BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

244 related articles for article (PubMed ID: 28695667)

  • 1. A comparison of existing methods for multiple imputation in individual participant data meta-analysis.
    Kunkel D; Kaizar EE
    Stat Med; 2017 Sep; 36(22):3507-3532. PubMed ID: 28695667
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Multiple imputation by chained equations for systematically and sporadically missing multilevel data.
    Resche-Rigon M; White IR
    Stat Methods Med Res; 2018 Jun; 27(6):1634-1649. PubMed ID: 27647809
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Hierarchical imputation of systematically and sporadically missing data: An approximate Bayesian approach using chained equations.
    Jolani S
    Biom J; 2018 Mar; 60(2):333-351. PubMed ID: 28990686
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model.
    Seaman SR; Hughes RA
    Stat Methods Med Res; 2018 Jun; 27(6):1603-1614. PubMed ID: 27597798
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Multiple imputation for handling systematically missing confounders in meta-analysis of individual participant data.
    Resche-Rigon M; White IR; Bartlett JW; Peters SA; Thompson SG;
    Stat Med; 2013 Dec; 32(28):4890-905. PubMed ID: 23857554
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Combining multiple imputation and meta-analysis with individual participant data.
    Burgess S; White IR; Resche-Rigon M; Wood AM
    Stat Med; 2013 Nov; 32(26):4499-514. PubMed ID: 23703895
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A Comparison of Multilevel Imputation Schemes for Random Coefficient Models: Fully Conditional Specification and Joint Model Imputation with Random Covariance Matrices.
    Enders CK; Hayes T; Du H
    Multivariate Behav Res; 2018; 53(5):695-713. PubMed ID: 30693802
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. SuperMICE: An Ensemble Machine Learning Approach to Multiple Imputation by Chained Equations.
    Laqueur HS; Shev AB; Kagawa RMC
    Am J Epidemiol; 2022 Feb; 191(3):516-525. PubMed ID: 34788362
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Dealing with missing covariates in epidemiologic studies: a comparison between multiple imputation and a full Bayesian approach.
    Erler NS; Rizopoulos D; Rosmalen Jv; Jaddoe VW; Franco OH; Lesaffre EM
    Stat Med; 2016 Jul; 35(17):2955-74. PubMed ID: 27042954
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Heckman imputation models for binary or continuous MNAR outcomes and MAR predictors.
    Galimard JE; Chevret S; Curis E; Resche-Rigon M
    BMC Med Res Methodol; 2018 Aug; 18(1):90. PubMed ID: 30170561
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE.
    Jolani S; Debray TP; Koffijberg H; van Buuren S; Moons KG
    Stat Med; 2015 May; 34(11):1841-63. PubMed ID: 25663182
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. 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]  

  • 17. Accounting for not-at-random missingness through imputation stacking.
    Beesley LJ; Taylor JMG
    Stat Med; 2021 Nov; 40(27):6118-6132. PubMed ID: 34459011
    [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 of missing data in multilevel designs: A comparison of different strategies.
    Lüdtke O; Robitzsch A; Grund S
    Psychol Methods; 2017 Mar; 22(1):141-165. PubMed ID: 27607544
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Review and evaluation of imputation methods for multivariate longitudinal data with mixed-type incomplete variables.
    Cao Y; Allore H; Vander Wyk B; Gutman R
    Stat Med; 2022 Dec; 41(30):5844-5876. PubMed ID: 36220138
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.