197 related articles for article (PubMed ID: 29870056)
1. Multiple imputation of missing data in nested case-control and case-cohort studies.
Keogh RH; Seaman SR; Bartlett JW; Wood AM
Biometrics; 2018 Dec; 74(4):1438-1449. PubMed ID: 29870056
[TBL] [Abstract][Full Text] [Related]
2. Using full-cohort data in nested case-control and case-cohort studies by multiple imputation.
Keogh RH; White IR
Stat Med; 2013 Oct; 32(23):4021-43. PubMed ID: 23613433
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. Evaluation of multiple imputation approaches for handling missing covariate information in a case-cohort study with a binary outcome.
Middleton M; Nguyen C; Moreno-Betancur M; Carlin JB; Lee KJ
BMC Med Res Methodol; 2022 Apr; 22(1):87. PubMed ID: 35369860
[TBL] [Abstract][Full Text] [Related]
6. On the use of multiple imputation to address data missing by design as well as unintended missing data in case-cohort studies with a binary endpoint.
Middleton M; Nguyen C; Carlin JB; Moreno-Betancur M; Lee KJ
BMC Med Res Methodol; 2023 Dec; 23(1):287. PubMed ID: 38062377
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. 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]
10. The rise of multiple imputation: a review of the reporting and implementation of the method in medical research.
Hayati Rezvan P; Lee KJ; Simpson JA
BMC Med Res Methodol; 2015 Apr; 15():30. PubMed ID: 25880850
[TBL] [Abstract][Full Text] [Related]
11. Substantive model compatible multilevel multiple imputation: A joint modeling approach.
Quartagno M; Carpenter JR
Stat Med; 2022 Nov; 41(25):5000-5015. PubMed ID: 35959539
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Imputing missing covariates in time-to-event analysis within distributed research networks: A simulation study.
Li D; Wong J; Li X; Toh S; Wang R
Pharmacoepidemiol Drug Saf; 2023 Mar; 32(3):330-340. PubMed ID: 36380400
[TBL] [Abstract][Full Text] [Related]
14. Doubly Robust Nonparametric Multiple Imputation for Ignorable Missing Data.
Long Q; Hsu CH; Li Y
Stat Sin; 2012; 22():149-172. PubMed ID: 22347786
[TBL] [Abstract][Full Text] [Related]
15. Evaluation of approaches for multiple imputation of three-level data.
Wijesuriya R; Moreno-Betancur M; Carlin JB; Lee KJ
BMC Med Res Methodol; 2020 Aug; 20(1):207. PubMed ID: 32787781
[TBL] [Abstract][Full Text] [Related]
16. Missing data and imputation: a practical illustration in a prognostic study on low back pain.
Vergouw D; Heymans MW; van der Windt DA; Foster NE; Dunn KM; van der Horst HE; de Vet HC
J Manipulative Physiol Ther; 2012 Jul; 35(6):464-71. PubMed ID: 22964020
[TBL] [Abstract][Full Text] [Related]
17. Variable selection under multiple imputation using the bootstrap in a prognostic study.
Heymans MW; van Buuren S; Knol DL; van Mechelen W; de Vet HC
BMC Med Res Methodol; 2007 Jul; 7():33. PubMed ID: 17629912
[TBL] [Abstract][Full Text] [Related]
18. Missing data strategies for time-varying confounders in comparative effectiveness studies of non-missing time-varying exposures and right-censored outcomes.
Desai M; Montez-Rath ME; Kapphahn K; Joyce VR; Mathur MB; Garcia A; Purington N; Owens DK
Stat Med; 2019 Jul; 38(17):3204-3220. PubMed ID: 31099433
[TBL] [Abstract][Full Text] [Related]
19. Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model.
Huque MH; Moreno-Betancur M; Quartagno M; Simpson JA; Carlin JB; Lee KJ
Biom J; 2020 Mar; 62(2):444-466. PubMed ID: 31919921
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
