ライフサイエンスコーパス: latent class analysis

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

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

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

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

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

6 BSM distance) were assessed and evaluated by latent class analysis.

7 ormance was evaluated by two comparators and latent class analysis.

8 ing patterns were grouped into classes using latent class analysis.

9 nt a brief overview of the principles behind latent class analysis.

10 of distinct inflammatory subphenotypes using latent class analysis.

11 Infant growth was estimated using latent class analysis.

12 used to determine T. cruzi serostatus using latent class analysis.

13 ric hospital capability level, defined using latent class analysis.

14 f ARS was defined based on 11 symptoms using latent class analysis.

15 Subphenotype was previously assigned using latent class analysis.

16 from routinely available clinical data using latent class analysis.

17 uence over time was measured by longitudinal latent class analysis.

18 uate the existence of symptom subtypes using latent class analysis.

19 on, to identify substance use patterns using latent class analysis.

20 atterns to determine menopausal status using latent class analysis.

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

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

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

24 Patients were classified by using latent class analysis.

25 in randomized controlled trial cohorts using latent class analysis.

26 Phenotypes were determined by using latent class analysis.

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

28 yzed using random-parameter logit models and latent-class analysis.

29 In the latent class analysis, 3 subgroups were identified.

30 Using latent class analysis, 4 phenotypes of atopic dermatitis

31 Four clusters were identified by latent class analysis: 44.9% no or mild group (cluster 1

32 The method ensembles latent class analysis, a model-based clustering method;

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

34 We used latent class analysis and Hierarchical Bayesian modeling

35 We performed latent class analysis and identified five latent exposur

36 We then used latent class analysis and identified two subtypes of PAS

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

38 mixture modelling approaches (which include latent class analysis and latent profile analysis).

39 In this cross-sectional study, we employed latent class analysis and linear mixed-effects models to

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

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

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

43 Latent class analysis- and spell-based phenotypes appear

44 IS-C phenotypic clusters were inferred using latent class analysis applied to the largest cohort to d

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

46 several categorized and continuous indices, latent class analysis based on any use of each substance

47 e (hospitalization) were determined by using latent class analysis based on clinical factors and vira

48 admission was avoidable was quantified using latent class analysis based on the independent reviews.

49 ing macroplastic and microplastic particles (latent class analysis) based on statistically defensible

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

51 en classifying detectability of cytokines by latent class analysis, carrying the 17q21 risk allele rs

52 Using day 1 cytokine levels, latent class analysis categorized patients into two subp

53 Latent class analysis classified GERD patients based on

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

55 Latent class analysis demonstrated that physicians could

56 Model performance was evaluated against the latent class analysis-derived phenotype.Measurements and

57 otypes and to assess concordance between the latent class analysis-derived phenotypes and phenotypes

58 erstanding of biological differences between latent class analysis-derived phenotypes and to assess c

59 Latent class analysis detected 7 multivariate disorder c

60 In latent class analysis, distinct trajectories characteriz

61 Here, we introduce LACE-UP [latent class analysis ensembled with UMAP (uniform manif

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

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

64 hospitalized with bronchiolitis in Finland, latent class analysis identified 3 bronchiolitis profile

65 e, 8.7 [5.0-12.5] years; 5407 [60.5%] male), latent class analysis identified 3 clusters characterize

66 Latent class analysis identified 3 distinct clusters of

67 Latent class analysis identified 4 subgroups: urgency fo

68 Latent class analysis identified 4 subtypes of atopic ec

69 Latent class analysis identified 5 employment quality ty

70 Latent class analysis identified 8 patient groups.

71 Latent class analysis identified differential survival b

72 Multi-group latent class analysis identified three groups of COVID m

73 However, latent-class analysis identified four distinct psycholog

74 This latent-class analysis identified some patients with upfr

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

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

77 matory, have been consistently identified by latent class analysis in numerous cohorts, with widely d

78 nflammatory phenotypes?Methods: We performed latent class analysis in ROSE using preenrollment clinic

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

80 been a recent upsurge in the application of latent class analysis in the fields of critical care, re

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

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

83 tline the key processes necessary to perform latent class analysis including some of the challenges a

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

85 Latent class analysis indicated 5 classes of poly-tobacc

86 Latent class analysis is a probabilistic modeling algori

87 iles for the long-term weekly use of DCPs by latent class analysis (LCA) and assess their association

88 asses of allergic respiratory diseases using latent class analysis (LCA) and distinguish each class u

89 GI symptom groups were determined using a latent class analysis (LCA) approach.

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

91 gE sensitization profiles were identified by latent class analysis (LCA) by considering IgE-reactivit

92 Latent class analysis (LCA) can address some limitations

93 Latent class analysis (LCA) has been used extensively to

94 Interleukin-18 (IL-18) plasma level and latent class analysis (LCA) have separately been shown t

95 Latent class analysis (LCA) identified subgroups of PEH,

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

97 Latent class analysis (LCA) is a mathematical technique

98 Latent class analysis (LCA) is a type of modeling analys

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

100 A latent class analysis (LCA) model was applied to analyze

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

102 Latent class analysis (LCA) of TTE/hemodynamic parameter

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

104 Latent class analysis (LCA) provides an unbiased statist

105 We used latent class analysis (LCA) to group participants based

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

107 NG, AND PARTICIPANTS: This cohort study used latent class analysis (LCA) to identify clinical subgrou

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

109 from 2004-2014 Nationwide Inpatient Samples, latent class analysis (LCA) was applied to 10 procedure

110 Latent class analysis (LCA) was conducted in a populatio

111 Methods: Latent class analysis (LCA) was independently performed

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

113 Simple latent class analysis (LCA) was used to assign participa

114 Latent class analysis (LCA) was used to classify the par

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

116 Latent class analysis (LCA) was used to identify MetS an

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

118 mine whether wheezing phenotypes, defined by latent class analysis (LCA), are associated with nine 17

119 Latent class analysis (LCA), based on prenatal opioid us

120 Using latent class analysis (LCA), we classified participants

121 on between dietary patterns, derived through latent class analysis (LCA), with visceral adiposity ind

122 acterize the microbiological signatures of a latent class analysis (LCA)-derived periodontal stratifi

123 a cohort of NERD patients with the means of latent class analysis (LCA).

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

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

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

127 otypic associations via multiple regression; latent-class analysis (LCA) to investigate the co-occurr

128 ostic enrichment of both cluster-derived and latent-class analysis (LCA)-derived biological ARDS subp

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

130 henotypes had been previously assigned using latent class analysis.Measurements and Main Results: The

131 First, latent class analysis methodology was applied independen

132 We applied unsupervised latent class analysis methods utilizing baseline clinica

133 A two-class latent class analysis model best fit the population (P =

134 A two-subphenotype latent class analysis model had the best fit in both the

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

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

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

138 n-phenotype differences compared with binary latent class analysis models and ascertained association

139 or the class-defining plasma proteins in the latent class analysis, no correlation was observed with

140 psis phenotypes prior to randomization using latent class analysis of 20 clinical and biomarker varia

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

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

143 We identified 2 sepsis phenotypes using latent class analysis of clinical and protein biomarker

144 iabetes and in diabetes groups, defined by a latent class analysis of diabetes duration, complexity,

145 iabetes and in diabetes groups, defined by a latent class analysis of diabetes duration, complexity,

146 ration and frequency, and using longitudinal latent class analysis of different smoking behaviour pat

147 Latent class analysis of family income, education, occup

148 Latent class analysis of items on the Fagerstrom Test fo

149 -life wheeze trajectories were defined using latent class analysis of longitudinal early-life wheezin

150 s higher- or lower-interventional based on a latent class analysis of survey responses about the freq

151 We then performed a de novo latent class analysis of the 14 BALF biomarkers to ident

152 Using Latent Class Analysis of the PROMIS-10 data, we identifi

153 Latent class analysis of triazole-naive patients identif

154 Latent-class analysis of 613 participants (mean age, 65;

155 Using latent class analysis on behavioral survey responses, we

156 ries were modelled using polytomous variable latent class analysis (poLCA) in both complete-case and

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

158 A latent class analysis produced 2- and 3-class models rep

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

160 Longitudinal latent class analysis revealed 3 grass sensitization tra

161 Latent class analysis revealed 4 distinct preference phe

162 Latent class analysis revealed 4 groups of respondents:

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

164 Latent class analysis revealed physical and mental healt

165 Latent class analysis revealed that the estimated propor

166 Latent class analysis revealed two respondent classes.

167 However, latent-class analysis revealed 2 distinct preference cla

168 Latent-class analysis revealed strong patient preference

169 Latent class analysis showed that the brief set of socia

170 Latent class analysis shows that African American males

171 Latent class analysis stratified by ethnicity identified

172 ession (overall levels of ADHD symptoms), 2) latent class analysis (subclasses of ADHD symptoms by se

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

174 Latent class analysis suggested five schizophrenic syndr

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

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

177 In latent class analysis, the class with child maltreatment

178 In latent class analysis, the sensitivity of DPPT was 93% (

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

180 Using latent class analysis, three groups emerged: diabetic (5

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

182 We used cross-sectional latent class analysis to characterise patterns of bevera

183 We used latent class analysis to characterise the early infectio

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

185 We used Bayesian latent class analysis to estimate the prevalence of LTBI

186 We used Bayesian latent class analysis to estimate the prevalence of LTBI

187 behavioral, and psychosocial traits, we used latent class analysis to identify behavioral phenotypes

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

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

190 We used latent class analysis to identify patients with distinct

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

192 We performed nonsupervised clustering using latent class analysis to identify subgroups of patients

193 We used latent class analysis to identify subtypes of fatigue.

194 We employed latent class analysis to identify the health lifestyle p

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

196 trajectory analysis-a special application of latent class analysis to longitudinal data-among new ant

197 stop shop for investigators seeking to apply latent class analysis to their data.

198 nction." Dietary patterns were identified by latent class analysis using data collected with a valida

199 SES was derived by latent class analysis using family income, occupation or

200 Latent class analysis was applied to 14 disaggregated DS

201 Latent class analysis was applied to detailed symptomati

202 Latent class analysis was applied to nine eating disorde

203 Bayesian latent class analysis was applied to test results to est

204 Bayesian latent class analysis was applied to test results to est

205 Latent class analysis was carried out to determine types

206 A latent class analysis was conducted of 12 adverse childh

207 Latent class analysis was conducted to identify multimor

208 A latent class analysis was conducted to identify patterns

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

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

211 Longitudinal latent class analysis was performed by using pain intens

212 A latent class analysis was performed to identify "latent

213 Latent class analysis was performed to identify phenotyp

214 Latent class analysis was performed to identify risk cla

215 Latent class analysis was performed with prenatal and ea

216 Latent class analysis was performed with the statistical

217 Latent class analysis was used and identified five class

218 to identify distinct trajectories, and joint latent class analysis was used to assess joint patterns

219 Latent class analysis was used to assess the clustering

220 Latent class analysis was used to derive phenotypes base

221 Latent class analysis was used to examine whether physic

222 Latent class analysis was used to generate two latent cl

223 A latent class analysis was used to identify and character

224 Latent class analysis was used to identify groups of pat

225 Latent class analysis was used to identify immune phenot

226 Latent class analysis was used to identify inflammation

227 Latent class analysis was used to identify segments of p

228 Latent class analysis was used to identify socioeconomic

229 Latent class analysis was used to identify subgroups wit

230 Latent class analysis was used to identify subgroups.

231 Latent class analysis was used to identify subphenotypes

232 Latent class analysis was used to identify subtypes base

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

234 Longitudinal latent class analysis was used to identify underlying lo

235 Longitudinal latent class analysis was used to investigate patterns o

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

237 Latent-class analysis was most compatible with the exist

238 Latent-class analysis was used to evaluate the usefulnes

239 Using latent class analysis, we derived dietary patterns based

240 Using Bayesian Latent Class Analysis, we estimated the sensitivity and

241 Using latent class analysis, we identified three distinct clas

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

243 By the latent class analysis, we show three distinct clusters i

244 asses derived from 5 UK-based birth cohorts (latent class analysis) were used to study GxE between th

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

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

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