ライフサイエンスコーパス: measurement error

1 JSW of > or =0.50 mm (i.e., greater than the measurement error).

2 accommodate the "noise" component of dietary measurement error.

3 sal effects under differential and dependent measurement error.

4 viduals with complete data and allowance for measurement error.

5 n models, multiple measures of concepts, and measurement error.

6 thods for validation, energy adjustment, and measurement error.

7 non-Gaussian specification for dealing with measurement error.

8 xaminations were reanalyzed to establish the measurement error.

9 trics include classical-type nondifferential measurement error.

10 igher average agreement, and lower estimated measurement error.

11 structures is within the limits of expected measurement error.

12 are unlikely to be explained solely by qPCR measurement error.

13 ontributes to within-subject variability and measurement error.

14 eports and provide methods of correcting for measurement error.

15 SD was the best predictor of an automated measurement error.

16 t include a meaningful incorporation of mass measurement error.

17 from other experiments as well as additional measurement error.

18 reconciles all the experimental data within measurement error.

19 dule attenuation, diameter, and location) on measurement error.

20 environment) and transient effects, such as measurement error.

21 consensus from alignment requires a model of measurement error.

22 es, such as confounding, selection bias, and measurement error.

23 Self-reported diet is prone to measurement error.

24 ) methods used in these studies are prone to measurement error.

25 s routinely suffer from bias due to exposure measurement error.

26 d reliability studies to assess differential measurement error.

27 s from nutritional epidemiologic studies for measurement error.

28 iance in the measured exposure due to random measurement error.

29 ge studies of BMI are robust with respect to measurement error.

30 s from nutritional epidemiologic studies for measurement error.

31 ting methods may not effectively correct for measurement error.

32 ptible to confounding, reverse causation and measurement error.

33 ity, and accelerometers are still subject to measurement error.

34 while accounting for dropout as well as for measurement error.

35 as from residual confounding or differential measurement error.

36 ssessment of diet using any method will have measurement error.

37 data-driven model that describes sources of measurement error.

38 was used to correct the effect estimates for measurement error.

39 ence device within the thresholds set by the measurement error.

40 responding analyses that did not correct for measurement error.

41 individual variability and susceptibility to measurement error.

42 to a change in retinal thickness rather than measurement error.

43 om cases and controls and that accounted for measurement error.

44 e modeling and careful treatment of exposure measurement error.

45 ing from the measuring instrument and random measurement errors.

46 the analysis, which may induce artifacts and measurement errors.

47 ting an even greater number of unpredictable measurement errors.

48 common inverse method on samples with large measurement errors.

49 ts, it is necessary to limit systematic mass measurement errors.

50 ferences and changes, and raise issues about measurement errors.

51 es the beadchip interrogates have very large measurement errors.

52 e it allows for understanding and correcting measurement errors.

53 re of these limitations to avoid substantial measurement errors.

54 osynthesis and axial CT were +/-2.1 mm (mean measurement error, 0 mm).

55 ocrystals (PLQY approximately 70%) to within measurement error (2-3%) of unity, while simultaneously

56 e a statistical model for description of the measurement error, 2) to establish the descriptive power

57 ng, with nonshared environmental factors and measurement error accounting for the other half.

58 Measurement error accounts for another 1% of the mutant-

59 osed, and employed to determine the inherent measurement error across multiple arrays used in this st

60 egression calibration can be used to provide measurement error-adjusted estimates of relationships be

61 (CI, -3.63 to 5.92 mL/min per 1.73 m2) after measurement-error adjustment for protein intake.

62 After measurement-error adjustment, the change in estimated GF

63 gression calibrations of dietary reports and measurement error adjustments.

64 lete washing of the resin-gel caused a 5-15% measurement error and a decrease in precision, even at i

65 The separate and combined influences of measurement error and biological variability on the disc

66 estimates from models can result in exposure measurement error and can potentially affect the validit

67 general and powerful approach to account for measurement error and causal pathways when analyzing dat

68 cuss this phenomenon within the framework of measurement error and identify sources of variation that

69 for creating rating scales which can reduce measurement error and increase the quality of resulting

70 Investigators should consider how measurement error and LODs may bias findings when examin

71 e optimized 2D view substantially reduced 2D measurement error and may be valuable when used in conju

72 Issues arising from measurement error and missing data are addressed.

73 nk claimed to identify 2 categories of error-measurement error and recall biases-in the methodology u

74 Methodological issues, such as measurement error and regression to the mean, have made

75 ation better compensates for systematic mass measurement errors and also significantly reduces the ma

76 gnition algorithm, MRF inherently suppresses measurement errors and can thus improve measurement accu

77 ietary assessment instruments are subject to measurement errors and correcting for them under the ass

78 any noticeable biases from the overall mass measurement errors and decreases the overall standard de

79 often involves using variables that contain measurement errors and formulating multiequations to cap

80 ces within certain thresholds defined by the measurement errors and the influence of these difference

81 increase in one or more dimensions above the measurement error, and at least 5% volume by using the A

82 ate has incorporated adjustment for exposure measurement error, and few have examined specific histol

83 expressed genes, the effects of experimental measurement error, and missing data.

84 for each SUV metric its mean value, relative measurement error, and repeatability (MEr-R).

85 ted diet assessment, with the possibility of measurement error, and the potential for residual or unm

86 rom health records which may be sensitive to measurement errors, and the observed associations may no

87 outcomes and preferences, once corrected for measurement error, appear to be about as heritable as ma

88 unoassay data that treats the propagation of measurement error appropriately.

89 Few results on differential and dependent measurement error are available in the literature.

90 While confounding and measurement error are common in observational studies, t

91 ion, systematic bias is removed and the mass measurement errors are centered at 0 ppm.

92 In some published datasets, we find that measurement errors are highly correlated between probes

93 earchers sometimes argue that their exposure-measurement errors are independent of other errors and a

94 effects in epidemiological studies, exposure measurement errors are likely to be caused because of th

95 ns when the number of subpopulations and the measurement errors are moderate.

96 controls, our results suggest that when the measurement errors are small (0.005), approximately 3% o

97 vidual animals are usually treated as random measurement error around the true response.

98 enomewide association studies, even when the measurement errors associated with DNA pooling are nonne

99 disease-associated markers, we find that the measurement errors associated with DNA pooling have litt

100 uld employ multiple measurements to minimize measurement errors associated with site-specific measure

101 attempts to measure ever-shorter distances, measurement errors become important to understand.

102 canning profile, to assess the dependence of measurement error between neighboring probes.

103 Global and sectoral RNFL thickness measurement errors between the two devices were also com

104 To control measurement error bias caused by variations in serum lip

105 Patients exceeding the measurement error by +/-2 SDs were identified with signi

106 In addition, they highlight variation in measurement error by pollutant and support the implement

107 including confounding, reverse causation and measurement error can afflict conventional mediation app

108 Measurement error can have an important impact on the es

109 Differential exposure measurement error can have more adverse effects on estim

110 y variables in a model for a health outcome, measurement error can lead to bias of the regression coe

111 roscedasticity, the neglect of components of measurement error can produce significant bias.

112 d mean-squared error are given under general measurement error conditions, which reinforce the very d

113 Our results show that the quadratic measurement error correction (QMEC) method performs bett

114 Measurement error correction methods offer a way to over

115 this variance can be obtained, present four measurement error correction methods that are applicable

116 ) and deattenuation factor (lambda), used in measurement error correction.

117 pollutant and support the implementation of measurement error corrections when possible.

118 h increasing levels of positively correlated measurement error created increasing downward or upward

119 er begins with an illustration of how random measurement error decreases the power of statistical tes

120 s the overall standard deviation of the mass measurement error distribution by 1.2-2-fold, depending

121 Differential measurement error due to differential patterns of spatio

122 g Forster resonance energy transfer distance measurement error due to unknown angles in the dipole or

123 uding nonshared environmental influences and measurement error) explain the remainder of the variance

124 , as expected from theoretical expectations, measurement errors follow a Lorentzian-like distribution

125 y significant difference between the average measurement error for contrast attenuations between 300

126 reference standard to obtain a mean absolute measurement error for each reader for each series.

127 escribed as a means of correcting effects of measurement error for normally distributed dietary varia

128 We develop models of measurement error for shotgun sequencing by combining th

129 Bland-Altman analysis showed similar measurement errors for single-BH SSIR and non-BH SSIR wh

130 Lack of statistical power and measurement errors for the environmental factors continu

131 All measurement errors found were <1 mm.

132 Moreover, previous studies were limited by measurement error from dietary self-reports.We derived b

133 line nutrient exposures (28%) and effects of measurement errors from nutrition exposures (24%).

134 ciples and methods for misclassification and measurement error guide the analysis.

135 om artifacts due to residual confounding and measurement errors; however, polymorphisms reliably asso

136 o classical analytic methods can account for measurement error (ie, sensitivity and specificity) for

137 sults show that the inclusion of probe-level measurement error improves accuracy in detecting differe

138 diture and body weight can be used to reduce measurement error, improving the ability of the food fre

139 tion is given for the effect of differential measurement error in a continuous exposure measure on th

140 ce's study design, we incorporated simulated measurement error in a reanalysis of the Public Health S

141 ility in experimentally derived data include measurement error in addition to the physical phenomena

142 When corrected for measurement error in alcohol consumption, dietary variab

143 Measurement error in both the exposure and the outcome i

144 These studies show that measurement error in combination with biological variabi

145 and to apply a practical tool to adjust for measurement error in complex sample data using a regress

146 ats include confounding, selection bias, and measurement error in either the exposure or the outcome.

147 direction of the bias due to nondifferential measurement error in estimating the natural direct and i

148 with hepatitis C virus, while accounting for measurement error in gamma-glutamyltransferase, using da

149 Although substantial measurement error in important confounders is known to u

150 The cost of measurement error in multivariate analyses is loss of st

151 nd urinary sodium or potassium may be due to measurement error in one or both estimates.

152 ective phenotyping designs and the impact of measurement error in phenotyping.

153 that false discovery is closely tied to mass measurement error in PMF analysis.

154 Accounting for measurement error in reported exposure using external va

155 Measurement error in self-reported data from questionnai

156 They showed that correction of measurement error in self-reported physical activity lev

157 Adjustment for measurement error in smoking behavior allowing up to 75%

158 reality, the trophic levels may vary due to measurement error in stable isotopes of nitrogen (delta(

159 ng and quantifying the sources of volumetric measurement error in the assessment of lung nodules with

160 s with the longitudinal study design and the measurement error in the diagnostic methods under study.

161 Limitations include potential measurement error in the fatty acids and other model cov

162 The new model suggests that, for these data, measurement error in the FFQ could lead to a 51% greater

163 tion of a trend to settings which also allow measurement error in the outcome and to cases involving

164 We investigated measurement error in the self-reported diets of US Hispa

165 ures of SWB, and 12-18% after correction for measurement error in the SWB measures.

166 n-extrapolation (SIMEX) procedure to address measurement error in time-varying covariates, and we com

167 Measurement errors in body mass and base metabolic rate,

168 Second, measurement errors in both protein-DNA binding data and

169 y in longitudinal studies to reduce exposure-measurement errors in EWAS.

170 Measurement errors in the dietary assessment of fruit an

171 Measurement errors in the exposure and the outcome are s

172 s of variance were performed to evaluate the measurement errors in the phantom study and the intersca

173 es are sensitive to the chosen sample and to measurement errors in the phenotype.

174 The simulations demonstrate that measurement errors in time-dependent covariates may indu

175 mized protocols can significantly reduce the measurement errors in wall activity estimates, but PET s

176 Correction for measurement error increased the magnitude of these estim

177 error in smoking behavior allowing up to 75% measurement error increased the proportions mediated to

178 The authors apply these rules to 4 forms of measurement error: independent nondifferential, dependen

179 Naive analyses that did not account for measurement error indicated statistically significant as

180 IOPcc may account for measurement error induced by corneal biomechanics.

181 ose a Bayesian method to include probe-level measurement error into the detection of differentially e

182 PsiM including all biological and systematic measurement errors introduced by the calibration paramet

183 This measurement error is a combination of systematic compone

184 Early studies indicated that the impact of measurement error is benign, leading generally only to a

185 tion of their precision is demonstrated when measurement error is disregarded.

186 sensitivity analyses in which adjustment for measurement error is explored.

187 n was estimated from self-report; thus, some measurement error is inevitable.

188 a potential problem with this statistic: if measurement error is large relative to the differences i

189 Memory Scale-Revised, suggesting that retest measurement error is not dramatically increased in the R

190 Measurement error is said to be nondifferential if measu

191 Measurement error is taken into account during the proce

192 reported previously (0.68 kcal/mol), but the measurement error is very close to the magnitude of the

193 Still, the imprecision caused by unavoidable measurement errors is a dominant factor for absolute qua

194 r approach does not eliminate the effects of measurement errors, it leads to more consistent results.

195 alorimetric glucose detection demonstrates a measurement error less than 2%.

196 onality, process noise, hidden variables and measurement error, make it possible to test more precise

197 ated for their association with an automated measurement error (manual measurement needed and exceede

198 tural inputs and clinical quality over time; measurement error may attenuate the estimated associatio

199 Measurement error may have attenuated any modest associa

200 ntrast to the one-stage pooling scheme where measurement errors may have large effect on statistical

201 te when the gold standard is also subject to measurement error (ME).

202 n epidemiologic research: validated exposure measurement error, measured selection bias, and measured

203 Dietary measurement error might explain the absence of a signifi

204 We investigated the effect of measurement error (misclassification) in sensitivity ana

205 r regression (method of least squares) and a measurement error model approach for more-accurate estim

206 We propose a measurement error model for a physical activity question

207 errors that arise from assuming a classical measurement error model for doubly labeled water and a B

208 We present a measurement error model that accommodates the mixture of

209 The authors present a measurement error model to estimate the validity (define

210 A flexible measurement error model was postulated.

211 ation factor (lambda) were estimated using a measurement error model with repeat 24-hour dietary reca

212 In this article, we consider a measurement error model-based method for bead-based micr

213 The performance of the proposed measurement error model-based method is evaluated via a

214 e TSC approach does not rely on any specific measurement error model.

215 Repeat-biomarker measurement error models accounting for systematic corre

216 Use of repeat-biomarker measurement error models resulted in a rho of 0.42.

217 se a new statistical procedure that utilizes measurement error models to estimate missing exposure da

218 hin-individual day-to-day variation by using measurement error models.

219 ted and experimental datasets with different measurement error models.

220 nd 4-5 y) from the NHANES 2003-2010 by using measurement error models.

221 Marginal structural measurement-error models can simultaneously account for

222 ted to provide a root-mean-square (rms) mass measurement error of <100 ppb on petroleum-based mixture

223 red NP in the complex matrix with a relative measurement error of 5.1% (as relative standard deviatio

224 Measurement error of acquired fluence at fluorescent emi

225 Because the relative feasibility and measurement error of dietary methods varies, this study

226 tematic investigations into the structure of measurement error of physical activity questionnaires ar

227 The method is relatively insensitive to mass measurement error of up to 20 ppm.

228 Diet interventions can impact measurement errors of dietary self-report.

229 can be modeled as piecewise constant and the measurement errors of different probes are independent.

230 Measurement errors of exposure and outcome can be classi

231 and metal-organic framework catalysts, with measurement errors of less than four per cent of the abs

232 Because random measurement errors of the two techniques differ, mathema

233 ated cells, with good reproducibility in the measurements (errors of less than 5%).

234 The impact of correcting for measurement error on health effect inference is concorda

235 The impact of measurement error on interpretation of clinical trial re

236 f bias attributable to classical and Berkson measurement error on odds ratios, assuming that the logi

237 Weinberg et al.'s result for nondifferential measurement error on preserving the direction of a trend

238 Whereas the bearing of mass measurement error on protein identification is sometimes

239 We further examined the influence of outcome measurement error on statistical power.

240 t Study on Diet and Cancer and the impact of measurement error on these associations.

241 We also simulate the impact of unavoidable measurement errors on apparent rates of intestinal gluco

242 in a sensitivity analysis, the impact of the measurement errors on the computed acoustic properties i

243 lanations such as effects of ASD severity or measurement error or low score variability in ASD subjec

244 Results could be affected by measurement error or residual confounding.

245 However, due to measurement errors or lack of data this knowledge is oft

246 ent when compared with the estimated between-measurement error (P=0.0055).

247 as observer perceptual error, while observer measurement error played a smaller role.

248 This probe-level measurement error provides useful information which can

249 In these measurements, error ranges of +/-0.03 ppm for the indire

250 bines propensity score matching methods with measurement error regression models.

251 l relative to the cost of intensive care; d) measurement errors require ongoing programmatic educatio

252 hickness or doing so with correction for its measurement error resulted in statistically significant

253 wing calibration, bias due to classical-type measurement error, resulting in as much as 50% attenuati

254 ciations without correction of self-reported measurement error should be viewed with caution.

255 o different patients, eyes, and sessions and measurement error specific to each disease group.

256 ome estimates than the consequences of other measurement errors such as underreporting of intake.

257 sample-to-sample variability or experimental measurement error, suggested that NOD2 AI is likely to r

258 y confounding directional asymmetry (DA) and measurement error terms.

259 wer average agreement, and greater estimated measurement error than other topics.

260 posures with improved precision and far less measurement error than with standard epidemiologic metho

261 ibration method reduces effects of classical measurement error that are typical of epidemiologic stud

262 iscards potentially useful information about measurement error that can be obtained from an appropria

263 Limitations of this study include measurement error that could lead to residual confoundin

264 rmination of cell boundaries, and introduces measurement error that propagates throughout subsequent

265 ments and screening steps are used to reduce measurement errors that are a consequence of detecting l

266 niques for both of these studies resulted in measurement errors that are too large to allow us to for

267 om the author's center, the types of corneal measurement errors that can occur in IOL calculation are

268 requency questionnaires are known to produce measurement error, the amount of error and effectiveness

269 It is shown that, in the presence of device measurement error, the classical and inverse calibration

270 After adjustment for measurement error, the HRs increased and the 95% CIs wid

271 was previously measured reveals that, within measurement error, the same number of vesicles are docke

272 ng by DeltaG(HOH) increases as the square of measurement error, there is a premium on precision.

273 s of those conventions introduce error; some measurement error thought to have been attributable to s

274 d health outcomes, efforts to reduce dietary measurement error through improved collection, evaluatio

275 arying residual confounding and differential measurement error through model-derived discrete random

276 c biases (e.g., confounding, selection bias, measurement error) to cover distortions of conclusions p

277 , the combined impact of correlated exposure measurement error, unmeasured confounding, interaction,

278 Differences in measurement error using LoBs versus gold colloid are als

279 We corrected for measurement error using recently developed methods that

280 e purpose of the study was to assess dietary measurement error using two self-reported dietary instru

281 atic error accounted for over 22% and 50% of measurement error variance for the 24-hour recalls and F

282 incorporating (i) experimentally determined measurement error variance, (ii) recursively updated est

283 blem but require an a priori estimate of the measurement error variance.

284 In the phantom, mass measurement error varied with threshold and calcium dens

285 d this correlation minimally (R2, 0.09), and measurement error was estimated to attenuate these assoc

286 hown to be small while most of the effect of measurement error was on the variance.

287 An estimate of within-individual (measurement) error was obtained by repeat measures made

288 After correcting for measurement errors, we found that associations between o

289 Available data used to correct for measurement error were primarily restricted to dietary v

290 n calibrated using biomarkers to correct for measurement error were simultaneously associated with th

291 uments is recommended as a way to adjust for measurement error when estimating diet-disease associati

292 his paper, the authors examine the effect of measurement error when the exposure variable of interest

293 veloped to characterize the effect of random measurement error when there is a lower threshold for re

294 ion, BPIT was shown to be robust against PET measurement errors when compared with a widely accepted

295 al mass standard we demonstrate sub-ppm mass measurement error which provides an unambiguous base com

296 ength, including radiography, are subject to measurement error, which could result in misclassificati

297 survey construction, the goal is to minimize measurement error with systematic planning and execution

298 simulations evaluating bias from correlated measurement error with varying reliability coefficients

299 Bias due to complex patterns of measurement error within diet scores cannot be excluded.

300 re-disease associations than nondifferential measurement error, yet relatively little has been writte