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

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

6 .96 hour; 95% CI, 0.56-1.37 hours; P < .001, analysis of covariance).

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

33 DESIGN Case-control analysis of covariance.

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

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

36 Statistical analysis was performed by using analysis of covariance.

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

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

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

40 Analysis of covariance adjusted for plasma folate, vitam

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

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

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

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

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

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

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

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

49 Regression and analysis of covariance analyses investigated relationshi

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

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

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

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

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

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

56 .00004) between AD and control brains, using analysis of covariance (ANCOVA) with age as covariate.

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

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

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

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

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

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

63 Analysis of covariance and exact Mann-Whitney tests were

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

65 Analysis of covariance and logistic regression models te

66 Analysis of covariance and logistic regression were appl

67 By analysis of covariance and multiple regression analysis,

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

92 An analysis of covariance controlling for BMI showed that a

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

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

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

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

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

98 Using analysis of covariance, early physical therapy showed im

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

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

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

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

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

104 Analysis of covariance for mixed models was used with th

105 Analysis of covariance for total cerebral volume demonst

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

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

108 Analysis of covariance found that the lower severity of

109 Analysis of covariance found the multivariate model that

110 Analysis of covariance, including potentially confoundin

111 Analysis of covariance indicated a significant associati

112 Analysis of covariance indicated that ambulatory status

113 Analysis of covariance indicated that density and the li

114 Analysis of covariance indicated that dissociation had a

115 An analysis of covariance indicated that in the entire coho

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

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

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

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

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

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

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

123 Analysis of covariance methods were used to obtain mean

124 Analysis of covariance methods were used to obtain mean

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

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

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

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

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

130 )-18 fragments at week 4 were assessed by an analysis of covariance model with adjustment for baselin

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

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

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

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

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

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

137 Analysis of covariance models was used to compare data f

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

139 Analysis of covariance models were used to compare regio

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

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

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

143 Analysis of covariance, multivariate logistic regression

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

145 Analysis of covariance on the subscale scores revealed t

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

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

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

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

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

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

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

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

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

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

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

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

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

159 Analysis of covariance revealed dose-dependent effects o

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

161 Analysis of covariance revealed no significant differenc

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

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

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

165 Analysis of covariance showed a main effect of diagnosti

166 Analysis of covariance showed nonsignificant effects for

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

168 Analysis of covariance showed that compared with the TAU

169 Analysis of covariance showed that LHRH-agonist treatmen

170 An analysis of covariance showed that lower BIS scorers exh

171 Analysis of covariance showed that mean IOP reduction wi

172 Analysis of covariance showed that the behavioral effect

173 Analysis of covariance shows that NPP is significant gre

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

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

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

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

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

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

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

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

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

183 We used analysis of covariance to examine associations of variou

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

185 Analysis of covariance was applied to compare change in

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

187 Analysis of covariance was conducted on spatial weights

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

189 An analysis of covariance was conducted to evaluate the imp

190 An analysis of covariance was employed to examine the effec

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

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

193 Analysis of covariance was performed to determine the re

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

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

196 Analysis of covariance was used for between-group compar

197 Analysis of covariance was used to adjust for baseline d

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

199 Analysis of covariance was used to assess differences in

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

201 Analysis of covariance was used to assess treatment effe

202 Analysis of covariance was used to compare cognitive fun

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

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

205 Analysis of covariance was used to control for gender ef

206 Analysis of covariance was used to control for intracran

207 Analysis of covariance was used to control for potential

208 Analysis of covariance was used to determine differences

209 Analysis of covariance was used to determine regional ef

210 Multivariate analysis of covariance was used to determine whether dif

211 Analysis of covariance was used to examine changes in he

212 Analysis of covariance was used to examine the relations

213 Analysis of covariance was used to identify parameters t

214 Analysis of covariance was used to model endpoint HQL sc

215 Analysis of covariance was used to model GFR measured by

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

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

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

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

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

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

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

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

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

225 Subsequent analysis of covariance, which controlled for correlation

226 We used analysis of covariance while controlling for baseline va

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

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

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

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

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

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

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

234 Analysis of covariance with planned contrasts showed no

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

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

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

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

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