ライフサイエンスコーパス: GEE

1 GEE analysis identified an interaction between the prese

2 GEE models showed that children had an exponential incre

3 nix-based cases (2.1 vs. 1.0 years; P=0.002, GEE model).

4 on earlier than limbus-based cases (P=0.002, GEE model).

5 common with fornix-based operations (P=0.01, GEE model).

6 The first GWA approach applied a GEE-based model to identify gene-based associations with

7 In a GEE linear regression model, the presence of F12-46C/T w

8 ms currently available for doing the REM and GEE modeling.

9 to be graded as higher and to be avascular (GEE model, both P < 0.0001).

10 ites were individually associated with IR by GEE (all false discovery rate-adjusted P values</=0.026)

11 al pulmonary function (REM) and categorical (GEE) types of respiratory data.

12 The most potent inhibitor (Cinn-GEE) displayed a K(D) value of 1.3 microM against the N-

13 innamaldehyde resulted in an inhibitor (Cinn-GEE) of substantially increased potency against all thre

14 ((1)H) and delta 201 ((13)C), the PTP1B/Cinn-GEE complex showed three distinct cross-peaks at delta 7

15 site-directed mutagenesis and by using Cinn-GEE specifically labeled with (13)C at the aldehyde carb

16 sm of inhibition was investigated using Cinn-GEE specifically labeled with (13)C at the aldehyde carb

17 While Cinn-GEE alone showed a single cross-peak at delta 9.64 ((1)H

18 Similar experiments with Cinn-GEE that had been labeled with (13)C at the benzylic pos

19 In the combined GEE model including adalimumab and etanercept, a body-ma

20 e, 2,3-butanedione, and the reagent pair EDC/GEE, are used together to pinpoint the binding sites of

21 models, and generalized estimating equation (GEE) analysis.

22 Generalized estimating equation (GEE) and random-effects models were used to test for lin

23 mined using generalized estimating equation (GEE) and within-twin pair analyses, adjusting for potent

24 n using the Generalized Estimating Equation (GEE) approach.

25 A general estimating equation (GEE) model was used to calculate the odds ratio (OR) for

26 d model and generalized estimating equation (GEE) model with repeated measures.

27 ongitudinal Generalized Estimating Equation (GEE) model, each 10 mg increase in prednisone dose is as

28 Generalized estimating equation (GEE) modeling with use of an unstructured binomial logit

29 Generalized estimating equation (GEE) models informed the mortality risk within 3 mo for

30 ression and generalized estimating equation (GEE) models to evaluate the association between log10-tr

31 Generalized estimating equation (GEE) models were fitted to compare covariate-adjusted pr

32 We fitted Generalized Estimating Equation (GEE) regression models to analyse repeated measurements

33 regression, generalised estimating equation (GEE), and receiver operating characteristic curves were

34 In a Generalized Estimation Equation (GEE) logit model for mothers (n = 179) and children (n =

35 l eye using generalized estimation equation (GEE) models that can account for within-subject correlat

36 ance using generalized estimating equations (GEE) and extended Cox models.

37 method of generalized estimating equations (GEE) for CAL changes from baseline to the 3-month visit,

38 method of generalized estimating equations (GEE) for correlated data was utilized to determine the r

39 lyses, and generalised estimating equations (GEE) for the global (ie, any) pathogen analyses, with ad

40 ffects and generalized estimating equations (GEE) logistic models showed that reinfection risk was si

41 The generalized estimating equations (GEE) method is commonly used to estimate population-aver

42 using the generalized estimating equations (GEE) method to test for associations between initial occ

43 t with the generalized estimating equations (GEE) method with an exchangeable correlation structure b

44 )-weighted generalized estimating equations (GEE) methods in the context of a study of Kenyan mothers

45 ltivariate generalized estimating equations (GEE) model with a binomial distribution was used to asse

46 Generalised estimating equations (GEE) tested the association between anti-CarP antibodies

47 We used generalized estimating equations (GEE) to examine associations of prenatal and childhood D

48 cted using generalized estimating equations (GEE) to examine the association of SCT with HbA1c levels

49 method of generalized estimating equations (GEE) to test for associations between increase or decrea

50 ervations, generalized estimating equations (GEE) were used for regression modeling.

51 Generalized Estimating Equations (GEE) were used to compare males with females in terms of

52 Generalized estimating equations (GEE) were used to estimate the impact of BMI and overwei

53 s based on generalized estimating equations (GEE), as a potential alternative to full maximum-likelih

54 sion using generalized estimating equations (GEE), from which odds ratios (OR) were estimated and tes

55 PCA), the generalizing estimating equations (GEE), the trait-based association test involving the ext

56 method of generalized estimating equations (GEE), with an exchangeable working correlation to accomm

57 ated using generalized estimating equations (GEE).

58 yses using generalised estimating equations (GEE).

59 well-known generalized estimating equations (GEEs) for longitudinal data analysis, we focus on the co

60 s fit with generalized estimating equations (GEEs) were used to estimate the association between soci

61 Generalized estimating equations (GEEs) were used to estimate the predictive value of regi

62 lyzed with generalized estimating equations (GEEs), a patient-based statistical approach.

63 mplemented generalized estimating equations (GEEs), an extension of the generalized linear model acco

64 d included generalized estimating equations (GEEs), latent class growth modeling (LCGM), linear mixed

65 gradient (generalized estimating equations [GEE] risk ratio 27.2, 95% CI 1.2 to 619.6, p = 0.0386) a

66 Glycine ethyl ester (GEE) and ammonium chloride served as replacements for ly

67 ing methods of FPOP and glycine ethyl ester (GEE) labeling.

68 uced carbon uptake gross ecosystem exchange (GEE).

69 Predictive habitat models using GAM-GEEs provided robust predictions in areas where telemetr

70 In GEE models, adjusted odds ratios per calendar year were

71 n index) indicated that water stress limited GEE and inhibited Reco .

72 In a multivariable GEE model, later year of observation was independently a

73 Predictors of CR in multivariate GEE analysis were age (odds ratio [OR] = 0.97, p = 0.011

74 y than those who were negative (multivariate GEE adjusted for age, sex, smoking status, ACPA, and yea

75 Both (15)NH4Cl and (15)N-GEE could be crosslinked to the three glutamines in alph

76 Non-GEE segment-based analysis revealed that for the two rev

77 According to the analysis results of GEE model, greater power of astigmatism was found to be

78 oth the achromatic pulsed-pedestal paradigm (GEE: beta [SE] = 0.35 [0.06]; P < 0.001) and the chromat

79 analysis for individual SNPs using the PBAT-GEE program indicated that SNP rs921451 was significantl

80 vidual SNP analysis performed using the PBAT-GEE program indicated that two SNPs in the AAs and four

81 e 6-month postoperative measurement periods (GEE, P < 0.0001).

82 619.6, p = 0.0386) and aortic regurgitation (GEE risk ratio 2.4, 95% CI 1.3 to 4.3, p = 0.0029).

83 Since GEE models include outcome data at all timepoints, these

84 Use of a standard GEE model including both scheduled and unscheduled visit

85 Among all subjects, GEE modeling identified a significant change in angiopoi

86 d to have near normal distributions and that GEE be used for categorical or non-normally distributed

87 The GEE and the classical Fisher combination test, on the ot

88 The GEE model revealed that the risk of leak decreased with

89 statistically significant covariates in the GEE models were: 1) baseline age; 2) level of glycemic c

90 tic models, conditional logistic models, the GEE models, and random-effects models by analyzing a bin

91 % corrected (95% CI 73.3% to 82.3%) with the GEE method, and the SVT positive predictivity was 100.0%

92 This GEE model identified 1 significant locus, GRM7, which pa

93 In unadjusted GEE analyses, for a given fasting glucose, HbA1c values

94 ehavioral characteristics was assessed using GEE logistic regression.

95 he estimate obtained using the IIRR-weighted GEE approach was compatible with estimates derived using

96 2.3%, 3.5%), while use of the IIRR-weighted GEE predicted a prevalence of 1.5% (95% confidence inter

97 dependent and additive predictive value when GEEs were used (P < .001, P = .02, P = .002, respectivel