These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

189 related articles for article (PubMed ID: 27488606)

  • 1. Multiple imputation of completely missing repeated measures data within person from a complex sample: application to accelerometer data in the National Health and Nutrition Examination Survey.
    Liu B; Yu M; Graubard BI; Troiano RP; Schenker N
    Stat Med; 2016 Dec; 35(28):5170-5188. PubMed ID: 27488606
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Multiple imputation of missing dual-energy X-ray absorptiometry data in the National Health and Nutrition Examination Survey.
    Schenker N; Borrud LG; Burt VL; Curtin LR; Flegal KM; Hughes J; Johnson CL; Looker AC; Mirel L
    Stat Med; 2011 Feb; 30(3):260-76. PubMed ID: 21213343
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Missing value imputation for physical activity data measured by accelerometer.
    Lee JA; Gill J
    Stat Methods Med Res; 2018 Feb; 27(2):490-506. PubMed ID: 26994215
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Data imputation for accelerometer-measured physical activity: the combined approach.
    Lee PH
    Am J Clin Nutr; 2013 May; 97(5):965-71. PubMed ID: 23553165
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Deep Learning Approach for Imputation of Missing Values in Actigraphy Data: Algorithm Development Study.
    Jang JH; Choi J; Roh HW; Son SJ; Hong CH; Kim EY; Kim TY; Yoon D
    JMIR Mhealth Uhealth; 2020 Jul; 8(7):e16113. PubMed ID: 32445459
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Imputation of missing data when measuring physical activity by accelerometry.
    Catellier DJ; Hannan PJ; Murray DM; Addy CL; Conway TL; Yang S; Rice JC
    Med Sci Sports Exerc; 2005 Nov; 37(11 Suppl):S555-62. PubMed ID: 16294118
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Item non-response imputation in the Korea National Health and Nutrition Examination Survey.
    Son S; Moon H; An H
    Epidemiol Health; 2022; 44():e2022096. PubMed ID: 36317400
    [TBL] [Abstract][Full Text] [Related]  

  • 8. The multiple imputation method: a case study involving secondary data analysis.
    Walani SR; Cleland CM
    Nurse Res; 2015 May; 22(5):13-9. PubMed ID: 25976532
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Empirical Comparison of Imputation Methods for Multivariate Missing Data in Public Health.
    Pan S; Chen S
    Int J Environ Res Public Health; 2023 Jan; 20(2):. PubMed ID: 36674279
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Multiple imputation approaches for epoch-level accelerometer data in trials.
    Tackney MS; Williamson E; Cook DG; Limb E; Harris T; Carpenter J
    Stat Methods Med Res; 2023 Oct; 32(10):1936-1960. PubMed ID: 37519214
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Statistical approaches to account for missing values in accelerometer data: Applications to modeling physical activity.
    Yue Xu S; Nelson S; Kerr J; Godbole S; Patterson R; Merchant G; Abramson I; Staudenmayer J; Natarajan L
    Stat Methods Med Res; 2018 Apr; 27(4):1168-1186. PubMed ID: 27405327
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Multiple imputation in the presence of non-normal data.
    Lee KJ; Carlin JB
    Stat Med; 2017 Feb; 36(4):606-617. PubMed ID: 27862164
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Hot Deck Multiple Imputation for Handling Missing Accelerometer Data.
    Butera NM; Li S; Evenson KR; Di C; Buchner DM; LaMonte MJ; LaCroix AZ; Herring A
    Stat Biosci; 2019 Jul; 11(2):422-448. PubMed ID: 31447952
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A practical guide to multiple imputation of missing data in nephrology.
    Blazek K; van Zwieten A; Saglimbene V; Teixeira-Pinto A
    Kidney Int; 2021 Jan; 99(1):68-74. PubMed ID: 32822702
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A two-step semiparametric method to accommodate sampling weights in multiple imputation.
    Zhou H; Elliott MR; Raghunathan TE
    Biometrics; 2016 Mar; 72(1):242-52. PubMed ID: 26393409
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Strategies for handling missing data that improve Frailty Index estimation and predictive power: lessons from the NHANES dataset.
    Pridham G; Rockwood K; Rutenberg A
    Geroscience; 2022 Apr; 44(2):897-923. PubMed ID: 35103915
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Missing data imputation via the expectation-maximization algorithm can improve principal component analysis aimed at deriving biomarker profiles and dietary patterns.
    Malan L; Smuts CM; Baumgartner J; Ricci C
    Nutr Res; 2020 Mar; 75():67-76. PubMed ID: 32035304
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Multiple imputation for patient reported outcome measures in randomised controlled trials: advantages and disadvantages of imputing at the item, subscale or composite score level.
    Rombach I; Gray AM; Jenkinson C; Murray DW; Rivero-Arias O
    BMC Med Res Methodol; 2018 Aug; 18(1):87. PubMed ID: 30153796
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.