ライフサイエンスコーパス: covariate

1 al MBF after adjustment for CFR and clinical covariates).

2 se probability weighting, and use of PS as a covariate.

3 nerated and examined in relation to DUP as a covariate.

4 ith the inclusion of a latent spring weather covariate.

5 egression analysis with gestational age as a covariate.

6 r status after diagnosis as a time-dependent covariate.

7 s were re-evaluated adjusting for BMI as the covariate.

8 WM-LL (r=-0.30, p<0.05) when using age as a covariate.

9 ee relatives and allows for the inclusion of covariates.

10 or patient-, surgeon-, and institution-level covariates.

11 isease, MAPT haplotype, and APOE genotype as covariates.

12 random effect of intercepts were included as covariates.

13 ncy of smoking and vaping and other relevant covariates.

14 for age, sex, and statistically significant covariates.

15 from shared family confounders and measured covariates.

16 ng more than 125 baseline and time-dependent covariates.

17 , adjusted for clinical and sociodemographic covariates.

18 of Inform Me exposure after controlling for covariates.

19 tatistically significant after adjusting for covariates.

20 nation with climatic, landform and lithology covariates.

21 were used to calculate hazard ratios for all covariates.

22 er controlling for multiple sociodemographic covariates.

23 nt, and other patient- and procedure-related covariates.

24 with or without adjustments for appropriate covariates.

25 s adjusted for genetic ancestry and selected covariates.

26 02 +/- 0.37, p = .005) after adjustments for covariates.

27 ower serum log(TSH)mIU/L with adjustment for covariates.

28 ints, as influenced by network structure and covariates.

29 sex, age, and height; technician; and other covariates.

30 ing for facility clustering and prespecified covariates.

31 mographic, computed tomography, and surgical covariates.

32 concentrations and infant AGD, adjusting for covariates.

33 or osmolality were used to standardize or as covariates.

34 leep duration at follow-up, independently of covariates.

35 ing built environment factors and individual covariates.

36 t CD4 count and VL analyzed as time-changing covariates.

37 ated to selective prescribing and unobserved covariates.

38 ment of bone scintigraphy and other relevant covariates.

39 nd inclusion of these metrics as independent covariates.

40 ted with exercise volume after adjusting for covariates.

41 the benchmark of R(2) > 36% with or without covariates.

42 ing sex, medication use, and scanner site as covariates.

43 f DSST impairment after full adjustment with covariates.

44 s less frequently, even after adjustment for covariates.

45 difference (P = 0.28) when adjusted for all covariates.

46 ites, adjusted for individual and ecological covariates.

47 nd birth year category were also examined as covariates.

48 were estimated with adjustment for measured covariates.

49 er adjustment for offline bullying and other covariates.

50 effect was similar across all subgroups and covariates.

51 covariates and (3) climate and dynamic LULC covariates.

52 ession models before and after adjusting for covariates.

53 the measures of impulsivity and to identify covariates.

54 a multivariable model controlling for other covariates.

55 ependent of other common clinicopathological covariates.

56 le by treatment type, adjusting for relevant covariates.

57 covariates and subsequently for in-hospital covariates.

58 ers, and demographic factors were considered covariates.

59 ity (3.6% vs. 2.5%, P <0.001), adjusting for covariates.

60 ther combined or individual) controlling for covariates.

61 etreatment clinical, laboratory, and imaging covariates.

62 .66; 95% CI: 1.86, 3.80) after adjusting for covariates.

63 were well balanced in terms of all measured covariates.

64 ome association, conditional on the measured covariates.

65 ared three types of models: (1) only climate covariates, (2) climate and static LULC covariates and (

66 th available cannabis use data and essential covariates, 26.8% were FRC users.

67 n of Diseases, Tenth, Revision, diagnosis as covariate-38 different common and rarer cancers, with br

68 After adjusting for various covariates, a 10-fold increase in hepatic triglyceride c

69 djustment for transplant as a time-dependent covariate abolished the higher risk of death in non-Hisp

70 mographic, physical, dietary, and behavioral covariates across the Asian subgroups.

71 oportional hazards model with time-dependent covariates (adjusted hazard ratio: 2.43 [1.49-3.95], P =

72 k of death by day 28 after adjustment for 16 covariates (adjusted odds ratio, 1.77; 95% CI, 1.17 to 2

73 he iron-supplemented groups were combined in covariate-adjusted analyses, the mean SBP in LBW childre

74 not receive GA versus those who received GA (covariate-adjusted cOR 1.53, 95% CI 1.14-2.04, p=0.0044)

75 We estimated covariate-adjusted differences in neurobehavioral outcom

76 Covariate-adjusted estimates of associations with a doub

77 Covariate-adjusted geometric means for the sum of the 4

78 , non-Hispanic black, Mexican American), and covariate-adjusted geometric means were computed by usin

79 We fit covariate-adjusted linear regression models and conducte

80 unterparts without delirium; at 1 month, the covariate-adjusted mean difference on the physical funct

81 Based on the covariate-adjusted model, if the PHS-IR label did not ex

82 In covariate-adjusted models fit on 381 eligible subjects,

83 We estimated the covariate-adjusted percent change in urinary concentrati

84 ater in foods, and total water with multiple covariate-adjusted risk of mortality from all causes.

85 Across different procedures, the covariate-adjusted risk of new persistent opioid use in

86 r adjusting for urinary flow rate, and using covariate-adjusted standardization resulted in null asso

87 ary flow rate or analytic strategies such as covariate-adjusted standardization should be considered.

88 lso used a recently proposed method known as covariate-adjusted standardization.

89 Covariate adjustment and matching performed well in all

90 ting equations for logistic regressions with covariate adjustment were applied to relate ROP to preec

91 ) to compare the performance of conventional covariate adjustment with 4 common PS matching, stratifi

92 are not necessarily superior to conventional covariate adjustment, and care should be taken to select

93 After covariate adjustment, attending physicians' (n = 40) med

94 ave theoretical advantages over conventional covariate adjustment, but their relative performance in

95 in the study, after matching and model-based covariate adjustment, compared with each control conditi

96 After covariate adjustment, opioid users (compared with those

97 Using covariate-adjustment Cox proportional hazards models, we

98 Results were similar after baseline covariate-adjustment for genetic ancestry, sex, age, wei

99 dels with consideration of batch effects and covariates age, sex, and ancestry proportions.

100 After controlling for covariates (age, sex, education, race, smoking, physical

101 ative binomial regression including clinical covariates (age, sex, percent predicted FEV1, self-repor

102 at day 90 in ordinal regression adjusted for covariates (age, sex, pretreatment NIHSS score, target o

103 sed on probabilistic principal component and covariates analysis (PPCCA).

104 as used to examine associations between each covariate and low IOP.

105 mate covariates, (2) climate and static LULC covariates and (3) climate and dynamic LULC covariates.

106 urther addressing the roles of environmental covariates and beta diversity.

107 rough adjustment for neighborhood behavioral covariates and decomposition of PM2.5 into 2 spatiotempo

108 adiposity among women, after adjustment for covariates and energy misreporting.

109 ther available information, such as clinical covariates and environmental predictors, are paramount t

110 , and a second model adjusted for additional covariates and estimated the effects of isocaloric macro

111 ng results and did not handle time-dependent covariates and history of treatment.

112 This pattern persisted after adjustment for covariates and in an analysis that included beneficiarie

113 d estimating equations adjusted for baseline covariates and included a test for different slopes of d

114 rds (Cox) model with adjustment for relevant covariates and median follow-up of 16.1 months, DM was a

115 over time, even with adjustment for baseline covariates and stroke and MI occurrence during follow-up

116 e outcomes, adjusting initially for baseline covariates and subsequently for in-hospital covariates.

117 s model, following sequential adjustment for covariates and testing for an age-sex interaction.

118 ol drinking was observed after adjusting for covariates and that abstinence was associated with decli

119 e regression modeling was used to adjust for covariates and to identify relationships between RPE-BM

120 illness in offspring, adjusting for measured covariates and unmeasured confounding using family-based

121 control was made for demographic and health covariates and was almost entirely eliminated when contr

122 ed tests to identify potentially confounding covariates, and logistic regression models were used to

123 ercentage points lower (after adjustment for covariates, and relative to the pretreatment period) in

124 fter adjustment for maximal MBF and clinical covariates; and adjusted hazard ratio, 1.03; 95% CI, 0.8

125 en enoxaparin was analyzed as a time-varying covariate, anticoagulation was associated with a >13-fol

126 The regression covariates are a large set of potential haplotypes and f

127 A primary model was fitted by using the same covariates as reported in the published studies, and a s

128 rolling for pregnancy, maternal and paternal covariates, as well as sibling comparisons, timing of ex

129 Covariate associated with significantly less improvement

130 In the training set, covariates associated with 1-year overall mortality at a

131 Covariates associated with changes in miniAQLQ scores af

132 Cox regression analysis was used to identify covariates associated with OS.

133 In sibling comparisons with within-family covariates, associations were substantially weaker and n

134 ults and showed good performance in terms of covariate balance (PS matching) and controlling confound

135 ol confounding can be assessed by evaluating covariate balance across exposure groups after PS adjust

136 proach using matching weights generated good covariate balance between those who did and those who di

137 ensity scores to adjust for the imbalance in covariate baseline values between these two groups.

138 ion and slower gait withstood adjustment for covariates (beta = -0.068, P = .03 and beta = -0.074, P

139 differences in transplant and pretransplant covariates between induction and no induction groups.

140 After adjusting for covariates, both exo-E415G and plasmepsin 2-3 markers si

141 escribing node centrality, but also included covariates breaking the network metrics into subsets tha

142 Other covariates (breast cancer risk factors, clinical risk fa

143 %) and are not explained by any life-history covariates but tend to be driven by external perturbatio

144 95% CI-0.69, -0.12; p < 0.005; adjusted for covariates) but not with radiographic osteoarthritis.

145 ity after control for demographic and health covariates, but the association trended towards a higher

146 After adjustment for covariates, cardiology care was associated with reductio

147 Demographic covariates, cardiovascular risk factors, and cardiac MR

148 lity of treatment-weighted adjustment for 51 covariates, conscious sedation was associated with lower

149 After controlling for covariates, COPD was not found to be associated with poo

150 A time-dependent covariate Cox model was used to determine the effect of

151 g the development of IBD as a time-dependent covariate, Crohn's disease and no IBD (both vs ulcerativ

152 For the 386 patients without missing covariate data among the 400 patients within the matched

153 proximity to sulfur application and relevant covariate data were available for 237 and 205 children f

154 lity control, 780 samples with phenotype and covariate data were included in the discovery stage, whe

155 trolled for all individual- and county-level covariates, decedents injured by non-firearm mechanisms

156 Adjusting for demographic and injury covariates, depression symptoms at the time of injury pr

157 Rate ratios were adjusted for covariates (diabetes mellitus, myocardial infarction, st

158 Adjustment for LVH as a time-varying covariate did not substantially attenuate the effect of

159 ultivariable analysis adjusting for clinical covariates, DRD association with PFS remained significan

160 a prespecified analytic plan for exposures, covariates, effect modifiers, and analysis, and the find

161 and all traits were adjusted for significant covariate effects of age and sex.Carotenoid concentratio

162 ions were detected in models with additional covariates.Eight gene-macronutrient interactions were id

163 pathways; 2) Use of a regression model whose covariates embed all method-driven outcomes to predict a

164 nd sex forced into all models and additional covariates evaluated by using the stepwise option for th

165 ta-derived models incorporating HIV-specific covariates exhibited weak calibration in a validation sa

166 ression models with adjustment for important covariates extracted from the database.

167 as observed; IPW conditional on exposure and covariates failed to correct estimates.

168 rs4957796 TT genotype remained a significant covariate for the 90-day mortality risk in the multivari

169 t whether exposure to antibiotics would be a covariate for this association.

170 Univariate analysis was used to identify covariates for a logistic regression model predictive of

171 data were regressed on ME/CFS severity plus covariates for age, sex, race, and an assay property of

172 d little to the predictive value of clinical covariates for exacerbations.

173 SAOMs can include effects and covariates for individuals, dyads and populations, which

174 Here we developed covariates for multiphenotype studies (CMS), an approach

175 dentified by analysis of variance, including covariates for RNA quality, sex, and clinical site, and

176 ropensity score matching to balance baseline covariates for the 2 comparison groups (deployed and non

177 nd aMED scores in women after adjustment for covariates: for the highest vs lowest quintiles, the haz

178 In multivariate analyses adjusted for covariates, frailty was associated among HIV-infected me

179 atent class allocation problem and present a covariate free class allocation approach based on the di

180 (after further exclusion of 334 with missing covariates) from six longitudinal cohorts with 5165 long

181 (further excluding 14 patients with missing covariates) from three longitudinal cohorts with 19 127

182 th differences apparent after adjustment for covariates (hazard ratio, 0.82; 95% confidence interval,

183 Cox regression adjusted for sociodemographic covariates, health behaviors, and chronic conditions.

184 n a competing risks model adjusted for known covariates, high CXCL9 levels measured in the peripheral

185 e interval [CI]: 0.79 to 0.85; adjusting for covariates, HR: 0.90; 95% CI: 0.87 to 0.93).

186 n per-plot counts of each life stage and the covariates hypothesized to affect abundance.

187 ollowing multivariate analyses adjusting for covariates IL6, interleukin 1beta (IL1beta), and interle

188 rocess, treating estimated change as a fixed covariate in a survival model.

189 Although SES has long been used as a covariate in human brain research, in recognition of its

190 ice criteria for surrogacy for the surrogate covariate in the adjusted model for all-cause mortality:

191 ensectomy was considered as a time-dependent covariate in the analysis.

192 After adjusting for 34 covariates in a Cox proportional hazards model, borderli

193 lity after adjusting for clinically relevant covariates in a Cox proportional hazards model.

194 even after accounting for baseline clinical covariates in multivariable models that incorporated LA

195 t report a justification for including these covariates in the PS.

196 comparing the relative strength of different covariates in their association with deer recruitment.

197 Other covariates included age, sex, race/ethnicity, anxiety or

198 Patient-level covariates included age, sex, race/ethnicity, comorbidit

199 Covariates included demographic, clinical, and radiograp

200 ations between adiposity and sleep duration (covariates included demographics, health behaviours, and

201 Covariates included demographics, hospital course (e.g.,

202 ts assessed, 20% (n = 24) did not detail the covariates included in the PS and 77% (n = 100) did not

203 AP exposure and outcomes after adjusting for covariates including body fat percent.

204 ional-hazards regression models adjusted for covariates including patient and donor risk factors and

205 After adjustment for covariates (including surgery), there was no difference

206 ction on viral load; (iii) we controlled for covariates, including age and sex, which may inflate est

207 riable logistic regression was used to model covariates, including anti-TNF agent use, on the occurre

208 In fact, after controlling for environmental covariates, increases in biomass with biodiversity are s

209 However, after adjusting for covariates, Indian persons had, on average, 0.18-mmHg hi

210 January 1, 1997, and December 31, 2011, with covariate information available, 7592 (0.5%) were diagno

211 39 years) in 2,574 households with complete covariate information were analyzed.

212 algorithm, and selection of the significant covariates is based upon the assessment of posterior pro

213 n median perceived "sometimes." Adjusted for covariates, it was perceived more often by nurses and ju

214 SVR was considered as a time-dependent covariate; its effect on outcome was assessed by the Cox

215 tial least square regression models included covariates known to affect craniofacial shape.

216 ns with walking speed were maintained in all covariate models (fully adjusted model: risk ratio, 0.89

217 After adjusting for covariates, multivariate analysis identified black race

218 All allow for adjustment of additional covariates not subjected to thresholding.

219 nt after SMILE after adjusting for all other covariates (odds ratios, 5.58, 4.80, 1.41, 3.06, and 2.1

220 ificance in Cox models when adjusted for the covariates of age and MYCN gene copy number.

221 structure-by adding an analysis to identify covariates of community turnover.

222 We identified key covariates of geographical variation in polio transmissi

223 5-HT1BR expression and sex were significant covariates of impulsivity.

224 Data on covariates of interest were extracted from the medical r

225 ir first hospitalization were balanced on 44 covariates of propensity score.

226 s with classical results, lacks controls for covariates of tree height, and can be explained alternat

227 plied to determine the effects of individual covariates on rates of clinical events, with time-to-eve

228 birth with adiposity, adjusting for baseline covariates only, and 2) made additional regression adjus

229 e incorporated several biologically relevant covariates, only height, weight, and admitting hospital

230 missing on diabetes, falls history, or other covariates or had ungradable fundus photographs and were

231 models incorporating baseline or in-hospital covariates (P > 0.2).

232 djusted for other cancer therapies and other covariates, patients with ADT treatment had no increased

233 After adjustment for baseline covariates, patients with cAVB experienced an increased

234 tein distribution and adjusted for potential covariates.Physical performance deteriorated over 3 y wi

235 anthropometric assessments that incorporated covariate predictors, polynomial terms for age, and prod

236 Analyses included baseline covariates: race, education, smoking status, diabetes, a

237 ncluding all SNPs, SNP-sex interactions, and covariates related to testing conditions.Results:FADS, r

238 osed" groups so that differences on observed covariates resemble differences between the actual expos

239 d after adjustment for LVH as a time-varying covariate, respectively).

240 With adjustments for covariates, results from Cox proportional hazards models

241 F risk scores, LDLR, and statin treatment as covariates, revealed a positive linear association betwe

242 The covariate set contained degree, a classic network metric

243 Other covariates showed inconsistent patterns of association w

244 ations between its parameters and researcher covariates, showing that departmental prestige predicts

245 , 2017, with controls along several baseline covariates such as adolescent IQ, family background, and

246 nce regions even after controlling for other covariates such as age, gender, education, and Mini-Ment

247 llows explicit control of effects from other covariates such as genetic background.

248 adjustment is made for potentially relevant covariates, such as body mass index.

249 ion >5 mg/L or AGP concentration >1 g/L) and covariates, such as demographics, reported illness, and

250 gotic associations were further adjusted for covariates, such as early alcohol or nicotine use, early

251 lp researchers discover sample attributes or covariates that are factors driving the main variability

252 eralized additive mixed models, we evaluated covariates that could affect growth rates; body size, di

253 We evaluate the effect of adjusting for covariates that have an unknown relationship with gene e

254 e population variation and identify cellular covariates that influence the stress-responsive transcri

255 analyses, including treatment arm, baseline covariates that were significant predictors for ADHF inc

256 raphic confounders and parental psychosocial covariates, the hazard ratio for all-cause mortality in

257 After adjustment for all covariates, those in the 20- to 29-year age range showed

258 allows straightforward incorporation of the covariates through a log-linear regression parametrizati

259 Besides adjustment for baseline covariates, time-varying covariates were also considered

260 d before and after each block, was used as a covariate to assess fatigue-related brain activation.

261 (GAMM) which allowed the effects of multiple covariates to be modelled as linear or smooth non-linear

262 ressions were adjusted for a priori selected covariates to determine differences by frailty and HIV s

263 More importantly, TASC is able to adjust for covariates to further eliminate confounding that may ori

264 and used logistic regression to relate those covariates to lamb survival.

265 rtional hazards modeling with time-dependent covariates to total, respiratory, and sudden infant deat

266 control for numerous demographic and health covariates, together with other R/S variables, attending

267 After adjusting for covariates, total reproductive duration in years was inv

268 We built covariates using contact network metrics, demographic in

269 or multiple patient, hospital, and procedure covariates using supervised principal components regress

270 With plant height as a covariate, vegetative biomass of ASP and SSP did not dif

271 al hazards model in which the impact of each covariate was adjusted for that of all others.

272 e-matching analysis based on 28 pretreatment covariates was performed and 461 matching pairs were der

273 The rate of missing data for potential covariates was reported in 9% of articles.

274 ive variability and used as a time-dependent covariate) was associated with an increase in the risk o

275 Baseline ePAD, independent of other covariates, was a significant predictor of mortality (ha

276 s modeling, with adjustment for time-updated covariates, was used to estimate risk for incident diabe

277 After adjusting for covariates, we found that maternal influenza infection (

278 After adjusting for covariates, we found that maternal smoking prior to pret

279 Following adjustment for covariates, we observed no significant cross-sectional r

280 ensity matching and adjusting for unbalanced covariates, we used conditional logistic regression and

281 djustment for batch effects, cell types, and covariates, we used robust linear regression models to e

282 stment for baseline covariates, time-varying covariates were also considered and led to comparable re

283 ter inverse probability treatment weighting, covariates were balanced, and the association between re

284 ltivascular imaging data along with clinical covariates were collected from 4,052 participants.

285 LEX and steroids treatment as time-dependent covariates were entered in: (1) a Cox model to investiga

286 Missing covariates were imputed.

287 n all 41 serum PLFAs and participant-related covariates were initially included in the model for sele

288 rent asthma) was measured at time t + 1, and covariates were measured at time t - 1 or at baseline.

289 Demographic and physiological covariates were measured in a discovery set of community

290 whereas more commonly collected demographic covariates were not.

291 Allergic outcomes and covariates were recorded through interviews and clinical

292 agnosis, time to diabetes diagnosis, and all covariates were self-reported.Between January 2002 and S

293 body mass index, and glycated hemoglobin as covariates were used to account for relevant confounders

294 Time-to-event analyses with time-updated covariates were used to calculate hazard ratios (HR) wit

295 69 SPRINT participants with complete data on covariates were utilized for model development, and data

296 when considering age and T2 lesion volume as covariates) were associated with IPS impairment.

297 prescriptions) of statins as a time-varying covariate with 1-year lag.

298 using a log-rank test without adjustment for covariates, with the primary comparison between sham con

299 nd settings, and factors affecting safety as covariates within a Bayesian hierarchical model to estim

300 Usage of time-averaged disease activity as a covariate would increase the power of studies to identif