ライフサイエンスコーパス: analysis of covariance

1 trolling for age (F(1,93) = 6.25, P = 0.014, analysis of covariance).

2 (n = 32) compared with the WM group (n = 25; analysis of covariance).

3 ations in ischemic blood flow (p = 0.003, by analysis of covariance).

4 .96 hour; 95% CI, 0.56-1.37 hours; P < .001, analysis of covariance).

5 r the effect of collateral flow (p = 0.0002, analysis of covariance).

6 47) mumol/L in the control group (p = 0.017, analysis of covariance).

7 ntly differed between treatments (P<0.001 by analysis of covariance).

8 1500 mg IW-3718 and placebo groups (P = .04, analysis of covariance).

9 DESIGN Case-control analysis of covariance.

10 ng a paired t test, multilevel analysis, and analysis of covariance.

11 Statistical analysis was performed by using analysis of covariance.

12 Cox regression models, and repeated measures analysis of covariance.

13 ons were decomposed by Roy Bargmann stepdown analysis of covariance.

14 nd baseline satiation were covariates in the analysis of covariance.

15 d cotwins and controls were determined using analysis of covariance.

16 d colony forming unit counts were made using analysis of covariance.

17 ed using a nonparametric randomization-based analysis of covariance.

18 n the change from baseline were evaluated by analysis of covariance.

19 LVIDd and EF were analyzed by quartiles from analysis of covariance.

20 values and subject groups were analyzed with analysis of covariance.

21 orbent assays (ELISAs) and compared by using analysis of covariance.

22 of teeth present, and periodontal status by analysis of covariance.

23 s were assessed with regression analysis and analysis of covariance.

24 Results were analyzed using analysis of covariance.

25 es were conducted by logistic regression and analysis of covariance.

26 um, and frontoparietal cortices, as shown by analysis of covariance.

27 ed values were analyzed with a mixed-effects analysis of covariance.

28 f other major medical conditions was done by analysis of covariance.

29 ype were analyzed using repeated measures of analysis of covariance.

30 er 5 years, as assessed by repeated-measures analysis of covariance.

31 Data were analyzed using 2-way mixed analysis of covariance.

32 measures of development were tested by using analysis of covariance.

33 een SLIT and placebo was assessed through an analysis of covariance.

34 l linear model such as linear regression and analysis of covariance.

35 dness) and species-specific intercepts using Analysis of Covariance.

36 um, and frontoparietal cortices, as shown by analysis of covariance.

37 nces from the placebo group were analyzed by analysis of covariance.

38 (never, ever) using analysis of variance and analysis of covariance.

39 ary efficacy end point was analyzed by using analysis of covariance.

40 yze the directionality of the interaction by analysis of covariance.

41 ts were analyzed using spline regression and analysis of covariance.

42 es in brain activation were identified using analysis of covariance.

43 patient's global assessment, analyzed using analysis of covariance.

44 cal analyses were based on repeated-measures analysis of covariance.

45 etween CP and H tissues was calculated using analysis of covariance.

46 In analysis of covariance, 1p/19q co-deletion status was th

47 comparison of between-groups differences by analysis of covariance adjusted for baseline tHcy levels

48 and Primary Intelligence and compared using analysis of covariance adjusted for child sex, prematuri

49 Analysis of covariance adjusted for demographics, infect

50 y change was analyzed with repeated-measures analysis of covariance adjusted for depression symptoms,

51 red analyzer and compared across groups with analysis of covariance adjusted for infant and maternal

52 Analysis of covariance adjusted for plasma folate, vitam

53 Groups were compared using 1-way analysis of covariance adjusted for sex.

54 Analysis of covariance, adjusted by baseline PI, was the

55 ity (RD), were compared between groups using analysis of covariance, adjusted for age, age squared, a

56 We performed analysis of covariance, adjusted for model for end-stage

57 s across GOSE categories were compared using analysis of covariance adjusting for age, sex and educat

58 umes and treatment response were tested with analysis of covariance adjusting for baseline Dementia R

59 Secondary outcomes were analyzed by analysis of covariance adjusting for subject baseline va

60 in scores were compared between groups using analysis of covariance, adjusting for pain score after s

61 europsychiatric conditions were tested using analysis of covariance, adjusting for sex, age, and gene

62 In some analyses for years 1-3, analysis of covariance adjustment indicated that this DS

63 were compared using analysis of variance and analysis of covariance (age and weight as covariates).

64 Through the use of analysis of covariance, all (18)F-FDG PET brain images o

65 A repeated-measures analysis of covariance allowing unequal slopes was used

66 Regression and analysis of covariance analyses investigated relationshi

67 Analysis of covariance analysis showed there was no sign

68 Analysis of covariance analysis was used to investigate

69 -way analysis of covariance and multivariate analysis of covariance analysis) were employed with a Tu

70 Analysis of covariance (ANCOVA) adjusted for age and Cox

71 Using analysis of covariance (ANCOVA) after last observation c

72 Analysis of covariance (ANCOVA) analyses tested the inte

73 Analysis of covariance (ANCOVA) analysis revealed that d

74 Analysis of covariance (ANCOVA) assessed energy-adjusted

75 dified intention-to-treat population with an analysis of covariance (ANCOVA) model with treatment gro

76 ence intervals (CIs) were estimated using an analysis of covariance (ANCOVA) model.

77 s from baseline were commonly assessed using analysis of covariance (ANCOVA) or mixed models for repe

78 A One-way Analysis of Covariance (ANCOVA) was conducted to evaluat

79 Analysis of covariance (ANCOVA) was used to determine if

80 obability tests and analysis of variance and analysis of covariance (ANCOVA) were employed.

81 d t tests, analysis of variance (ANOVA), and analysis of covariance (ANCOVA) were used to assess with

82 .00004) between AD and control brains, using analysis of covariance (ANCOVA) with age as covariate.

83 ssociation analysis (p = 1.56 x 10(-4) in an analysis of covariance (ANCOVA) with an adjustment for u

84 Results show that, in general, analysis of covariance (ANCOVA) yields greater power tha

85 The recommended solution is an analysis of covariance (ANCOVA), but it is rarely used,

86 group differences using five-level, one-way analysis of covariance (ANCOVA), followed by post hoc t

87 nts and comparison subjects were compared by analysis of covariance (ANCOVA).

88 using paired t test, independent t test, and analysis of covariance (ANCOVA).

89 significant synergistic reduction [P<0.0001, analysis of covariance (ANCOVA)] in residual tumor volum

90 to task performance, cognitive score or age [analysis of covariance (ANCOVA)F (2, 18) = 8.44, P = 0.0

91 e coherence (ITPC) were examined using mixed analysis of covariance and Bayesian analyses controlling

92 Analysis of covariance and exact Mann-Whitney tests were

93 res for the cognitive tests were analyzed by analysis of covariance and Kruskal-Wallis analysis; the

94 Analysis of covariance and logistic regression models te

95 Analysis of covariance and logistic regression were appl

96 By analysis of covariance and multiple regression analysis,

97 Two statistical approaches (one-way analysis of covariance and multivariate analysis of cova

98 Analysis of covariance and multivariate linear regressio

99 between 1990 and 2000 and analyzed by using analysis of covariance and multivariate regression metho

100 Data analysis used repeated measures analysis of covariance and nonparametric tests of trend.

101 Analysis of covariance and paired-sample t tests were us

102 Statistical analyses included analysis of covariance and Pearson correlation, correcte

103 groups with low and high CER scores by using analysis of covariance and quartiles of body weight to a

104 rding to contrasts after a repeated-measures analysis of covariance and random regression with the us

105 treatment to end of study was compared using analysis of covariance and regression analysis adjusting

106 ure DLB, DLB+AD and pure AD using univariate analysis of covariance and separate logistic regression

107 Analysis of covariance and Spearman rank correlation tes

108 ed, with age and education controlled, in an analysis of covariance and subgroup matching.

109 ted with these conditions was compared using analysis of covariance and t statistics.

110 Analysis of covariance and t-tests were used for group c

111 etween-species responses were compared using analysis of covariance and trait-gradient analysis.

112 Statistical (analysis of covariance) and clinical effects (reliable c

113 R values were compared by using both linear (analysis of covariance) and log-linear (analysis of cova

114 ed using a nonpaired Mann-Whitney U test, an analysis of covariance, and a Pearson chi2 test.

115 ing between the 2 waves were conducted using analysis of covariance, and multiple regression analysis

116 Data were analyzed by using t tests, analysis of covariance, and multiple regression.

117 Wilcoxon rank sum, analysis of covariance, and permutation data analyses we

118 partial correlations, analysis of variance, analysis of covariance, and t tests.

119 subjected to repeated-measures multivariate analysis of covariance, and the Pearson product moment c

120 ge disequilibrium score regression and local analysis of covariance annotation.

121 Partial correlations and repeated-measures analysis of covariance assessed the relation between cha

122 er-dose treatment group versus placebo using analysis of covariance at each relevant time point.

123 d T2 maps were compared between groups using analysis of covariance at each voxel, with age and ventr

124 Treatment effects were compared using analysis of covariance (baseline colonic transit as cova

125 ative genetic model-fitting was used for the analysis of covariance between BMI and WC.

126 Univariate methods, such as the t test or analysis of covariance, cannot evaluate the efficacy of

127 An analysis of covariance controlling for BMI showed that a

128 lity traits were examined using multivariate analysis of covariance, controlling for marker-marker in

129 Multivariate analysis of covariance, controlling for sex, age, educat

130 g potential) was analyzed using multivariate analysis of covariance, controlling for the effects of a

131 A repeated-measures multivariate analysis of covariance covaried for change in total ener

132 Mixed-effect analysis of covariance crossover models were used to tes

133 Analysis of covariance demonstrated that offspring of pr

134 mg group, +0.76%; P = 0.223 and P = 0.403 by analysis of covariance) did not reach significance.

135 Using analysis of covariance, early physical therapy showed im

136 Analysis of covariance examining change at 6 weeks, 3 mo

137 with MDD as compared with control subjects [analysis of covariance: F(1,23) = 5.161, p = .033].

138 re more likely to quit smoking (multivariate analysis of covariance, F8,69 = 4.5; P < .001), regardle

139 lyzed throughout the whole brain by using an analysis of covariance family-wise cluster corrected for

140 Scores were analyzed using an analysis of covariance for change from baseline at end p

141 Analysis of covariance for mixed models was used with th

142 Analysis of covariance for total cerebral volume demonst

143 nd FFM was significantly greater (P = 0.027, analysis of covariance) for HIV-infected subjects [REE (

144 Analysis of covariance found a significant main effect o

145 lculated as a percentage of cerebellar rCBF, analysis of covariance found decreases in HD caudate den

146 Analysis of covariance found that the lower severity of

147 Analysis of covariance found the multivariate model that

148 Analysis of covariance, including potentially confoundin

149 Analysis of covariance indicated a significant associati

150 Analysis of covariance indicated neither age nor sex was

151 Analysis of covariance indicated that ambulatory status

152 Analysis of covariance indicated that density and the li

153 Analysis of covariance indicated that dissociation had a

154 An analysis of covariance indicated that in the entire coho

155 As in previous studies, analysis of covariance indicated that the subjects in th

156 e was assessed using logistic regression and analysis of covariance (intent-to-treat).

157 size the importance of incorporating RTM and analysis of covariance into the design and reporting of

158 onders regarding improvement in symptoms via analysis of covariance irrespective of the treatment rec

159 We then used multivariate analysis of covariance (MANCOVA) test for sex difference

160 At the cross-sectional level, multivariate analysis of covariance (MANCOVA) was conducted to examin

161 ecause of animal dropout), repeated-measures analysis of covariance may fail to detect a treatment ef

162 power toothbrush delivered an adjusted (via analysis of covariance) mean difference between baseline

163 Nonparametric Kruskal-Wallis tests and analysis of covariance methods were used to evaluate the

164 Analysis of covariance methods were used to obtain mean

165 Analysis of covariance methods were used to obtain mean

166 -voxel basis with a one-tailed fixed-effects analysis of covariance model adjusted for age.

167 onfidence interval, -73% to 110%]; P=0.77 in analysis of covariance model adjusted for baseline prote

168 rlier, smaller study that used a topographic analysis of covariance model did not show that localized

169 trol score between treatment groups using an analysis of covariance model that adjusted for baseline

170 ores were calculated using repeated measures analysis of covariance model that adjusted for treatment

171 A baseline score-adjusted analysis of covariance model using effect-coded interven

172 )-18 fragments at week 4 were assessed by an analysis of covariance model with adjustment for baselin

173 sent cigarette smoking) were entered into an analysis of covariance model, followed by depression sta

174 The effect of age was significant in the analysis of covariance model.

175 a multivariable Cox regression model and an analysis of covariance model.

176 test was used for within-group analysis; an analysis-of-covariance model (with age as a covariate) w

177 Time to detection in an analysis-of-covariance model was associated with lung ca

178 Multivariate analysis of covariance models (ANCOVA) was constructed i

179 ention-to-treat analysis was performed using analysis of covariance models and conducted from Septemb

180 For each ATN group, analysis of covariance models identified differences in

181 Analysis of covariance models was used to compare data f

182 Analysis of covariance models were used to assess HRQoL

183 Longitudinal mixed models and analysis of covariance models were used to assess the ef

184 Analysis of covariance models were used to compare regio

185 In intention-to-treat analysis of covariance models, with adjustment for basel

186 ces in tensor metrics were examined by using analysis of covariance models.

187 d diabetes risk using multivariable-adjusted analysis of covariance models.

188 Analysis-of-covariance models were used to estimate the

189 Analysis of covariance, multivariate logistic regression

190 r, after adjusting for baseline scores using analysis of covariance, no significant between-group dif

191 We employed a three-way analysis of covariance on FC to conduct statistical anal

192 Analysis of covariance on the subscale scores revealed t

193 rences between CP and SNG were compared with analysis of covariance or logistic regressions with mult

194 n observed sample means were evaluated using analysis of covariance or t test; categorical data was a

195 Data were evaluated with analysis of covariance, ordinal logistic regression anal

196 ters by absolute and Z-scores, respectively (analysis of covariance P < .05).

197 er with letrozole (87%) than tamoxifen (75%; analysis of covariance P = 0.0009).

198 ne-adjusted mean 25+/-5 versus 31+/-6 mm Hg; analysis of covariance P=0.022).

199 The statistical evaluation consisted of analysis of covariance (P <0.05).

200 bo by modified intention to treat (n = 340) (analysis of covariance, P = .022; mixed model for repeat

201 atients (analysis of variance, P = .002, and analysis of covariance, P = .03, respectively); the caud

202 globus pallidus were larger in the patients (analysis of covariance, P = .05, P = .007, and P < .001,

203 tricles tended to be larger in the patients (analysis of covariance, P = .06).

204 o the decrease in rabbits without minipumps (analysis of covariance, P = 0.0092).

205 decrease of 4.6 +/- 2.6 mm(3) (p < 0.001 by analysis of covariance; p < 0.05 for comparison of all p

206 on TOVA visual reaction times (multivariate analysis of covariance; P = .006); KABC-2 sequential pro

207 to 50.3) seconds for conventional settings (analysis of covariance; P=0.044 between groups) despite

208 From GW association and analysis of covariance performed on a total sample of 42

209 Repeated measures of analysis of covariance revealed a group effect across gh

210 Analysis of covariance revealed dose-dependent effects o

211 cortisol with baseline symptom severity, and analysis of covariance revealed higher baseline cortisol

212 Analysis of covariance revealed no significant differenc

213 Stepwise general linear modeling with analysis of covariance revealed that only creatinine lev

214 General linear modeling with analysis of covariance revealed that serum cystatin C wa

215 A nested mixed model analysis of covariance revealed that the intervention wa

216 Analysis of covariance showed a main effect of diagnosti

217 Analysis of covariance showed nonsignificant effects for

218 ention group, age, sex, and body mass index, analysis of covariance showed that baseline plasma lipid

219 Analysis of covariance showed that compared with the TAU

220 Analysis of covariance showed that LHRH-agonist treatmen

221 An analysis of covariance showed that lower BIS scorers exh

222 Analysis of covariance showed that mean IOP reduction wi

223 Analysis of covariance showed that the behavioral effect

224 Analysis of covariance shows that NPP is significant gre

225 included Student t test, Fisher exact test, analysis of covariance, Spearman correlation, and logist

226 ata were analyzed with analysis of variance, analysis of covariance, stepwise multiple regression, an

227 However, analysis of covariance suggested that the magnitude of t

228 Analysis of covariance, taking the baseline IOP as a cov

229 e were identified on a voxelwise basis by an analysis of covariance that controlled for between-group

230 sing a mixed-effects repeated measures model analysis of covariance that included data from all avail

231 ter adjustment for base-line differences (by analysis of covariance), there was no significant differ

232 oupled to HF were identified using voxelwise analysis of covariance throughout the entire brain and a

233 Data were analyzed using a 2-way mixed analysis of covariance (time and treatment) on complete

234 The first design uses analysis of covariance to assess treatment effects in su

235 The authors used multivariable analysis of covariance to compare high-density lipoprote

236 We performed analysis of covariance to evaluate if 1 additional year

237 We used analysis of covariance to examine associations of variou

238 Student t test, test for linear regression, analysis of covariance, two-way factorial analysis of va

239 Analysis of covariance was applied to compare change in

240 Repeated-measures analysis of covariance was carried out to determine (1)

241 Analysis of covariance was conducted on spatial weights

242 es using mixed model, one-way between-groups analysis of covariance was conducted to compare the ONH

243 An analysis of covariance was conducted to evaluate the imp

244 An analysis of covariance was employed to examine the effec

245 Analysis of covariance was performed on the 6-month chan

246 A mixed model analysis of covariance was performed to compare regional

247 Analysis of covariance was performed to determine the re

248 An analysis of covariance was performed to explore the diff

249 ponse to low-dose dexamethasone, and two-way analysis of covariance was performed using maternal and

250 Multivariate analysis of covariance was performed: birth weight, birt

251 Analysis of covariance was used for between-group compar

252 Analysis of covariance was used for modeling the associa

253 Analysis of covariance was used to adjust for baseline d

254 Logistic regression and analysis of covariance was used to assess associations w

255 Analysis of covariance was used to assess between-group

256 Analysis of covariance was used to assess differences in

257 nces were evaluated using paired t-tests and analysis of covariance was used to assess subjective dep

258 Analysis of covariance was used to assess the diet x gen

259 Analysis of covariance was used to assess treatment effe

260 Analysis of covariance was used to compare cognitive fun

261 Analysis of covariance was used to compare mean CES-D Sc

262 Analysis of covariance was used to compare the change fr

263 A repeated measures analysis of covariance was used to compare the SCV and I

264 Analysis of covariance was used to control for gender ef

265 Analysis of covariance was used to control for intracran

266 Analysis of covariance was used to control for potential

267 Analysis of covariance was used to determine differences

268 Analysis of covariance was used to determine regional ef

269 Analysis of covariance was used to determine the associa

270 Multivariate analysis of covariance was used to determine whether dif

271 Analysis of covariance was used to examine changes in he

272 Analysis of covariance was used to examine the relations

273 Analysis of covariance was used to identify parameters t

274 Analysis of covariance was used to model endpoint HQL sc

275 Analysis of covariance was used to model GFR measured by

276 Analysis of covariance was used to test for between-grou

277 Analysis of covariance was used to test for covariate-ad

278 A three-way analysis of covariance was used to test for the main eff

279 Analysis of covariance was utilized to compare the diffe

280 By performing multivariate analysis of covariance, we identified significant associ

281 Multiple logistic regression and analysis of covariance were performed on data for baseli

282 and analysis of variance, and multivariable analysis of covariance were performed on the annualized

283 Descriptive statistics and 2-factorial analysis of covariance were used to assess the effects o

284 stic regression (binary and multinomial) and analysis of covariance were used to examine the relation

285 Univariate analyses and analysis of covariance were used to investigate recipien

286 Subsequent analysis of covariance, which controlled for correlation

287 We used analysis of covariance while controlling for baseline va

288 However, repeated-measures multivariate analysis of covariance with age (in months) and tobacco

289 s were determined by using two-way factorial analysis of covariance with age adjustment.

290 The groups were compared by analysis of covariance with age, sex, race, and room tem

291 between mean final areas 10%, p = 0.006) in analysis of covariance with initial mattress dust weight

292 compared among the IQ-based subgroups using analysis of covariance with intracranial volume and age

293 ear (analysis of covariance) and log-linear (analysis of covariance with log-transformed data) regres

294 xa, dietary, and habitat groups (detected by analysis of covariance with P < or = 0.05) include the f

295 Analysis of covariance with planned contrasts showed no

296 were compared using a 4-way repeated measure analysis of covariance (with group and gender as between

297 and their interaction were analyzed by using analysis of covariance, with adjustment for age and educ

298 Intention-to-treat analysis of covariance, with adjustment for baseline cog

299 A one-way analysis of covariance, with group as fixed factor (whol

300 Analysis of covariance, with weight as the covariate, in