242 related articles for article (PubMed ID: 34110942)
1. Non-parametric approach for frequentist multiple imputation in survival analysis with missing covariates.
Takeuchi Y; Ogawa M; Hagiwara Y; Matsuyama Y
Stat Methods Med Res; 2021 Jul; 30(7):1691-1707. PubMed ID: 34110942
[TBL] [Abstract][Full Text] [Related]
2. Multiple imputation for handling missing outcome data when estimating the relative risk.
Sullivan TR; Lee KJ; Ryan P; Salter AB
BMC Med Res Methodol; 2017 Sep; 17(1):134. PubMed ID: 28877666
[TBL] [Abstract][Full Text] [Related]
3. A bias-corrected estimator in multiple imputation for missing data.
Tomita H; Fujisawa H; Henmi M
Stat Med; 2018 Oct; 37(23):3373-3386. PubMed ID: 29845646
[TBL] [Abstract][Full Text] [Related]
4. A comparison of multiple imputation and fully augmented weighted estimators for Cox regression with missing covariates.
Qi L; Wang YF; He Y
Stat Med; 2010 Nov; 29(25):2592-604. PubMed ID: 20806403
[TBL] [Abstract][Full Text] [Related]
5. Cox regression analysis with missing covariates via nonparametric multiple imputation.
Hsu CH; Yu M
Stat Methods Med Res; 2019 Jun; 28(6):1676-1688. PubMed ID: 29717943
[TBL] [Abstract][Full Text] [Related]
6. Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: a simulation study.
De Silva AP; Moreno-Betancur M; De Livera AM; Lee KJ; Simpson JA
BMC Med Res Methodol; 2019 Jan; 19(1):14. PubMed ID: 30630434
[TBL] [Abstract][Full Text] [Related]
7. Estimating excess hazard ratios and net survival when covariate data are missing: strategies for multiple imputation.
Falcaro M; Nur U; Rachet B; Carpenter JR
Epidemiology; 2015 May; 26(3):421-8. PubMed ID: 25774607
[TBL] [Abstract][Full Text] [Related]
8. Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study.
Marshall A; Altman DG; Royston P; Holder RL
BMC Med Res Methodol; 2010 Jan; 10():7. PubMed ID: 20085642
[TBL] [Abstract][Full Text] [Related]
9. Fitting additive hazards models for case-cohort studies: a multiple imputation approach.
Jung J; Harel O; Kang S
Stat Med; 2016 Jul; 35(17):2975-90. PubMed ID: 26194861
[TBL] [Abstract][Full Text] [Related]
10. A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis.
Jahangiri M; Kazemnejad A; Goldfeld KS; Daneshpour MS; Mostafaei S; Khalili D; Moghadas MR; Akbarzadeh M
BMC Med Res Methodol; 2023 Jul; 23(1):161. PubMed ID: 37415114
[TBL] [Abstract][Full Text] [Related]
11. Dealing with missing information on covariates for excess mortality hazard regression models - Making the imputation model compatible with the substantive model.
Antunes L; Mendonça D; Bento MJ; Njagi EN; Belot A; Rachet B
Stat Methods Med Res; 2021 Oct; 30(10):2256-2268. PubMed ID: 34473604
[TBL] [Abstract][Full Text] [Related]
12. Multiple imputation in Cox regression when there are time-varying effects of covariates.
Keogh RH; Morris TP
Stat Med; 2018 Nov; 37(25):3661-3678. PubMed ID: 30014575
[TBL] [Abstract][Full Text] [Related]
13. A comparison of multiple imputation methods for missing data in longitudinal studies.
Huque MH; Carlin JB; Simpson JA; Lee KJ
BMC Med Res Methodol; 2018 Dec; 18(1):168. PubMed ID: 30541455
[TBL] [Abstract][Full Text] [Related]
14. Multiple imputation of missing data under missing at random: compatible imputation models are not sufficient to avoid bias if they are mis-specified.
Curnow E; Carpenter JR; Heron JE; Cornish RP; Rach S; Didelez V; Langeheine M; Tilling K
J Clin Epidemiol; 2023 Aug; 160():100-109. PubMed ID: 37343895
[TBL] [Abstract][Full Text] [Related]
15. Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model.
Bartlett JW; Seaman SR; White IR; Carpenter JR;
Stat Methods Med Res; 2015 Aug; 24(4):462-87. PubMed ID: 24525487
[TBL] [Abstract][Full Text] [Related]
16. A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: a simulation study.
De Silva AP; Moreno-Betancur M; De Livera AM; Lee KJ; Simpson JA
BMC Med Res Methodol; 2017 Jul; 17(1):114. PubMed ID: 28743256
[TBL] [Abstract][Full Text] [Related]
17. Propensity score analysis with partially observed covariates: How should multiple imputation be used?
Leyrat C; Seaman SR; White IR; Douglas I; Smeeth L; Kim J; Resche-Rigon M; Carpenter JR; Williamson EJ
Stat Methods Med Res; 2019 Jan; 28(1):3-19. PubMed ID: 28573919
[TBL] [Abstract][Full Text] [Related]
18. A fair comparison of tree-based and parametric methods in multiple imputation by chained equations.
Slade E; Naylor MG
Stat Med; 2020 Apr; 39(8):1156-1166. PubMed ID: 31997388
[TBL] [Abstract][Full Text] [Related]
19. Multiple imputation with sequential penalized regression.
Zahid FM; Heumann C
Stat Methods Med Res; 2019 May; 28(5):1311-1327. PubMed ID: 29451087
[TBL] [Abstract][Full Text] [Related]
20. Handling missing data in matched case-control studies using multiple imputation.
Seaman SR; Keogh RH
Biometrics; 2015 Dec; 71(4):1150-9. PubMed ID: 26237003
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]