ライフサイエンスコーパス: measurement error

1 ic samples, which are unavoidably subject to measurement error.

2 ting methods may not effectively correct for measurement error.

3 disease resurgence but could also be due to measurement error.

4 ptible to confounding, reverse causation and measurement error.

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

6 as from residual confounding or differential measurement error.

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

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

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

10 responding analyses that did not correct for measurement error.

11 individual variability and susceptibility to measurement error.

12 s using comparative methods that account for measurement error.

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

14 e modeling and careful treatment of exposure measurement error.

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

16 sal effects under differential and dependent measurement error.

17 viduals with complete data and allowance for measurement error.

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

19 non-Gaussian specification for dealing with measurement error.

20 trics include classical-type nondifferential measurement error.

21 igher average agreement, and lower estimated measurement error.

22 structures is within the limits of expected measurement error.

23 nces in temperature and pCO(2), and reducing measurement error.

24 ontributes to within-subject variability and measurement error.

25 eports and provide methods of correcting for measurement error.

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

27 at volume is very modest and could be due to measurement error.

28 t include a meaningful incorporation of mass measurement error.

29 from other experiments as well as additional measurement error.

30 reconciles all the experimental data within measurement error.

31 butable to resident operative competency and measurement error.

32 ome measurement for this to be solely due to measurement error.

33 ided by the maximum strength of differential measurement error.

34 utility of imperfect data by accounting for measurement error.

35 sufficiently well-defined interventions, and measurement error.

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

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

38 ts generating high quality data and reducing measurement error.

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

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

41 xaminations were reanalyzed to establish the measurement error.

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

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

44 es the beadchip interrogates have very large measurement errors.

45 e it allows for understanding and correcting measurement errors.

46 re of these limitations to avoid substantial measurement errors.

47 the analysis, which may induce artifacts and 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 over a person's life span and is subject to measurement errors.

52 and we found none that discussed correlated measurement errors.

53 mize the sensitivity of g (m18) estimates to measurement errors.

54 ing from the measuring instrument and random measurement errors.

55 ting an even greater number of unpredictable measurement errors.

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

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

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

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

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

61 gression calibrations of dietary reports and measurement error adjustments.

62 Union Army cohort as between cohorts, makes measurement error an unlikely explanation.

63 ut self-report of dietary intake is prone to measurement error and bias.

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

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

66 droplet distributions were used to estimate measurement error and dynamic range and to examine the e

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

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

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

70 s of RIME over a naive approach that ignores measurement error and MIME using a hypothetical example

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

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

73 rameter-free, frame-invariant, and robust to measurement errors and can be computed from unfiltered c

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

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

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

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

78 tool to reduce the prevalence of volumetric measurement errors and lost axial resolution.

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

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

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

82 Unmeasured confounding, measurement error, and low fracture rate.

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 isks, pitfalls of "modifiable risk factors", measurement error, and risk for bias.

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

87 and addressed bias due to reverse causality, measurement error, and time-invariant confounding.

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

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

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

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

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

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

94 However, when the measurement errors are correlated with each other, addit

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

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

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

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

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

100 Measurement error associated with self-reported dietary

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

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

103 may be due to ( a) insufficient attention to measurement error, ( b) subtle but age-sensitive differe

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

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

106 perimetry tests made 6 months apart reduced measurement error (between-test measurement variability)

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

108 aken over repeated administrations, reducing measurement error bias in assessment of diet-disease ass

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

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

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

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

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

114 eplication, triangulation, quantification of measurement error, contextualization of each effect in t

115 t formally considered the impact of exposure measurement error contributed by the limited spatiotempo

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

117 -Intake"), which was developed to facilitate measurement error correction in self-reported mean daily

118 Measurement error correction methods offer a way to over

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

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

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

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

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

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

125 Differential measurement error due to differential patterns of spatio

126 Study limitations include measurement error due to maternal self-report of smoking

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

128 The DI is subject to substantial measurement errors due to cell offset from the flow cent

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

130 effect and proposed methods for handling the measurement error, fewer have investigated the case wher

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

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

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

134 All measurement errors found were <1 mm.

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

136 erit insensitivity to system preparation and measurement error from the two-qubit tomography scheme.

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

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

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

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

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

142 rage external validation data to account for measurement error in a wide range of scenarios.

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

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

145 Measurement error in both the exposure and the outcome i

146 that link could be an artifactual result of measurement error in child birthdates.

147 Measurement error in child MOB helps to explain the asso

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

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

150 ants and nonmigrants, low response rate, and measurement error in estimating diet and activity from q

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

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

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

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

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

156 at least 4 of 20 sources of bias, including measurement error in predictors (n = 12) and/or outcome

157 Accounting for measurement error in reported exposure using external va

158 Measurement error in self-reported data from questionnai

159 mitations of the study may include potential measurement error in self-reported dietary intake, inabi

160 proved to be a useful instrument to correct measurement error in self-reported food intake data.

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

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

163 Understanding measurement error in sodium and potassium intake is esse

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

165 We accounted for measurement error in substance use using Bayesian models

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

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

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

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

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

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

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

173 Measurement errors in body mass and base metabolic rate,

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

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

176 Measurement errors in the dietary assessment of fruit an

177 Measurement errors in the exposure and the outcome are s

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

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

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

181 lained by variability due to sampling and/or measurement error, in a group of studies often underpowe

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

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

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

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

186 were determined for each protocol and their measurement errors (intra subject repeatability) calcula

187 Measurement errors introduced by different components li

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

189 This measurement error is a combination of systematic compone

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

191 Measurement error is common in epidemiology, but few stu

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

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

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

195 This maximum strength of differential measurement error is itself assessed as the risk ratio o

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

197 Measurement error is said to be nondifferential if measu

198 Measurement error is taken into account during the proce

199 The authors demonstrate that phenotypic measurement error is unlikely to account for these null

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

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

202 ging data can be generated rapidly with mass measurement errors <5 ppm and ~40 000 resolving power.

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

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

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

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

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

208 ources of bias, like multiple imputation for measurement error (MIME), rely on internal validation da

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

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

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

212 The measurement error model simultaneously compared all 3 me

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

214 We develop a Bayesian measurement error model to estimate proportions of eosin

215 A flexible measurement error model was postulated.

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

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

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

219 Repeat-biomarker measurement error models accounting for systematic corre

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

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

222 Measurement error models were used to estimate usual sod

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

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

225 ted and experimental datasets with different measurement error models.

226 Marginal structural measurement-error models can simultaneously account for

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

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

229 Accuracy was defined as the measurement error of BMD (DeltaBMD), and precision was d

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

231 analysis results are given for differential measurement error of either the exposure or outcome.

232 by connecting the cells in series (CiS), the measurement error of electrochemical data caused by stab

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

234 However, it is essential to know the measurement error of such methods, if selection for back

235 Further, the average measurement error of the distance from the implant shoul

236 In the case of differential measurement error of the exposure, under certain assumpt

237 In the case of differential measurement error of the outcome, it is shown that the t

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

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

240 Measurement errors of exposure and outcome can be classi

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

242 le approach to suppress background noise and measurement errors of single photon imager operation in

243 tatic, free bending radiographic images gave measurement errors of up to 4 mm, which was approximatel

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

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

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

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

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

249 We also investigated the impact of measurement error on reported data.

250 t of participants, to evaluate the impact of measurement error on risk estimates.

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

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

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

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

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

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

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

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

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

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

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

262 Failure to account for measurement error resulted in clinically meaningful unde

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

264 nt a reparameterized imputation approach for measurement error (RIME) that can be used with internal

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

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

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

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

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

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

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

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

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

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

275 ndicate the minimum strength of differential measurement error that would be needed to explain away a

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

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

278 sed by the research community to account for measurement errors that arise during sample preparation

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

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

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

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

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

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

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

286 We corrected for measurement error using recently developed methods that

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

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

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

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

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

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

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

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

295 We discuss important systematic and random measurement errors when using these kits and suggest mea

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

297 nic and out-of-clinic BP, and concerns about measurement error with manual BP measurement techniques

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

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

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