ライフサイエンスコーパス: missing data

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

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

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

4 uded in the maternal mortality analysis (108 missing data).

5 stical assumptions from ecological data with missing data.

6 nefits from sample pooling and should reduce missing data.

7 with multiple imputation used to account for missing data.

8 ints by 30-50% at 1% FDR, and thus decreased missing data.

9 s a well-established method for dealing with missing data.

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

11 hazards model, using multiple imputation for missing data.

12 putation for varying mechanism and amount of missing data.

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

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

15 lth confounders, with multiple imputation of missing data.

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

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

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

19 ce, as well as the analysis being limited by missing data.

20 sample sizes vary among projects because of missing data.

21 ferent ways in which they can be affected by missing data.

22 sis of extended pedigrees and pedigrees with missing data.

23 ability weighting was applied to account for missing data.

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

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

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

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

28 l misclassification error in the presence of missing data.

29 , and determining appropriate ways to manage missing data.

30 ere performed, using multiple imputation for missing data.

31 Many patients had missing data.

32 gardless of the chosen approach for handling missing data.

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

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

35 A multiple imputation procedure was used for missing data.

36 n maximization algorithm to handle extensive missing data.

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

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

39 maximum likelihood estimates to account for missing data.

40 ability weighting and multiple imputation of missing data.

41 ect selection, and inappropriate handling of missing data.

42 information in spite of great proportions of missing data.

43 tation analysis was performed to address for missing data.

44 uld address issues of both noncompliance and missing data.

45 d to preserve anonymity and we accounted for missing data.

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

47 ecipients, adjusting for baseline scores and missing data.

48 ta analysis, particularly in the presence of missing data.

49 from each participant, including those with missing data.

50 nd time, while accounting for uncertainty in missing data.

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

52 bility across populations, and robustness to missing data.

53 ll available data and without imputation for missing data.

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

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

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

57 mals using datasets with different levels of missing data.

58 ficult problems such as methods for reducing missing data.

59 cts of differing assumptions and methods for missing data.

60 ecovery to improve with increasing levels of missing data.

61 ced by appropriate methods for accommodating missing data.

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

63 lyses were performed to assess the effect of missing data.

64 s present under various causal structures of missing data.

65 to adjust for changes in hospital usage and missing data.

66 sier to use, required less time and had less missing data.

67 rding accuracy, with multiple imputation for missing data.

68 multiple imputation was used to account for missing data.

69 ion by chained equations to assess effect of missing data.

70 handle features of variable length and with missing data.

71 he use of multiple imputation to account for missing data.

72 hile being robust to noisy, heterogeneous or missing data.

73 and multiple imputation were used to handle missing data.

74 e testing when using multiple-imputation for missing data.

75 urther removal of duplicate participants and missing data.

76 as for variability in feature length and for missing data.

77 with chained equations to impute values for missing data.

78 imputation or other strategies for handling missing data.

79 mputations by chained equations to deal with missing data.

80 9.8%) had a spot sign, and 24 of 123 without missing data (19.5%) experienced ICH expansion.

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

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

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

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

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

86 concordant after local excision, with excess missing data (60%).

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

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

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

90 .8%) had higher mortality than those without missing data (adjusted hazard ratio = 1.36, 95% confiden

91 In a sensitivity analysis in which all missing data after 56 days were imputed as treatment fai

92 show that our approach can accurately impute missing data after genome segmentation, reversing the ty

93 olled, 963 patients were excluded because of missing data and 51 842 (98%) patients remained for the

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

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

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

97 ures of our neural network model in handling missing data and calculating prediction uncertainty enab

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

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

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

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

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

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

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

105 es, have been hampered by a preponderance of missing data and lack of methods to accommodate them.

106 The main study limitations were missing data and lack of randomization in the DMD analys

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

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

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

112 pecified PRO concepts, statistical analysis, missing data and sensitivity analysis, instrument comple

113 State-space models account for missing data and the error inherent when sampling carcas

114 hain Monte-Carlo simulation method to impute missing data and then fed the selected variables to mult

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

116 firmed interactions and to better understand missing data and unknowns.

117 We removed duplicates and participants with missing data and used all remaining participants to exam

118 Ignoring missing data and using only subjects with complete data

119 Twelve patients had missing data and were excluded.

120 he postoperative complications analysis (107 missing data) and 3684 were included in the maternal mor

121 from overlapping peptides, the imputation of missing data, and a normalization routine to determine r

122 ntention-to-treat principles, accounting for missing data, and adjusted for outcome baseline scores.

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

124 dardising statistical terminology related to missing data, and determining appropriate ways to manage

125 s that this resulted in a high proportion of missing data, and our analyses assume data are missing a

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

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

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

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

130 Missing data are a commonly occurring threat to the vali

131 The sources of batch effects and missing data are incompletely understood necessitating n

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

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

134 ception use, and was robust to the effect of missing data (assuming missingness at random).

135 artially caused by investigator confusion on missing data assumptions for different methods.

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

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

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

139 ntative of communities at low diversity, but missing data at low diversity from field studies could r

140 the 8-week group, including 3 patients with missing data at the SVR24 timepoint, all patients who ac

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

142 With selected populations or missing data, balance across treatment groups can be ins

143 n not only alleviate the impact of noise and missing data, but also predict unseen gene expression le

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

145 Missing data can affect the accuracy of oil field carbon

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

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

148 bilistic methods and can handle the inherent missing data characteristic that dominates single-cell C

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

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

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

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

153 Missing data did not differ according to treatment group

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

155 easured confounding or selection bias due to missing data (e.g., dropout).

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

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

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

159 omoeostasis model assessment (HOMA) indexes, missing data for baseline covariates, and missing data f

160 seline measures of glucose tolerance status, missing data for baseline homoeostasis model assessment

161 Patients with diabetes at baseline, missing data for baseline measures of glucose tolerance

162 r 1.73 m(2) at baseline (one participant had missing data for eGFR); 6974 (40.6%) had established ath

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

164 s, missing data for baseline covariates, and missing data for measures of glucose tolerance status at

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

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

167 workers' catchment areas for sampling, some missing data for variables derived from secondary source

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

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

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

171 the-counter medication use, dietary intake), missing data (i.e., only 20% of patients had a baseline

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

173 istics that require new methods for handling missing data imputation and differential proteome analys

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

175 s variance stabilization, normalization, and missing data imputation to account for the large dynamic

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

177 as an advisable method for trans-omics block missing data imputation.

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

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

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

181 d a preinjury opioid abuse diagnosis, or had missing data in a preselected variable.

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

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

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

185 increasingly used to address the presence of missing data in clinical research.

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

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

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

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

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

191 data set, gap filling may be used to reduce missing data in single samples remaining after peak dete

192 n of sample from birth to assessment age and missing data in some exposure areas from those assessed.

193 by chained equations was used to account for missing data in the development and internal validation

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

195 timating causal effects, careful handling of missing data is needed to avoid bias.

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

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

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

199 was significant heterogeneity in methods and missing data, limiting synthesis and precluding meta-ana

200 roup of 45,382 high quality SNPs (MAF >0.05; missing data <5%) were selected for analysis of this gro

201 Missing data may affect absolute and relative risk estim

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

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

204 etects as non-random missing data, model the missing data mechanism, and use this model to impute mis

205 guidance, we provide a brief introduction of missing data mechanisms and the unreasonable assumptions

206 Some problematic missing data methods such as complete case (CC) analysis

207 rlie CC and LOCF and recommend 2 appropriate missing data methods: multiple imputation and full infor

208 e propose to treat non-detects as non-random missing data, model the missing data mechanism, and use

209 categorical visual function responses, with missing data multiply imputed.

210 nd/or outcome (n = 7), omitting samples with missing data (n = 10), selecting variables based on univ

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

212 Unmeasured confounding, missing data (namely incomplete laboratory data), and lo

213 llow-up for a chronic condition, substantial missing data, no information on patient out-of-pocket an

214 Missing data occur frequently in clinical research.

215 Missing data occurred frequently, with only 13% of patie

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

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

218 72 did not undergo the procedure and two had missing data on anaesthetic strategy), 236 (30%) of 797

219 l selection bias during the follow-up due to missing data on frailty components.

220 Missing data on lifestyle interventions, possible miscla

221 positive, of whom 145 were excluded (84 had missing data on race or ethnic group, 9 were Hispanic, a

222 The dependence of missing data on these covariates must be considered to o

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

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

225 d by intrapartum antibiotics, breastfeeding, missing data, or familial factors.

226 6 person-years); 138 patients (16.4%) had no missing data over 10 years of follow-up.

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

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

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

230 (>97-99%), even at heterozygous sites, less missing data per marker across a population of samples,

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

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

233 nchmarking, we applied our approach to infer missing data points in the widely used BrainSpan resourc

234 cs experiments, which are often plagued with missing data points, we also integrate an imputation sys

235 .7]; 96872 males, 23963 females, and 12 with missing data) presented with sports-related ocular traum

236 Our analysis was limited by missing data prior to the establishment of nationwide ZI

237 In this paper, we address the missing data problem in distributed environments such as

238 Missing data prohibited meaningful analysis of patient a

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

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

241 le cell RNA-seq has a far larger fraction of missing data reported as zeros (dropouts) than tradition

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

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

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

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

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

247 come which could deal with a present complex missing data structure.

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

249 vels in most genes and higher frequencies of missing data than bulk population sequencing technologie

250 Evolutionary studies are often limited by missing data that are critical to understanding the hist

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

252 es, operationalizing variables, dealing with missing data, the importance of appropriate validation,

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

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

255 lly robust to conservative assumptions about missing data, the trial provides modest evidence that co

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

257 oteomics, where methods frequently result in missing data this increasing scale is also decreasing th

258 le feature vector for each patient, handling missing data through a resilient, multimodal dropout met

259 ata, as well as variable feature lengths and missing data, thus enabling its wide-spread use on any l

260 erogeneous data, including the high rates of missing data to be expected in the real-world setting, w

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

262 hted imputation method for trans-omics block missing data (TOBMIkNN) to handle gene-absence individua

263 utation model that accounts for a mixture of missing data types.

264 Missing data ubiquitously occur in randomized controlled

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

266 Inconclusive results, missing data, variable adherence, patient-reported findi

267 Although missing data weaken interpretation of the findings, admi

268 Missing data were <20% and adherence to intervention of

269 Participants missing data were assumed to be smoking.

270 Missing data were common for systolic blood pressure (21

271 red specifically to MNAR in situations where missing data were completely MNAR.

272 sensitivity analysis in which neonates with missing data were counted as having had a primary outcom

273 Statistical approaches for dealing with missing data were documented in 26 (37%) RCTs.

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

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

276 Missing data were handled by multiple imputation of 50 d

277 Missing data were handled using multiple imputations.

278 Missing data were handled with multiple imputation by ch

279 Missing data were handled with multiple imputation, and

280 Missing data were handled with multiple imputations.

281 Missing data were imputed to account for variable respon

282 Missing data were imputed with multiple imputation metho

283 blood loss from baseline to the final month; missing data were imputed with the use of multiple imput

284 Missing data were imputed.

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

286 Missing data were managed using multiple imputation, acc

287 Missing data were multiply imputed with chained equation

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

289 study are that children with high levels of missing data were not excluded, interview participants m

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

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

292 Missing data were often incompletely reported, and conti

293 Missing data were replaced using multiple imputation.

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

295 ve and false-negative rates and entries with missing data-were applied to ensure reliable data, 11 24

296 ple imputation should be used to account for missing data when developing prognostic models.

297 ses; the exception is at very high levels of missing data, where stochastic strategies, like acquaint

298 Limitations of this study include potential missing data, which is in part addressed by the use of c

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

300 ty analyses based on multiple imputation for missing data yielded P values for the primary end point