ライフサイエンスコーパス: missing data

1 the EQ-5D ( pound17,941 with imputation for missing data).

2 s to be used (especially in the treatment of missing data).

3 as by modified intention to treat (excluding missing data).

4 r internet use (denominators varied owing to missing data).

5 sitive screening in 87.3% (n = 96/110, n = 6 missing data).

6 iffer between the groups, after allowing for missing data.

7 tation analysis was performed to address for missing data.

8 uld address issues of both noncompliance and missing data.

9 tools for low coverage data and can estimate missing data.

10 ecipients, adjusting for baseline scores and missing data.

11 from each participant, including those with missing data.

12 established in 129 (10%) patients because of missing data.

13 bility across populations, and robustness to missing data.

14 ll available data and without imputation for missing data.

15 y, and used multiple imputation to deal with missing data.

16 troduced through use of defaults in place of missing data.

17 o-treat basis, with multiple imputations for missing data.

18 mals using datasets with different levels of missing data.

19 ficult problems such as methods for reducing missing data.

20 cts of differing assumptions and methods for missing data.

21 ced by appropriate methods for accommodating missing data.

22 variables, but accuracy was highest with no missing data.

23 op a method to directly model non-detects as missing data.

24 dest sample sizes, incomplete pedigrees, and missing data.

25 lth confounders, with multiple imputation of missing data.

26 ologic data sets in which some patients have missing data.

27 Findings were robust to missing data.

28 and used multiple imputation to account for missing data.

29 Main analyses were imputed for missing data.

30 and this effect increased with the amount of missing data.

31 Multiple imputation was used to handle missing data.

32 ed in our analysis because of high levels of missing data.

33 nts and last observation carried forward for missing data.

34 stent in sensitivity analyses accounting for missing data.

35 submissions have high rates of incomplete or missing data.

36 e of the network, and is robust to noise and missing data.

37 ver, compliance is a problem that results in missing data.

38 observation-carried-forward method to impute missing data.

39 ultiple imputations were employed to address missing data.

40 d data sets that differed in their amount of missing data.

41 e outcome was used to decrease the effect of missing data.

42 erred from a taxon-sparse matrix with little missing data.

43 Multiple imputation was used to address missing data.

44 ors is used, and training in the presence of missing data.

45 nical trials can be substantially reduced by missing data.

46 f sequence errors, and a large proportion of missing data.

47 ures had notable floor or ceiling effects or missing data.

48 ation and weighting were used to account for missing data.

49 uded in the analysis, without imputation for missing data.

50 of the studies failed to report or describe missing data.

51 good predictive validity for countries with missing data.

52 hazards model, using multiple imputation for missing data.

53 sample sizes vary among projects because of missing data.

54 sis of extended pedigrees and pedigrees with missing data.

55 ability weighting was applied to account for missing data.

56 tudy and 19 could not be included because of missing data.

57 arse LDA model for datasets with and without missing data.

58 t 9 months; multiple imputation was used for missing data.

59 uted to findings-for example, differences in missing data.

60 putation for varying mechanism and amount of missing data.

61 l misclassification error in the presence of missing data.

62 ere performed, using multiple imputation for missing data.

63 Many patients had missing data.

64 gardless of the chosen approach for handling missing data.

65 ntion to treat, with multiple imputation for missing data.

66 mate the likelihood of reporting bias and/or missing data.

67 A multiple imputation procedure was used for missing data.

68 n maximization algorithm to handle extensive missing data.

69 and a survival model to handle non-ignorable missing data.

70 e-control status that included imputation of missing data.

71 maximum likelihood estimates to account for missing data.

72 ability weighting and multiple imputation of missing data.

73 ect selection, and inappropriate handling of missing data.

74 information in spite of great proportions of missing data.

75 9.8%) had a spot sign, and 24 of 123 without missing data (19.5%) experienced ICH expansion.

76 n, data consistency, baseline imbalance, and missing data), (2) reporting any important issues that e

77 Patients with missing data (24.8%) had higher mortality than those wit

78 g and adjustment; (2) handling and impact of missing data; (3) consistency and clinical relevance of

79 tively as follows: 11 for high percentage of missing data, 4 for skewness, and 1 for inadequate item

80 After excluding patients with missing data, 54 (35%) patients had no immediate therapy

81 infant born at 22 weeks excluded because of missing data (6 of 27 [22%] born at 22 weeks, and 16 of

82 Death (n = 102; missing data: 6) was attributed to liver disease in 48 (

83 the 159 patients, 2 were excluded because of missing data, 79 underwent LDKT alone (group A), and 78

84 ailable data, including data from women with missing data, 95.5% of participants were included in the

85 e of baseline-carried-forward imputation for missing data.A total of 153 participants (means +/- SDs:

86 CI, 1.27-3.37; P = .004, with imputation of missing data; absolute difference, 17.2%; 95% CI, 6.2% t

87 .8%) had higher mortality than those without missing data (adjusted hazard ratio = 1.36, 95% confiden

88 he uncertainty due to multiple imputation of missing data affects the cluster analysis outcomes-namel

89 derations, including a substantial amount of missing data and a nonuniform distribution of sequence r

90 comprised a multiple imputation approach for missing data and a subgroup analysis for the more severe

91 gradual changes in composition, accommodates missing data and allows for coherent estimates of uncert

92 epidemiologic studies which are affected by missing data and analyzed by logistic regression, exposu

93 After imputing missing data and applying NHANES sampling weights, we ex

94 Moreover, we examine the effects of common missing data and common modeling assumptions on (r)KFP,

95 stimating equations model that accounted for missing data and covariates showed no significant differ

96 the small numbers of APOE-e4/e4 individuals, missing data and differential dropout, limited ethnic an

97 ak data while explicitly accounting for both missing data and erroneous data.

98 d genotypes to account for genotyping error, missing data and false matches.

99 (cGBS) strategies suffer from high rates of missing data and genotyping errors, particularly at hete

100 od can (1) effectively reproduce patterns of missing data and heterozygosity observed in real data; (

101 d simulation studies, we found the amount of missing data and imputation method to substantially chan

102 roup analyses, including when accounting for missing data and in a subgroup of patients with a pulse

103 l item reduction was informed by presence of missing data and individual item response pattern.

104 ation maximization algorithm to both address missing data and introduce priors to promote sparsity.

105 s were rated as high risk of bias because of missing data and last-observation-carried-forward method

106 sults, authors should disclose the amount of missing data and other details.

107 We applied multiple imputation to address missing data and performed binary logistic regression an

108 onal study design avoided the limitations of missing data and potential selection biases inherent in

109 After exclusions for missing data and prevalent cancer, 13 253 ARIC participa

110 ase-control data allows for the inclusion of missing data and prior knowledge, while investigating as

111 s were performed, with varying approaches to missing data and recanting.

112 approaches can and should be used to handle missing data and repeated measurements.

113 st-effective, this method produces extensive missing data and requires complex bioinformatics analysi

114 ftware has simplified multiple imputation of missing data and the analysis of multiply imputed data t

115 The model allows for missing data and typing error, but does not model linkag

116 It remains unclear how robust SAOMs are to missing data and uncertainty around social relationships

117 o contact the authors of primary studies for missing data and with the experts in the field.Trials we

118 tested and 99.77% were concordant, 0.14% had missing data, and 0.09% were discordant.

119 analysis was performed to test the effect of missing data, and a composite outcome was used to decrea

120 variables, omission of cases with excessive missing data, and approaches for imputing highly skewed

121 rring chromatin states, improved handling of missing data, and linear scaling with dataset size.

122 ing health literacy and functional outcomes, missing data, and potential for confounding.

123 ensitivity analyses, multiple imputation for missing data, and probabilistic sensitivity analysis.

124 sing a pan-Antarctic Bayesian model to infer missing data, and show that spatial aggregation leads to

125 ly incorporate uncertainty due to imbalance, missing data, and small sample size.

126 algorithm separates parameters, accommodates missing data, and supports prior information on relation

127 We used multiple imputation to estimate missing data, and the imputation model captured clusteri

128 Missing data are a commonly occurring threat to the vali

129 Missing data are a prevailing problem in any type of dat

130 le, various issues in analyzing studies with missing data are discussed.

131 non-independence of infectious-disease data, missing data, ascertainment bias, consistency between mo

132 ability of WCWs were determined by assessing missing data, association with validated AD outcomes, an

133 founders, under a surprisingly wide range of missing-data assumptions.

134 pleted using the number of patients with non-missing data at baseline and at least one post-baseline

135 last observation carried forward method for missing data at interim visits.

136 ropometric measures are typically subject to missing data at various time points, which was also the

137 describe 4 Bayesian methods for imputing the missing data based on a missing-at-random assumption: mu

138 the statistical approaches for dealing with missing data be explicitly stated, and that PRO-specific

139 presence of modest genetic heterogeneity or missing data but also identified multiple candidate gene

140 nectivity that allowed us to approximate the missing data by extrapolation.

141 We simulated missing data by omitting the predictor dyspnea cohort-wi

142 Missing data can affect the accuracy of oil field carbon

143 Even in the absence of bias, missing data can hurt precision, resulting in wider conf

144 Incorrectly handling missing data can lead to imprecise and biased estimates.

145 Simulations indicate that loci with missing data can produce biased estimates of key populat

146 Missing data can result in biased estimates of the assoc

147 tal homoplasy and the detrimental effects of missing data collectively obscure stem bandicoot relatio

148 Findings were compared using 4 different missing-data complete-case analysis, 2 multiple-imputati

149 class and quality of life were reported, but missing data complicated interpretation.

150 in patients with ALS or FTD is prevented by missing data demonstrating cosegregation of the variants

151 Because of missing data, developmental outcomes, including four inf

152 mputation approaches to assess the effect of missing data did not change the results of the primary a

153 Missing data did not differ according to treatment group

154 se may be because of insufficient follow-up, missing data, disease heterogeneity, inconsistent compli

155 that long-branch attraction, saturation, and missing data do not influence these results.

156 ober 2007-September 2010), and the number of missing data elements has generally declined.

157 on procedure or weighted means were used for missing data elements.

158 genotype data into haplotype data, imputing missing data, estimating recombination rates, inferring

159 false positives increased with the level of missing data for all imputation methods.

160 , IOP asymmetry between eyes of >5 mmHg, and missing data for any covariables.

161 Exclusion criteria were missing data for either race or initial PD modality.

162 The rate of missing data for potential covariates was reported in 9%

163 ght less than 500 g or more than 7000 g; and missing data for pregnancy weight gain.

164 ssment not available in 24% of patients, and missing data for some patients and SCA characteristics.

165 There were missing data for the primary outcome (yoga group, n = 21

166 oftware solutions in the accuracy, yield and missing data fraction of variant calling, as tested on l

167 t of >107,000 amino acids with less than 28% missing data from 27 flatworm taxa in 11 orders covering

168 ize of each VRC01 administration regimen and missing data from participants who were unable to comple

169 An essential property for missing data imputation and detection of outliers is tha

170 letion methods, is flexible to be applied to missing data imputation for large meta-analysis with dif

171 lar, we illustrate the method's capacity for missing data imputation, data fusion and clustering.The

172 studies present computational challenges for missing data imputation, while the advances of genotype

173 including normalization, discretization and missing data imputation.

174 ucleotide polymorphism panel of soybean with missing data imputed using various methods, (2) evaluate

175 ase severity, age, and Karnofsky score, with missing data imputed.

176 ceived at least one dose of study drug, with missing data imputed.

177 of applying different approaches to handling missing data in an analysis of the association between b

178 tion of the fluxes and ability to impute the missing data in between the measurements.

179 ation (MI) has been widely used for handling missing data in biomedical research.

180 Multiple imputation was used to deal with missing data in covariates.

181 Urgent efforts are needed to fill the missing data in developing countries.

182 s in the cohort to compensate for sparse and missing data in each individual.

183 on (MI) is increasingly being used to handle missing data in epidemiologic research.

184 uations (MICE) is commonly used for imputing missing data in epidemiologic research.

185 ion is particularly well suited to deal with missing data in large epidemiologic studies, because typ

186 on model framework may be employed to assess missing data in LCAs of products and processes.

187 lic dropout is a commonly observed source of missing data in microsatellite genotypes, in which one o

188 than competing methods both with and without missing data in most of the experiments.

189 It can compensate for missing data in one source, and can reduce false positiv

190 ionship information to incorporate partially missing data in the analysis while correcting for depend

191 A-DPB1 and HLA-DRB3-5, is highly tolerant of missing data in the imputation panel and works on standa

192 t assessable at 2 months and were counted as missing data in the intention-to-treat analysis.

193 We discuss the impact of missing data in this trial, its implications for informa

194 ple sizes or simplistic methods for handling missing data, including last-observation-carried-forward

195 use of EHR data include: data availability, missing data, incorrect data, and vast quantities of uns

196 Adjustment for missing data increases the group-wise linkage rates by 4

197 The experimental results on missing data indicate that our joint learning method suc

198 METHODS for handling missing data, intrafamily correlation, and competing ris

199 The problem of missing data is commonly handled by imputation; however,

200 eal-time results generation and (2) handling missing data is still challenging.

201 ten only satisfactory approach to addressing missing data is to prevent it.

202 Perhaps the most common approach to handling missing data is to simply drop those records with 1 or m

203 To meaningfully reduce missing data, it is important to recognize and address m

204 seizure analysis we excluded 38 people with missing data leaving 657 (309 male, and 249 aged <18 yea

205 Missing data may affect absolute and relative risk estim

206 od of throwing out all participants with any missing data may lead to incorrect results and conclusio

207 Results After imputation of missing data, mean cumulative costs were -euro3,950 (95%

208 We propose a model of the missing data mechanism and develop a method to directly

209 ementation for epidemiologists familiar with missing data methods.

210 ther improved by using any of the 4 proposed missing data methods; the improvement is equivalent to a

211 ; usual care group, n = 18) and differential missing data (more in the yoga group) for secondary outc

212 for exclusions were nondeployment (n = 34), missing data (n = 181), and rank of noncommissioned and

213 data) at 12 months, excluding patients with missing data (n = 4) and those requiring a glaucoma-rela

214 n distributions and systematically addressed missing data, non-linear time trends, and representative

215 ng completely at random and likely represent missing data occurring not at random.

216 n was used to impute tumor markers for those missing data on 1-3 markers.

217 72 did not undergo the procedure and two had missing data on anaesthetic strategy), 236 (30%) of 797

218 2013 that included PCI, excluding those with missing data on bleeding complications or who underwent

219 lable, and there was a substantial amount of missing data on cardiovascular risk factors.

220 nalysis was restricted to persons who had no missing data on covariates (859 cases, 868 controls).

221 ormation maximum likelihood method to handle missing data on four relationship items.

222 findings were not the result of bias due to missing data on grandparental age.

223 Missing data on lifestyle interventions, possible miscla

224 Missing data on noninvasive stress tests present a chall

225 orting for allocation concealment, dropouts, missing data on outcomes, and heterogeneity in biomarker

226 ersons receiving instruction; prevalences of missing data on serving size and incomplete food descrip

227 Missing data on smoking status and comorbidity.

228 nces in the content, quality of data values, missing data on vitamin D2 and 25(OH)D3 and documentatio

229 admissions, records were excluded because of missing data or if they were for an individual's second

230 l field change was not evaluable, because of missing data or severe visual field loss at baseline.

231 ims data, such as incomplete, inaccurate, or missing data, or the lack of specific billing codes for

232 Although this matrix has more missing data, our a posteriori partitioning strategy red

233 6 person-years); 138 patients (16.4%) had no missing data over 10 years of follow-up.

234 stics and cost of implementing PRO programs; missing data, particularly from hard-to-reach and ill pa

235 propriate statistical methods to account for missing data, patient dropout, and use of rescue medicat

236 Analysts should examine the missing data pattern and try to determine the causes of

237 arch has been conducted for handling general missing data patterns where multiple variables have miss

238 igh-dimensional data that can handle general missing data patterns.

239 (>97-99%), even at heterozygous sites, less missing data per marker across a population of samples,

240 nd it is common for some individuals to have missing data (phenotypes, genotypes, or covariates).

241 After imputation of missing data, point prevalence was 1.0 case (0.3-2.4) pe

242 The FI was less than the number of missing data points in 52.6% of trials.

243 s = 9% to 24%) and nonacute indications with missing data precluding appropriateness classification (

244 .7]; 96872 males, 23963 females, and 12 with missing data) presented with sports-related ocular traum

245 e by orthodox meta-analysis is confounded by missing data (publication bias).

246 chieves high recovery accuracy even when the missing data rate is as high as 90%.

247 Sequence from 2 HapMap samples confirmed missing data rates of 2-3% at sites successfully typed b

248 se with multiple listed races and those with missing data regarding race or the diagnostic cardiac ca

249 Missing data (reporting bias) was determined by Egger re

250 international normalized ratio < 1.3 and/or missing data required for analysis, we compared all-caus

251 ivity analysis using multiple imputation for missing data resulted in acupuncture appearing less effe

252 to-treat cohort with multiple imputation for missing data, RYGB participants had the greatest mean we

253 sian Markov chain Monte Carlo) accounted for missing data, selective dropout from graft failure, corr

254 n to characterizing uncertainty and handling missing data should be taken into account when interpret

255 eractome while taking into account noise and missing data, should be applicable to a wide range of hi

256 Current superposition methods deal with missing data simply by superpositioning a subset of poin

257 ality for completeness, diagnostic accuracy, missing data, stochastic variations, and probable causes

258 across populations, and higher robustness to missing data than any clinical phenotype.

259 d this approach more accurately reconstructs missing data than imputation based on neighboring CpGs (

260 tion time for such inference, as well as the missing data that confound low-frequency allele discover

261 We discuss various classifications of missing data that may arise in a study and demonstrate i

262 To address the problem of missing data, the authors employed sequential regression

263 ion group, after excluding two subjects with missing data, the median absolute difference of the tota

264 Despite amounts of missing data, the transcriptomic loci resolve deeper nod

265 adults; after excluding the 81 patients with missing data, these population estimates translate to 97

266 posite outcomes (one study), the handling of missing data (three studies), unadjusted versus adjusted

267 propose taking advantage of methodology for missing data to estimate relationships and adjust outcom

268 e and transparent means to impute univariate missing data under general missing-not-at-random mechani

269 ls in phylogenomic reconstruction, including missing data, unequal rates of evolution, and others.

270 en (n = 362) and men (n = 220) adjusting for missing data using multiple imputation and compared psyc

271 gative set, large-scale learning and massive missing data values.

272 An alignment of 1,541 loci that allowed missing data was 87% complete and resulted in a highly r

273 cy endpoint was met, a substantial amount of missing data was observed.

274 Although missing data weaken interpretation of the findings, admi

275 Missing data were common for systolic blood pressure (21

276 died during the first 3 days of life, or had missing data were excluded from the analysis.

277 who reported a history of cancer or who had missing data were excluded, yielding 476,396 subjects fo

278 allergic conjunctivitis, and 2 patients with missing data were excluded; 81 patients were included in

279 Noise and missing data were further introduced in the datasets in

280 t and in a dataset of complete records where missing data were generated (simulated dataset).

281 Missing data were handled using multiple imputations.

282 Missing data were handled with multiple imputation, and

283 Missing data were imputed with multiple imputation metho

284 Missing data were imputed.

285 ed on microsatellites as more loci with more missing data were included.

286 Missing data were managed according to the last observat

287 Missing data were managed using multiple imputation, acc

288 Analysis was by intention to treat, and missing data were multiply imputed.

289 In a sensitivity analysis, in which missing data were not imputed, peak VO2 at 24 weeks decr

290 cocaine nonuse days, irrespective of whether missing data were not or were imputed conservatively to

291 the United States, and loss to follow-up and missing data were observed.

292 Missing data were replaced using multiple imputation.

293 Missing data were replaced using multiple imputation.

294 Missing data were resolved by author contact.

295 Datasets where the missing data were simulated for oxygenation or oxygenati

296 nvestigators should consider how to minimize missing data when planning a study.

297 alysis, including individuals with partially missing data, while properly accounting for the dependen

298 st one post-baseline assessment, and imputed missing data with the last observation carried forward a

299 g 5,362 participants (5% of participants had missing data) with a mean annual progression of 14 micro

300 ling sampling over different time points and missing data without extra computational difficulty.