; Feldman BM
J Clin Epidemiol; 2008 Sep; 61(9):875-81. PubMed ID: 18486447
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
