BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

259 related articles for article (PubMed ID: 30014575)

  • 41. 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]  

  • 42. Sequential BART for imputation of missing covariates.
    Xu D; Daniels MJ; Winterstein AG
    Biostatistics; 2016 Jul; 17(3):589-602. PubMed ID: 26980459
    [TBL] [Abstract][Full Text] [Related]  

  • 43. 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]  

  • 44. 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]  

  • 45. The "Why" behind including "Y" in your imputation model.
    D'Agostino McGowan L; Lotspeich SC; Hepler SA
    Stat Methods Med Res; 2024 Jun; 33(6):996-1020. PubMed ID: 38625810
    [TBL] [Abstract][Full Text] [Related]  

  • 46. Using audit information to adjust parameter estimates for data errors in clinical trials.
    Shepherd BE; Shaw PA; Dodd LE
    Clin Trials; 2012 Dec; 9(6):721-9. PubMed ID: 22848072
    [TBL] [Abstract][Full Text] [Related]  

  • 47. 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]  

  • 48. Recovery of information from multiple imputation: a simulation study.
    Lee KJ; Carlin JB
    Emerg Themes Epidemiol; 2012 Jun; 9(1):3. PubMed ID: 22695083
    [TBL] [Abstract][Full Text] [Related]  

  • 49. Improving upon the efficiency of complete case analysis when covariates are MNAR.
    Bartlett JW; Carpenter JR; Tilling K; Vansteelandt S
    Biostatistics; 2014 Oct; 15(4):719-30. PubMed ID: 24907708
    [TBL] [Abstract][Full Text] [Related]  

  • 50. 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]  

  • 51. Bayesian analysis for partly linear Cox model with measurement error and time-varying covariate effect.
    Pan A; Song X; Huang H
    Stat Med; 2022 Oct; 41(23):4666-4681. PubMed ID: 35899596
    [TBL] [Abstract][Full Text] [Related]  

  • 52. Methods for handling missing data in serially sampled sputum specimens for mycobacterial culture conversion calculation.
    Malatesta S; Weir IR; Weber SE; Bouton TC; Carney T; Theron D; Myers B; Horsburgh CR; Warren RM; Jacobson KR; White LF
    BMC Med Res Methodol; 2022 Nov; 22(1):297. PubMed ID: 36402979
    [TBL] [Abstract][Full Text] [Related]  

  • 53. 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]  

  • 54. 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]  

  • 55. Multiple imputation based on restricted mean model for censored data.
    Liu LX; Murray S; Tsodikov A
    Stat Med; 2011 May; 30(12):1339-50. PubMed ID: 21560139
    [TBL] [Abstract][Full Text] [Related]  

  • 56. A stacked approach for chained equations multiple imputation incorporating the substantive model.
    Beesley LJ; Taylor JMG
    Biometrics; 2021 Dec; 77(4):1342-1354. PubMed ID: 32920819
    [TBL] [Abstract][Full Text] [Related]  

  • 57. Empirical Likelihood in Nonignorable Covariate-Missing Data Problems.
    Xie Y; Zhang B
    Int J Biostat; 2017 Apr; 13(1):. PubMed ID: 28441139
    [TBL] [Abstract][Full Text] [Related]  

  • 58. Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods.
    Seaman SR; Bartlett JW; White IR
    BMC Med Res Methodol; 2012 Apr; 12():46. PubMed ID: 22489953
    [TBL] [Abstract][Full Text] [Related]  

  • 59. 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]  

  • 60. Generalizing treatment effects with incomplete covariates: Identifying assumptions and multiple imputation algorithms.
    Mayer I; Josse J;
    Biom J; 2023 Jun; 65(5):e2100294. PubMed ID: 36907999
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 13.