152 related articles for article (PubMed ID: 23852468)
1. An efficient and robust method for analyzing population pharmacokinetic data in genome-wide pharmacogenomic studies: a generalized estimating equation approach.
Nagashima K; Sato Y; Noma H; Hamada C
Stat Med; 2013 Nov; 32(27):4838-58. PubMed ID: 23852468
[TBL] [Abstract][Full Text] [Related]
2. PolyGEE: a generalized estimating equation approach to the efficient and robust estimation of polygenic effects in large-scale association studies.
Hecker J; Prokopenko D; Lange C; Fier HL
Biostatistics; 2018 Jul; 19(3):295-306. PubMed ID: 28968646
[TBL] [Abstract][Full Text] [Related]
3. The robustness of generalized estimating equations for association tests in extended family data.
Suktitipat B; Mathias RA; Vaidya D; Yanek LR; Young JH; Becker LC; Becker DM; Wilson AF; Fallin MD
Hum Hered; 2012; 74(1):17-26. PubMed ID: 23038411
[TBL] [Abstract][Full Text] [Related]
4. Single Marker Family-Based Association Analysis Not Conditional on Parental Information.
Namkung J; Won S
Methods Mol Biol; 2017; 1666():409-439. PubMed ID: 28980257
[TBL] [Abstract][Full Text] [Related]
5. Nonlinear mixed-effects models with misspecified random-effects distribution.
Drikvandi R
Pharm Stat; 2020 May; 19(3):187-201. PubMed ID: 31663263
[TBL] [Abstract][Full Text] [Related]
6. Application of robust estimating equations to the analysis of quantitative longitudinal data.
Hu M; Lachin JM
Stat Med; 2001 Nov; 20(22):3411-28. PubMed ID: 11746326
[TBL] [Abstract][Full Text] [Related]
7. A bias-reduced generalized estimating equation approach for proportional odds models with small-sample longitudinal ordinal data.
Tada Y; Sato T
BMC Med Res Methodol; 2024 Jun; 24(1):140. PubMed ID: 38943068
[TBL] [Abstract][Full Text] [Related]
8. A conceptual framework for pharmacodynamic genome-wide association studies in pharmacogenomics.
Wu R; Tong C; Wang Z; Mauger D; Tantisira K; Szefler SJ; Chinchilli VM; Israel E
Drug Discov Today; 2011 Oct; 16(19-20):884-90. PubMed ID: 21920452
[TBL] [Abstract][Full Text] [Related]
9. Pharmacogenomic characterization of gemcitabine response--a framework for data integration to enable personalized medicine.
Harris M; Bhuvaneshwar K; Natarajan T; Sheahan L; Wang D; Tadesse MG; Shoulson I; Filice R; Steadman K; Pishvaian MJ; Madhavan S; Deeken J
Pharmacogenet Genomics; 2014 Feb; 24(2):81-93. PubMed ID: 24401833
[TBL] [Abstract][Full Text] [Related]
10. A comparison of strategies for analyzing dichotomous outcomes in genome-wide association studies with general pedigrees.
Chen MH; Liu X; Wei F; Larson MG; Fox CS; Vasan RS; Yang Q
Genet Epidemiol; 2011 Nov; 35(7):650-7. PubMed ID: 21818773
[TBL] [Abstract][Full Text] [Related]
11. Comparing Generalized Estimating Equation and Linear Mixed Effects Model for Estimating Marginal Association with Bivariate Continuous Outcomes.
Li M; Kong X
Ophthalmic Epidemiol; 2023 Jun; 30(3):307-316. PubMed ID: 35838059
[TBL] [Abstract][Full Text] [Related]
12. Population pharmacokinetics of gemcitabine and its metabolite in Japanese cancer patients: impact of genetic polymorphisms.
Sugiyama E; Kaniwa N; Kim SR; Hasegawa R; Saito Y; Ueno H; Okusaka T; Ikeda M; Morizane C; Kondo S; Yamamoto N; Tamura T; Furuse J; Ishii H; Yoshida T; Saijo N; Sawada J
Clin Pharmacokinet; 2010 Aug; 49(8):549-58. PubMed ID: 20608756
[TBL] [Abstract][Full Text] [Related]
13. Pharmacogenomics of gemcitabine: can genetic studies lead to tailor-made therapy?
Ueno H; Kiyosawa K; Kaniwa N
Br J Cancer; 2007 Jul; 97(2):145-51. PubMed ID: 17595663
[TBL] [Abstract][Full Text] [Related]
14. Regularized sandwich estimators for analysis of high-dimensional data using generalized estimating equations.
Warton DI
Biometrics; 2011 Mar; 67(1):116-23. PubMed ID: 20528857
[TBL] [Abstract][Full Text] [Related]
15. A GEE-type approach to untangle structural and random zeros in predictors.
Ye P; Tang W; He J; He H
Stat Methods Med Res; 2019 Dec; 28(12):3683-3696. PubMed ID: 30472921
[TBL] [Abstract][Full Text] [Related]
16. Population Pharmacokinetics of Gemcitabine and dFdU in Pancreatic Cancer Patients Using an Optimal Design, Sparse Sampling Approach.
Serdjebi C; Gattacceca F; Seitz JF; Fein F; Gagnière J; François E; Abakar-Mahamat A; Deplanque G; Rachid M; Lacarelle B; Ciccolini J; Dahan L
Ther Drug Monit; 2017 Jun; 39(3):290-296. PubMed ID: 28346313
[TBL] [Abstract][Full Text] [Related]
17. An investigation of penalization and data augmentation to improve convergence of generalized estimating equations for clustered binary outcomes.
Geroldinger A; Blagus R; Ogden H; Heinze G
BMC Med Res Methodol; 2022 Jun; 22(1):168. PubMed ID: 35681120
[TBL] [Abstract][Full Text] [Related]
18. Fitting marginal models in small samples: A simulation study of marginalized multilevel models and generalized estimating equations.
Bie R; Haneuse S; Huey N; Schildcrout J; McGee G
Stat Med; 2021 Oct; 40(24):5298-5312. PubMed ID: 34251697
[TBL] [Abstract][Full Text] [Related]
19. A readily available improvement over method of moments for intra-cluster correlation estimation in the context of cluster randomized trials and fitting a GEE-type marginal model for binary outcomes.
Westgate PM
Clin Trials; 2019 Feb; 16(1):41-51. PubMed ID: 30295512
[TBL] [Abstract][Full Text] [Related]
20. A comparison of bias-adjusted generalized estimating equations for sparse binary data in small-sample longitudinal studies.
Gosho M; Ishii R; Noma H; Maruo K
Stat Med; 2023 Jul; 42(15):2711-2727. PubMed ID: 37062288
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
