ライフサイエンスコーパス: covariate

1 mulations and meteorological time series (as covariates).

2 0.0005, with baseline BCVA, CST, and age as covariates).

3 ve) rather than on the absolute scale of the covariate.

4 ata included when heading date was used as a covariate.

5 nd Chronic Health Evaluation III scores as a covariate.

6 linical rejection analyzed as a time-varying covariate.

7 t serum triglyceride and used ethnicity as a covariate.

8 cer treatment was included as a time-varying covariate.

9 nships we included distance from Africa as a covariate.

10 vely normal controls, adjusting for age as a covariate.

11 interactions between treatment and baseline covariates.

12 ion assessed at Y30 after adjustment for key covariates.

13 us test to assess the informativeness of the covariates.

14 standardized to the distribution of baseline covariates.

15 ty score-matched, adjusting for >95 baseline covariates.

16 nsity and glaucoma while controlling for all covariates.

17 ter <=2.5 mum [PM(2.5)]) and other important covariates.

18 ive outcome was not associated with baseline covariates.

19 al hazards models to determine the effect of covariates.

20 motherapy (IC) among clinical and microbiome covariates.

21 hods to predict outcomes based on observable covariates.

22 ogistic regression, controlling for clinical covariates.

23 virus 1, possibly influenced by demographic covariates.

24 PCR, allowing for modification by specified covariates.

25 symptoms were weak after adjustment for all covariates.

26 R from a PCR, optionally including specified covariates.

27 idual gene expression adjusting for clinical covariates.

28 in a 5-year period, and other socioeconomic covariates.

29 to estimate outcomes adjusting for clinical covariates.

30 smoking, and FEV(1) % predicted as clinical covariates.

31 n regarding individual nodes, attributes, or covariates.

32 is a function of a potentially large set of covariates.

33 nd their determinants based on environmental covariates.

34 g multiple logistic regressions adjusted for covariates.

35 This remained true after adjusting for covariates.

36 to all 39 men, adjusting for age and race as covariates.

37 G, HbA(1c), and diabetes while adjusting for covariates.

38 dolutegravir in all matrices and to evaluate covariates.

39 (FC) in 34 users and 19 non-users, employing covariates.

40 cal phase III trial, adjusting for biomarker covariates.

41 tes were modelled as a function of CCF50 and covariates.

42 ffects model to represent the time-dependent covariates.

43 um and adjusted for baseline and in-hospital covariates.

44 fied analyses using sex and APOE epsilon4 as covariates.

45 survival, even after adjustment for clinical covariates.

46 trolling false discoveries and adjusting for covariates.

47 ociated with suicide attempts independent of covariates.

48 levels of outdoor pollutants were treated as covariates.

49 opes ((15) N(2) ) and measured environmental covariates.

50 ate of change in DVA after adjusting for key covariates.

51 odels to adjust for clinical and demographic covariates.

52 age, stage, comorbidities, and socioeconomic covariates.

53 scales and estimated survival based on these covariates.

54 ions were consistent after adjusting for all covariates.

55 count for both individual- and habitat-level covariates.

56 istical significance of associations for all covariates.

57 g treatment and common prognostic factors as covariates.

58 ation analysis with a high-dimension pool of covariates.

59 dels that incorporate physical indicators as covariates.

60 regression models accounted for 14 resident covariates.

61 while controlling for important county-level covariates.

62 thin-ICU correlation, patient- and ICU-level covariates.

63 parental mental illness, with adjustment for covariates.

64 measure of T cell sensitization and clinical covariates.

65 From the model without covariates, 96 communities located mainly in the Cascade

66 ments and Main Results: After adjustment for covariates, a 1-SD decrease in infant tptef/te and VmaxF

67 Among baseline covariates, aBSI (P = 0.01) and ALP (P = 0.001) were sig

68 e independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are ov

69 s study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related

70 Covariate-adaptive FDR control methods with informative

71 ased estimates of the coefficients for other covariates added to the model.

72 We performed covariate adjusted logistic regressions to identify asso

73 In time-dependent covariate adjusted models, post-procedure MALE hospitali

74 Covariates adjusted using multivariable linear regressio

75 r incident ICB symptoms after adjustment for covariates (adjusted hazard ratio [HR] = 1.17, p = 0.458

76 In covariate-adjusted analyses, residence in a cluster was

77 sing multiple informant models, we estimated covariate-adjusted associations of an interquartile rang

78 Covariate-adjusted Bayesian kernel machine regression wa

79 ed using Pearson chi-square tests as well as covariate-adjusted Cochran-Mantel-Haenszel tests.

80 Covariate-adjusted Cox and competing risk regression mod

81 GL, and carbohydrates, were estimated using covariate-adjusted Cox proportional hazard models.

82 Covariate-adjusted hazard ratios (HRs) and 95% CIs were

83 Covariate-adjusted linear and permutation-based models w

84 Consistent findings emerged in covariate-adjusted models of antidepressant treatment, s

85 Respective Bayesian mean covariate-adjusted pCR rates and percentage probability

86 nd super learning were utilized to produce a covariate-adjusted proportion of outcomes for each regim

87 t importance, with the additional benefit of covariate adjustment and multiple testing correction.

88 spective analysis employing propensity score covariate adjustment method of prospectively collected d

89 ssive disorder (MDD), we show that NPDR with covariate adjustment removes spurious associations due t

90 After covariate adjustment, FGF23 was associated with higher L

91 After covariate adjustment, LAA ligation remained a significan

92 nalysis.Measurements and Main Results: After covariate adjustment, self-reported Hispanic patients (n

93 erate-severe or severe food insecurity after covariate adjustment.

94 Thus, the ability to perform covariate adjustments becomes particularly important for

95 oinformatics and medical informatics, namely covariate adjustments.

96 Multistate life table models with covariates age, gender, occupational position, smoking,

97 r mixed model was performed, controlling for covariates (age, sex, body mass index), examining intera

98 over 10-year follow-up according to baseline covariates: age, height, weight, systolic blood pressure

99 After adjustment for potential covariates, ages at alcohol initiation (AAIs) of 18.1-29

100 was used to correlate aBSI with the baseline covariates: alkaline phosphatase (ALP) and prostate-spec

101 h's Phylogenetic Diversity and non-redundant covariate analyses reveal that the serum 1,25(OH)(2)D le

102 icted probability of death was included as a covariate and individual ICU as a random effect.

103 insights into the relations between a set of covariates and compositional data with or without a know

104 regression models and adjust for a range of covariates and fixed effects on the primary sampling uni

105 ing IPTW, cohorts were well balanced for all covariates and health-seeking behavior indicators.

106 Analyses were adjusted for covariates and multiple hypothesis testing.

107 Covariates and outcomes were obtained from the mandated

108 tates to quantify the effects of demographic covariates and social mobility on doubling rates and cas

109 lling for demographic and transplant-related covariates and use of corticosteroids, bisphosphonates,

110 me and the treatment allocation, and all the covariates) and four methods: g-computation, inverse pro

111 rily many repeated observations, can include covariates, and also maintains nominal false positive an

112 dem with a regression model for marker-level covariates, and demonstrate how incorporating these addi

113 nt with each metric relating uniquely to the covariates, and loosely collected species generally prev

114 ictor data types, statistically corrects for covariates, and permits statistical inference and penali

115 ce surfaces, environmental and socioeconomic covariates, and survey data informed a Bayesian prevalen

116 e generally more informative than biological covariates, and the covariates of methylation mean and v

117 nation, allows for adjustment of confounding covariates, and uses permutation-based P-values that can

118 e.g., age) and categorical (e.g., diagnosis) covariates are accommodated in plate/batch/lot artifact

119 s, duration of response (DOR), survival, and covariates are described on the basis of the mITT popula

120 If none of these covariates are effect-measure modifiers on the absolute

121 We find that statistical covariates are generally more informative than biologica

122 ly improve detection power, and if so, which covariates are more relevant for EWAS data.

123 Often, only some covariates are well-balanced, making it unclear whether

124 ed cubic spline with adjustment for the same covariates as in the primary analysis.

125 ion of a treatment effect across levels of a covariate, as measured on a selected scale, against a cl

126 g competence data into statistical models as covariates, as the response variable, and through postmo

127 r model, consisting of previously identified covariates associated to outcome, resulted in a signific

128 Both frameworks compare environmental covariates associated with locations animals visit with

129 ariable selection, for the identification of covariates associated with microbial taxa abundance data

130 th, vendor, and pulse sequence identified as covariates associated with T2.

131 h locations animals visit with environmental covariates at a set of locations assumed available to th

132 rve were used to evaluate the association of covariates at baseline and their change at treatment dis

133 suggest that CEM, although it achieves good covariate balance, might not be optimal for large claims

134 o bias and precision and always created good covariate balance.

135 CEM generally achieved the best covariate balance.

136 pare relative prevalence or bias in observed covariates between the instrument and exposure.

137 ing location) and an individual time-varying covariate (body size).

138 ion in both provinces, after controlling for covariates, boiling with electric kettles was associated

139 at 3.0 T = -6 msec, P = .002) as significant covariates, but it did not identify any association with

140 These tests require investigators to make covariate-by-covariate judgments about the validity of t

141 models to adjust regional brain volumes for covariates, calculate effect sizes, and simulate possibl

142 However, confounding covariates can make patterns related to the scientific q

143 daptive FDR control methods with informative covariates can significantly increase the detection powe

144 simulations suggest that considering all the covariates causing the outcome led to the lowest bias an

145 Together with known clinical covariates, CHIT1 levels explained 12% and 27% of varian

146 After adjustment for covariates, clinic systolic and diastolic BP were strong

147 aortic valve replacement (as time-dependent covariates); comorbidities; medications; and echocardiog

148 y was limited by the lack of availability of covariate data and diverse study contexts and methodolog

149 useful when phase 1 longitudinal outcome and covariate data are available but data on the exposure (e

150 Complete outcome and covariate data were available for 4,428 and 4,369 partic

151 placenta samples with complete exposure and covariate data.

152 contrast, the informativeness of biological covariates depends on specific datasets.

153 Including blood pressure as a covariate did not alter these CBF findings (all P > 0.05

154 The consideration of all the covariates did not decrease the bias but significantly r

155 considered to obtain a valid estimate of the covariate distribution.

156 ite signals as well as other diet and animal covariates (DMI, ME, weight, BW(0.75), DMI/BW(0.75)).

157 clustering by facility and a priori baseline covariates (e.g., age, heart failure, and facility-level

158 After adjusting for covariates, each 10-count increase in accelerometer inte

159 After adjusting for covariates, each 5-mug/dL higher childhood blood lead le

160 Adjusting for covariates, elevated equilibrium (hazard ratio, 0.38 [95

161 s regression with infections as time-varying covariates, estimating hazard ratios and 95% confidence

162 Deterministic trends with dynamic covariates explain over 35% of the variability in DO.

163 ity criteria on whom we had complete data on covariates, exposures, and outcome.

164 regression of the response on a time-varying covariate for each subject.

165 is bias can be eliminated by adding a binary covariate for exposed versus not to the model.

166 The hourly PNC models used covariates for meteorology, traffic, and sulfur dioxide

167 agement Information System and combined with covariates from other sources.

168 2-month weight as mediator, adjusted for all covariates from the primary analysis.

169 chine learning model that predicts (based on covariates gleaned from sexual assault kit questionnaire

170 .012) even after adjustment for time-varying covariate graft loss (aHR, 1.68 [1.08-2.62]; P = 0.022)

171 Covariates grouped in terms of demographics, lifestyle f

172 After adjusting for covariates, high plasma folate and high plasma vitamin B

173 ffected after adjustment for a wide range of covariates; however, controlling for biomarkers, particu

174 biases accounts for previously characterized covariates impacting Hi-C contact counts.

175 , finding that using logistic regression and covariates improves the ability to call enrichment of IS

176 whole-genome/exome sequencing, and clinical covariates in 134 neuroblastoma patient samples at diagn

177 Our approach is based on regressing out the covariates in a manner that avoids 'leakage' during the

178 Age group, race, and gender were included as covariates in multiple logistic regression analyses to c

179 d to evaluate the discriminating strength of covariates in predicting OS.

180 atment, and socioeconomic characteristics as covariates in the analysis.

181 -based method or a model for the outcome and covariates in the multiple regressions.

182 is class, and exercise capacity were used as covariates in the multivariate Cox regression analysis.

183 ionship between the treatment assignment and covariates in the propensity score-based method or a mod

184 Covariates included age, gender, ethnicity, blood pressu

185 Covariates included age, sex, education, depressive symp

186 Covariates included demographic, socioeconomic, lifestyl

187 Covariates included in the model were the number of year

188 ogy factor representing their shared effect, covariates included that factor, prior history of suicid

189 reported, the latter adjusting for baseline covariates including age, education level, marriage leng

190 ntify statistically significant trends, with covariates including age, gender, race/ethnicity, and pr

191 en bone age and anthropometry, adjusting for covariates including age, sex, dietary intake, and morbi

192 Findings were robust to covariates including sex and age.

193 the amount exposed as a single quantitative covariate, including the nonexposed with value zero.

194 95% CI 0.17-1.01) after adjustments for all covariates, including age, gender, BMI, race, depression

195 conditioning set across strata of prognostic covariates, including American Joint Committee on Cancer

196 We controlled for multiple covariates, including gross domestic product, democratis

197 ty after adjustment for clinical and imaging covariates, including right ventricular ejection fractio

198 8, P = 0.014) were taken into account (other covariates, including tacrolimus trough concentrations,

199 In joint modeling analyses adjusting for 16 covariates, including viral cause, a 10% increase in rel

200 ate linear regression analysis, adjusted for covariates, indicated a significant association between

201 for large claims-database studies with rich covariate information; it might be ideal if only a few (

202 Adding covariates into a structural model showed significant pa

203 data to incorporate time-dependent biomarker covariates into the hazard regression approach to diseas

204 when the distribution of the time-dependent covariate is non-Gaussian, as is the case with microbial

205 Causal knowledge on the relation between covariates is required for mediation analysis, and it is

206 rade status, with adjustment for demographic covariates jointly.

207 s require investigators to make covariate-by-covariate judgments about the validity of the IV design.

208 the field measurements with 66 environmental covariate layers to create a global, one-kilometre-resol

209 We used multiple imputation for missing covariates, logistic regression to model the association

210 es jointly: temporal changes in a phenotypic covariate (mass); relationship of mass to breeding proba

211 and alternative methods driven by identified covariates.Measurements and Main Results: We identified

212 The dependence of missing data on these covariates must be considered to obtain a valid estimate

213 mained robust after adjustment for important covariates, namely age; study period; device; presence o

214 most 4-times lower odds after adjustment for covariates (odds ratio, 0.26 for group 2 vs. group 1; 95

215 = 888), we identify statin therapy as a key covariate of microbiome diversification.

216 but it did not identify any association with covariates of age (beta at 1.5 T = 0 msec per year, P =

217 Using linear regression, with covariates of age, sex and education, CAIDE score >6 was

218 multinomial logistic regression to generate covariates of care and viral load status and their margi

219 Third, all covariates of food consumption diversity we tested were

220 ty of interpregnancy interval data and other covariates of interest (age, education, urban or rural r

221 The covariates of interest varied between NS (n = 698), ST (

222 odel the association between gene counts and covariates of interest.

223 ormative than biological covariates, and the covariates of methylation mean and variance are almost u

224 Here, we examined the characteristics and covariates of the cancer cell response to DNMDP.

225 l hazards regression with the adjustment (as covariates) of age, sex, weight, stage, tumor type, tumo

226 d to examine the effect of clinicopathologic covariates on discordance.

227 e biomarkers of a patient in the presence of covariates or factors determining the disease progressio

228 ing behaviour, C-section indication, missing covariates, or familial factors.

229 the association between muscle mass and the covariates over time.

230 aortic valve replacement as a time-dependent covariate, patients with LG-LF and DI<0.25 exhibited a c

231 In all, 24 covariates per SNP/gene pair were included in the AdaPT

232 provide individual patient data on baseline covariates, pre- and postoperative headache scores at me

233 to share a common set of genotype sites and covariates prior to encryption.

234 ndance data and various parameterizations of covariates' prior probabilities of inclusion.

235 After adjustment for significant covariates (race/ethnicity, malignant disease, graft, an

236 regression models to adjust for county-level covariates related to demographics (ie, race and ethnici

237 No significant (P < .01) covariates remained after backward elimination and no ef

238 all 36 studies, none adjusted for nonseptic covariates reported to increase checkpoint molecule expr

239 nt distribution of confounders and any other covariates required for conditional independence of miss

240 of covariance, taking the baseline IOP as a covariate, revealed no differences in postoperative IOP

241 Logistic regression models to determine covariate risk contribution to death following an acute

242 uated towards unity with increasingly larger covariate sets used for confounding control.

243 he individual studies adjusted for different covariate sets, which was the case here.

244 e., sparse data bias)-especially with larger covariate sets.

245 curve (area under the curve, 0.64) for CETS covariate showed that 252 days of coverage (or 3 surveil

246 ion controlling for demographic and clinical covariates showed that cumulative financial strain was p

247 After multivariate analysis adjusting for covariates, Ss+ was still associated with greater risk o

248 of results across methods, including use of covariate stability, can greatly enhance data interpreta

249 ity score matching was performed by using 23 covariates, stratified by estimated glomerular filtratio

250 gnificantly improved by including additional covariates such as performance status and albumin, yield

251 Mammals were most responsive to covariates that accounted explicitly for the vertical an

252 When stratifying by the levels of prognostic covariates, the 60-months CS estimates for disease-free

253 ampling, infant antibiotic exposure or other covariates, the antepartum depressive symptom trajectory

254 After controlling for covariates, the effect of discount eligibility was signi

255 After adjustment for all covariates, the incidence rate ratio was 0.12 (95% CI, 0

256 After additional adjustment for other covariates, the incidence rate ratio was 0.37 (95% CI, 0

257 After adjusting for potential covariates, the pooled HR for the highest compared with

258 Adjusting for covariates, the PRT group exhibited superior long-term o

259 lts obtained by using four different sets of covariates (those causing the outcome, those causing the

260 roach to handling a potentially large set of covariates through a model-based approach to standardiza

261 lysis has shown that weight was not the best covariate to explain variability of rifampicin exposure.

262 hazards regression models with time-varying covariates to compare the likelihood of receiving eye ca

263 were identified with DESeq2 software, using covariates to correct for sex, age, library batches, and

264 similarity), and their change in relation to covariates to test whether defining and conserving biodi

265 test statistics using auxiliary information (covariates) to weight hypothesis tests for association.

266 r adjusting length of stay and mortality for covariates, undocumented immigrants had shorter length o

267 features is computed from the given patient covariates using genetic algorithm optimized ratio and p

268 aptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets

269 SNPs and genes allow us to map the following covariate values to these pairs: GWAS statistics from ge

270 complications arise when the effect sizes of covariates vary on multiple levels (e.g. within vs. amon

271 Binary logistic regression with age as covariate was used to identify SNPs associated with sarc

272 ital heart defects, and health and lifestyle covariates was linked.

273 A general linear model with age and sex as covariates was used to compare the three groups.

274 eral linear mixed-effects model adjusted for covariates was used, as follows: patient characteristics

275 italization records, and pharmacy records as covariates, we controlled for confounding in Cox models

276 In models containing all of the covariates, we find strong and consistent evidence for t

277 Controlling for relevant covariates, we found that persistently elevated leukocyt

278 formation on residential address or relevant covariates, we included 61,447 participants in data anal

279 nteractions between body mass index and each covariate were also evaluated.

280 hospitalization included as a time-dependent covariate were developed to estimate hazard ratios for o

281 Included covariates were age, hypertension, diagnoses including o

282 zed estimating equations adjusted for design covariates were conducted to examine associations betwee

283 Baseline covariates were explored as risk factors for RSV febrile

284 A priori determined covariates were forced expiratory volume in 1 second and

285 Covariates were introduced after adjusted model selectio

286 tinomial regression was used to assess which covariates were related to greater ECD decline.

287 f PTSD, depression, and anxiety, and limited covariates were reported in the included studies.

288 ymptoms at birth, and long-term sequelae; 31 covariates were tested.

289 ith time-updated information on exposure and covariates were used to calculate the adjusted hazard ra

290 .5) prediction formulas, with BMI and age as covariates, were developed using data from 17,359 non-Hi

291 efficients, both unadjusted and adjusted for covariates, were extracted from studies examining the pr

292 uals, heritability estimation, and including covariates when testing association.

293 verse proportion of treatment weighting as a covariate, whereas in the pharmacogenomic study, HRs wer

294 Existing models assume linear effect of covariates, which is restrictive and may not be sufficie

295 Each requires the introduction of a set of covariates, which remains difficult to choose, especiall

296 same examinations once.Associations between covariates with DSPN at entry were assessed using the ch

297 of BL and time-dependent (TD) pRBD and other covariates with the development of ICB symptoms was eval

298 Kaplan-Meier method, and the association of covariates with the hazard of death was assessed using m

299 Mean centring of covariates within subjects offers a useful approach in s

300 opensity score matching based on 23 baseline covariates yielded 4,301 well-balanced pairs.