ライフサイエンスコーパス: latent class analysis

1 hort study, subtypes of AUD were assessed by latent class analysis.

2 atterns to determine menopausal status using latent class analysis.

3 profiles, using a specific statistical tool: Latent class analysis.

4 Patients were classified by using latent class analysis.

5 Phenotypes were determined by using latent class analysis.

6 t characteristics were also assessed using a latent class analysis.

7 EOs across the day, were determined by using latent class analysis.

8 ets of 2 community samples were subjected to latent class analysis.

9 ificant eating disorders were submitted to a latent class analysis.

10 rical and clinical variables not used in the latent class analysis.

11 erived on the basis of symptomatology, using latent class analysis.

12 ymptom factors; and 3) syndromes, defined by latent class analysis.

13 Using latent class analysis, 4 phenotypes of atopic dermatitis

14 iptive, case-control, attributable fraction, latent class analysis) address some but not all challeng

15 fined immunologic phenotypes with the use of latent class analysis and investigated their association

16 different statistical approaches, including latent class analysis and self-organizing maps.

17 iation between depressive subtypes (based on latent class analysis) and biological measures.

18 y results, case-control logistic regression, latent class analysis, and attributable fraction, but ea

19 measures were examined using a confirmatory latent class analysis approach.

20 es of aspergillosis in CF were identified by latent class analysis by using serologic, RT-PCR, and GM

21 Latent class analysis classified GERD patients based on

22 The latent class analysis confirmed that the RNA-based test

23 Latent class analysis demonstrated that physicians could

24 Latent class analysis detected 7 multivariate disorder c

25 Heart Failure Criteria (MHFC), derived using latent class analysis from widely available items in the

26 first time, to the authors' knowledge, that latent class analysis has been applied to longitudinal d

27 Latent class analysis identified 8 patient groups.

28 bination of high-dimensional phenotyping and latent class analysis identifies subtypes of HFREF with

29 ages 31-53 years) can be characterized using latent class analysis in a population-based birth cohort

30 rts from birth to 7 years were derived using latent class analysis in the Avon Longitudinal Study of

31 We present a Bayesian latent class analysis in which we evaluated the accuracy

32 of ADHD subtypes as defined by DSM-IV and by latent-class analysis in a population sample of adolesce

33 Findings from latent class analysis indicate that MDMA users have a si

34 Latent class analysis indicated 5 classes of poly-tobacc

35 comparisons assessed with kappa scores, and latent class analysis (LCA) as an unbiased estimator of

36 ation over the first 6 years of life using a latent class analysis (LCA) integrating 3 dimensions of

37 Latent class analysis (LCA) is a mathematical technique

38 With a latent class analysis (LCA) model that included all the

39 We performed a latent class analysis (LCA) of OC features, cross-sectio

40 A custom latent class analysis (LCA) procedure was developed to i

41 In a prior report, the authors used latent class analysis (LCA) to identify a distinctive at

42 We apply a data-driven latent class analysis (LCA) to model 54 specific health

43 Latent class analysis (LCA) was performed to estimate th

44 Latent class analysis (LCA) was used to determine visual

45 Latent class analysis (LCA) was used to identify underly

46 s and risk factors of allergic disease using latent class analysis (LCA).

47 atients with AERD through the application of latent class analysis (LCA).

48 e patient infection standard (PIS) and using latent class analysis (LCA).

49 pplying two latent class models-longitudinal latent class analysis (LLCA) and latent class growth ana

50 A five-class latent class analysis model was collapsed into cases and

51 The latent class analysis model with the best fit to PASTURE

52 uantiFERON-TB Gold In-Tube (QFT) tests using latent class analysis model.

53 sified women by their dietary patterns using latent class analysis of 66 foods and studied the associ

54 We used latent class analysis of baseline clinical and plasma bi

55 Latent class analysis of items on the Fagerstrom Test fo

56 Latent class analysis of triazole-naive patients identif

57 uracy of each test, estimated using Bayesian latent class analysis (presented with 95% Bayesian credi

58 In a latent class analysis, real-time PCR had significantly h

59 Longitudinal latent class analysis revealed 3 grass sensitization tra

60 Hierarchical cluster analysis and latent class analysis revealed developmental changes in

61 Latent class analysis revealed that the estimated propor

62 Latent class analysis showed that the brief set of socia

63 Latent class analysis shows that African American males

64 /impulsive DSM-IV subtype and the individual latent-class analysis subtypes did not co-cluster.

65 Latent class analysis suggested five schizophrenic syndr

66 ng high-dimensional clinical phenotyping and latent class analysis that may be useful in personalizin

67 infection status was strengthened by use of latent-class analysis that combined data for markers of

68 In the latent-class analysis, the highest-order maternal weight

69 We proceed by using Latent Class Analysis to assess whether it is possible t

70 SIC performs an unsupervised, fully Bayesian latent class analysis to estimate false positive and fal

71 We designed a nested, 2-stage latent class analysis to identify cross-sectional sensit

72 od-parent attributes, with subsequent use of latent class analysis to identify groups of parents with

73 As in the original cohort, we used latent class analysis to identify phenotypes on the basi

74 eze and cough in early childhood by applying latent class analysis to longitudinal data from a popula

75 Latent class analysis was applied to 14 disaggregated DS

76 Latent class analysis was applied to detailed symptomati

77 Latent class analysis was applied to nine eating disorde

78 Latent class analysis was carried out to determine types

79 Design, Setting, and PARTICIPANTS: Latent class analysis was conducted using 2003-2008 data

80 Latent class analysis was found to be a useful tool for

81 Longitudinal latent class analysis was performed by using pain intens

82 A latent class analysis was performed to identify "latent

83 Latent class analysis was used and identified five class

84 Latent class analysis was used to derive phenotypes base

85 Latent class analysis was used to examine whether physic

86 Latent class analysis was used to generate two latent cl

87 Latent class analysis was used to identify groups of pat

88 Latent class analysis was used to identify immune phenot

89 Latent class analysis was used to identify socioeconomic

90 The latent class analysis was used to identify subtypes of a

91 Longitudinal latent class analysis was used to investigate patterns o

92 ine which elements of ADHD cluster together, latent-class analysis was applied to data obtained from

93 Latent-class analysis was most compatible with the exist

94 Latent-class analysis was used to evaluate the usefulnes

95 Through the use of latent class analysis, we revealed a high-risk subtype (

96 ar activation times between burst pairs, and latent class analysis, which revealed a population of 5-

97 yndrome and meaningful subtypes emerged from latent class analysis, which were validated by patterns

98 n 7434 subjects with non-missing indicators, latent class analysis yielded 5 latent classes.