153 related articles for article (PubMed ID: 31604610)
1. The missing indicator approach for censored covariates subject to limit of detection in logistic regression models.
Chiou SH; Betensky RA; Balasubramanian R
Ann Epidemiol; 2019 Oct; 38():57-64. PubMed ID: 31604610
[TBL] [Abstract][Full Text] [Related]
2. Comparison of the missing-indicator method and conditional logistic regression in 1:m matched case-control studies with missing exposure values.
Li X; Song X; Gray RH
Am J Epidemiol; 2004 Mar; 159(6):603-10. PubMed ID: 15003965
[TBL] [Abstract][Full Text] [Related]
3. Outcome-sensitive multiple imputation: a simulation study.
Kontopantelis E; White IR; Sperrin M; Buchan I
BMC Med Res Methodol; 2017 Jan; 17(1):2. PubMed ID: 28068910
[TBL] [Abstract][Full Text] [Related]
4. Multiple imputation with missing indicators as proxies for unmeasured variables: simulation study.
Sperrin M; Martin GP
BMC Med Res Methodol; 2020 Jul; 20(1):185. PubMed ID: 32640992
[TBL] [Abstract][Full Text] [Related]
5. GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies.
Wei R; Wang J; Jia E; Chen T; Ni Y; Jia W
PLoS Comput Biol; 2018 Jan; 14(1):e1005973. PubMed ID: 29385130
[TBL] [Abstract][Full Text] [Related]
6. A comparison of different methods to handle missing data in the context of propensity score analysis.
Choi J; Dekkers OM; le Cessie S
Eur J Epidemiol; 2019 Jan; 34(1):23-36. PubMed ID: 30341708
[TBL] [Abstract][Full Text] [Related]
7. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P
Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557
[TBL] [Abstract][Full Text] [Related]
8. A semiparametric imputation approach for regression with censored covariate with application to an AMD progression study.
Ding Y; Kong S; Kang S; Chen W
Stat Med; 2018 Oct; 37(23):3293-3308. PubMed ID: 29845616
[TBL] [Abstract][Full Text] [Related]
9. A semiparametric missing-data-induced intensity method for missing covariate data in individually matched case-control studies.
Gebregziabher M; Langholz B
Biometrics; 2010 Sep; 66(3):845-54. PubMed ID: 19751251
[TBL] [Abstract][Full Text] [Related]
10. Review: a gentle introduction to imputation of missing values.
Donders AR; van der Heijden GJ; Stijnen T; Moons KG
J Clin Epidemiol; 2006 Oct; 59(10):1087-91. PubMed ID: 16980149
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics.
Dekermanjian JP; Shaddox E; Nandy D; Ghosh D; Kechris K
BMC Bioinformatics; 2022 May; 23(1):179. PubMed ID: 35578165
[TBL] [Abstract][Full Text] [Related]
13. A nonparametric multiple imputation approach for missing categorical data.
Zhou M; He Y; Yu M; Hsu CH
BMC Med Res Methodol; 2017 Jun; 17(1):87. PubMed ID: 28587662
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Imputation of Missing Covariates in Randomized Controlled Trials with Continuous Outcomes: Simple, Unbiased and Efficient Methods.
Kayembe MT; Jolani S; Tan FES; van Breukelen GJP
J Biopharm Stat; 2022 Sep; 32(5):717-739. PubMed ID: 35041565
[TBL] [Abstract][Full Text] [Related]
16. Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study.
Kokla M; Virtanen J; Kolehmainen M; Paananen J; Hanhineva K
BMC Bioinformatics; 2019 Oct; 20(1):492. PubMed ID: 31601178
[TBL] [Abstract][Full Text] [Related]
17. Multiple imputation with missing data indicators.
Beesley LJ; Bondarenko I; Elliot MR; Kurian AW; Katz SJ; Taylor JM
Stat Methods Med Res; 2021 Dec; 30(12):2685-2700. PubMed ID: 34643465
[TBL] [Abstract][Full Text] [Related]
18. Explicating the Conditions Under Which Multilevel Multiple Imputation Mitigates Bias Resulting from Random Coefficient-Dependent Missing Longitudinal Data.
Gottfredson NC; Sterba SK; Jackson KM
Prev Sci; 2017 Jan; 18(1):12-19. PubMed ID: 27866307
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Comparative methods for handling missing data in large databases.
Henry AJ; Hevelone ND; Lipsitz S; Nguyen LL
J Vasc Surg; 2013 Nov; 58(5):1353-1359.e6. PubMed ID: 23830314
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
