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 *

121 related articles for article (PubMed ID: 36097102)

  • 1. Covariate inclusion in factor mixture modeling: Evaluating one-step and three-step approaches under model misspecification and overfitting.
    Wang Y; Cao C; Kim E
    Behav Res Methods; 2023 Sep; 55(6):3281-3296. PubMed ID: 36097102
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Testing Measurement Invariance Across Unobserved Groups: The Role of Covariates in Factor Mixture Modeling.
    Wang Y; Kim E; Ferron JM; Dedrick RF; Tan TX; Stark S
    Educ Psychol Meas; 2021 Feb; 81(1):61-89. PubMed ID: 33456062
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Investigating Approaches to Estimating Covariate Effects in Growth Mixture Modeling: A Simulation Study.
    Li M; Harring JR
    Educ Psychol Meas; 2017 Oct; 77(5):766-791. PubMed ID: 29795930
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Class Enumeration and Parameter Recovery of Growth Mixture Modeling and Second-Order Growth Mixture Modeling in the Presence of Measurement Noninvariance between Latent Classes.
    Kim ES; Wang Y
    Front Psychol; 2017; 8():1499. PubMed ID: 28928691
    [TBL] [Abstract][Full Text] [Related]  

  • 6. The impact of total and partial inclusion or exclusion of active and inactive time invariant covariates in growth mixture models.
    Diallo TM; Morin AJ; Lu H
    Psychol Methods; 2017 Mar; 22(1):166-190. PubMed ID: 27643403
    [TBL] [Abstract][Full Text] [Related]  

  • 7. An evaluation of the use of covariates to assist in class enumeration in linear growth mixture modeling.
    Hu J; Leite WL; Gao M
    Behav Res Methods; 2017 Jun; 49(3):1179-1190. PubMed ID: 28275951
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A comparison of covariate adjustment approaches under model misspecification in individually randomized trials.
    Tackney MS; Morris T; White I; Leyrat C; Diaz-Ordaz K; Williamson E
    Trials; 2023 Jan; 24(1):14. PubMed ID: 36609282
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Exploration of model misspecification in latent class methods for longitudinal data: Correlation structure matters.
    Neely ML; Pieper CF; Gu B; Dmitrieva NO; Pendergast JF
    Stat Med; 2023 Jun; 42(14):2420-2438. PubMed ID: 37019876
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Evaluating the Quality of Classification in Mixture Model Simulations.
    Jang Y; Hong S
    Educ Psychol Meas; 2023 Apr; 83(2):351-374. PubMed ID: 36866069
    [TBL] [Abstract][Full Text] [Related]  

  • 11. On Inclusion of Covariates for Class Enumeration of Growth Mixture Models.
    Li L; Hser YI
    Multivariate Behav Res; 2011; 46(2):266-302. PubMed ID: 23904664
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Modeling predictors of latent classes in regression mixture models.
    Minjung K; Jeroen V; Zsuzsa B; Thomas J; Lee VHM
    Struct Equ Modeling; 2016; 23(4):601-614. PubMed ID: 31588168
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Determining the Number of Latent Classes in Single- and Multi-Phase Growth Mixture Models.
    Kim SY
    Struct Equ Modeling; 2014; 21(2):263-279. PubMed ID: 24729675
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Assessing the Robustness of Mixture Models to Measurement Noninvariance.
    Cole VT; Bauer DJ; Hussong AM
    Multivariate Behav Res; 2019; 54(6):882-905. PubMed ID: 31264477
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The Impact of Item Misspecification and Dichotomization on Class and Parameter Recovery in LCA of Count Data.
    Macia KS; Wickham RE
    Multivariate Behav Res; 2019; 54(1):113-145. PubMed ID: 30595072
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Evaluating performance of covariate-constrained randomization (CCR) techniques under misspecification of cluster-level variables in cluster-randomized trials.
    Organ M; Tandon SD; Diebold A; Johnson JK; Yeh C; Ciolino JD
    Contemp Clin Trials Commun; 2021 Jun; 22():100754. PubMed ID: 33732943
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Is adding more indicators to a latent class analysis beneficial or detrimental? Results of a Monte-Carlo study.
    Wurpts IC; Geiser C
    Front Psychol; 2014; 5():920. PubMed ID: 25191298
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches.
    Lubke G; Tueller S
    Struct Equ Modeling; 2010 Oct; 17(4):605-628. PubMed ID: 24648712
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Accuracy of mixture item response theory models for identifying sample heterogeneity in patient-reported outcomes: a simulation study.
    Sajobi TT; Lix LM; Russell L; Schulz D; Liu J; Zumbo BD; Sawatzky R
    Qual Life Res; 2022 Dec; 31(12):3423-3432. PubMed ID: 35716223
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Different Approaches to Covariate Inclusion in the Mixture Rasch Model.
    Li T; Jiao H; Macready GB
    Educ Psychol Meas; 2016 Oct; 76(5):848-872. PubMed ID: 29795891
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.