ライフサイエンスコーパス: dropout

1 and time to relapse (defined as first use or dropout).

2 or selection bias due to missing data (e.g., dropout).

3 omized with 33 in each arm (accounting for 2 dropouts).

4 outs) and biological mesh closure (n = 50; 2 dropouts).

5 ldren lost to follow up (3.4%) and no school dropout.

6 eek 12 or at the last follow-up visit before dropout.

7 patient characteristics and rate of waitlist dropout.

8 treatment groups, even after accounting for dropout.

9 n static over the next decade due to patient dropout.

10 ferences between groups after accounting for dropout.

11 erience an increase from 53% to 64% waitlist dropout.

12 mbrane thickening, albuminuria, and podocyte dropout.

13 ach to achieve the target dose without early dropout.

14 ter retinal tubulations and choriocapillaris dropout.

15 fter listing) owing to tumor progression and dropout.

16 xed-effects model accounting for informative dropout.

17 and contact lens use and selection bias from dropout.

18 ecovery, other interview-based outcomes, and dropout.

19 analyses; 18 of these had relapsed prior to dropout.

20 and survival model to adjust for informative dropout.

21 ge remained significantly associated with MG dropout.

22 rol to account for false alleles and allelic dropout.

23 ent strategies offered to HCC patients after dropout.

24 plification, including allelic imbalance and dropout.

25 .22-2.84) were predictors of mortality after dropout.

26 on of artefenomel) but there were no further dropouts.

27 outs, and insufficient response: 26 (33%) of dropouts.

28 ." RESULTS: There were no deaths or patients dropouts.

29 degree of overdispersion or large number of dropouts.

30 ostly viral and alcoholic, and there were no dropouts.

31 nsion reduction or imputation to address the dropouts.

32 hat lead to excess false zero values, termed dropouts.

33 were well tolerated, with no side effects or dropouts.

34 CI 0.037 to 0.11]) and a negative effect for dropout (-0.070 [-0.105 to 0.036]) and death (-0.002 [-0

35 B/RIF group were on treatment due to reduced dropout (15 [8%] of 185 in the MTB/RIF group did not rec

36 38% vs. 22%; P = 0.017) and a higher risk of dropout (22% vs. 8%; P = 0.01).

37 he outcomes and no difference in participant dropout (24.5% in PE and 20.0% in EMDR, P = .57).

38 t and CABG patients had similar rates of CRP dropout (27.9% vs. 37.2% respectively, P=0.4).

39 uption and lower eyelid meibomian gland (MG) dropout, adjusted for age and sex (odds ratio [OR] = 28.

40 Overall, the ARMS-qPCR had frequent allele-dropout (ADO), rendering it inappropriate as the sole di

41 treating extensive BCLM with the pitfall of dropout after the first stage.

42 er CFU counts in the home had more severe MG dropout, after adjusting for age and other confounders.

43 01), but not associated with transplant list dropout (aHR 0.95, 95% CI 0.65-1.39, p=0.79) or post-tra

44 egraded evidence leading to allele and locus dropout; allele sharing of contributors leading to allel

45 ent randomized trials did not handle patient dropout and "rescue" medication properly.

46 on heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributi

47 ect drugs had increased probabilities of ART dropout and faster disease progression.

48 We employ both coarse-grained node dropout and fine-grained edge dropout to address the iss

49 associated with decreased waitlist mortality/dropout and increased transplant probability.

50 ral smoothing, thereby decreasing the signal dropout and increasing the temporal signal-to-noise rati

51 gostino Kaplan-Meier approach to account for dropout and loss to follow-up before 10 years.

52 Participant dropout and lower-than-expected disease event rate limit

53 g units and deep learning techniques such as dropout and momentum training to accelerate the DNN trai

54 , Ddr1(-/-) models had increased acinar cell dropout and reduced proliferation with no difference in

55 ity analyses examining the effect of patient dropout and treatment adherence did not alter the result

56 f SCS technologies including frequent allele dropout and variable sequence coverage may prohibit a pe

57 Dropouts and adverse events were limited and did not dif

58 tudinal data collected can feature mid-study dropouts and complex nonlinear temporal response pattern

59 on rates, skewed mutational outcomes, target dropouts and different sequencing strategies.

60 techniques including wide residual networks, dropouts and dilated convolutions.

61 fect among GT 2/3 patients, who showed fewer dropouts and higher SVR rates.

62 approach in which we explicitly account for dropouts and isoform quantification errors.

63 dalities across single cells, meanwhile, the dropouts and low expressions are treated as left truncat

64 The primary endpoint was analyzed without dropouts and was reached in 43% (7 of 16) of the control

65 are subject to stochastic effects, such as "dropout" and "dropin" of alleles, and highly variable st

66 domly assigned to primary closure (n = 54; 1 dropouts) and biological mesh closure (n = 50; 2 dropout

67 CD4 T-cell count progression, mortality, ART dropout, and ART reinitiation using a continuous-time mu

68 (FP) and false-negatives (FN) due to allelic dropout, and cell doublets, significantly complicate the

69 ariable risks of tumor progression, waitlist dropout, and posttransplant recurrence.

70 re protected from immune infiltration, crypt dropout, and ulcers following administration of dextran

71 methods to account for missing data, patient dropout, and use of rescue medication.

72 events or intercurrent illness: 27 (34%) of dropouts, and insufficient response: 26 (33%) of dropout

73 n has the desired robustness to outliers and dropouts, and it significantly improves eQTL mapping.

74 data augmentation, batch normalization, and dropout are appealing solutions to reduce overfitting an

75 aluation of the dropout rate and reasons for dropout are important not only in the planning of clinic

76 -term trends in density while accounting for dropout as well as for measurement error.

77 Instead of treating dropouts as a problem to be fixed, we embrace it as a us

78 We find that the high rate of dropouts associated with scRNA-seq is a major obstacle t

79 ained edge dropout can further fine-tune the dropout at the interaction (edge) level.

80 t can efficiently and systematically enforce dropout at the node (gene) level, while fine-grained edg

81 76, using intention-to-treat with retrieved dropout at week 24 and observed cases at week 76, consis

82 median follow-up was 31 months (IQR 14-62); dropout before starting antiretroviral therapy or AIDS o

83 cRNA-seq analysis methods typically overcome dropout by combining information across cells in a lower

84 In particular, coarse-grained node dropout can efficiently and systematically enforce dropo

85 e node (gene) level, while fine-grained edge dropout can further fine-tune the dropout at the interac

86 Participant dropouts can reduce the power of allergen immunotherapy

87 ted a self-reported survey on factors behind dropout cases and poor adherence cases.

88 lthy participants, likely due to fMRI signal dropout caused by the air/bone interface of the petrous

89 )nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves model

90 e 1- and 2-year cumulative probabilities for dropout (competing risk) were 24.1% and 34.2% in the dow

91 ography, spirometry, 6-minute-walk distance, dropouts, compliance, and side effects were evaluated.

92 trodecrement and paraspinal electromyography dropout consistent with atonic seizures.

93 Focal capillary dropout could be visualized in the SVC, but not the ICP

94 Focal capillary dropout could be visualized more clearly in the NFLP tha

95 Treatment outcome (cure, dropout, death, or development of drug-resistant tubercu

96 Conditional waitlist mortality/dropout decreased, and transplant probability increased

97 performing visualization, imputation of gene dropouts, detection of rare transcriptomic profiles, and

98 s were rates of response/remission rates and dropout/discontinuation due to adverse events.

99 Dropout due to EHD (HR, 0.61; 95% CI, 0.38-0.98) and und

100 d risk factors for death, after waiting list dropout due to hepatocellular carcinoma (HCC) progressio

101 ignificant reduction in the risk of waitlist dropout due to progression (relative risk [RR], 0.32; 95

102 Dropouts due to adverse effects increased steeply throug

103 ction in depression severity), tolerability (dropouts due to adverse effects), and acceptability (dro

104 nge, serious adverse events, adverse events, dropouts due to adverse effects, and treatment adherence

105 n the yoga group; serious adverse events and dropouts due to adverse events were comparable between g

106 nonserious, or serious adverse events and of dropouts due to adverse events were found when comparing

107 Adverse events (most mild or moderate) and dropouts due to adverse events were more common with nal

108 d by multiple averse factors, notably signal dropouts due to magnetic inhomogeneity and low signal-to

109 e associated with an increased likelihood of dropouts due to side effects (meta-regression: beta=0.00

110 tment failure, recurrence, or death or study dropout during treatment) measured 24 months after the e

111 otein regions associated with a strong sgRNA dropout effect in the screens.

112 discrepancy becomes more prominent when the dropout effect or the overdispersion effect is large.

113 ed PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell se

114 cribe a novel imputation method that reduces dropout effects in single-cell sequencing.

115 lnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributio

116 nder what conditions of treatment uptake and dropout elimination of HIV is feasible.

117 cal interventions were acceptable with lower dropout, even in studies rated at low risk of attrition

118 onal measurements as well as an abundance of dropout events (that is, zero expression measurements).

119 However, the prevalence of dropout events imposes complications during data analysi

120 ces an ensemble learning method for imputing dropout events in scRNA-seq data.

121 Such dropout events present a fundamental challenge for vario

122 Imputation of dropout events that may mislead downstream analyses is a

123 tperforms the existing methods in recovering dropout events, capturing true distribution of gene expr

124 These zero counts, or dropout events, complicate the analysis of scRNA-seq dat

125 d, including the computational management of dropout events, the reconstruction of biological pathway

126 duction methods because of the prevalence of dropout events, which lead to zero-inflated data.

127 pressed genes with zero or low read counts ('dropout' events) and multimodal data distributions.

128 ial confounders, missing covariate data, and dropout, ever-use of two pesticide classes, fumigants an

129 quencing, as they do not account for allelic dropout, false-positive errors and coverage nonuniformit

130 ms, categorical response to treatment, study dropout for any reason and for inefficacy of treatment,

131 pertussis-tetanus (DPT) vaccine coverage and dropout for children aged 12-23 months in 52 African cou

132 ulative incidence of waitlist events (LT and dropout for death or too sick) were calculated and model

133 LRT is used to mitigate waitlist dropout for patients with HCC awaiting LT.

134 ophrenia, categorical response to treatment, dropouts for any reason and for inefficacy of treatment,

135 The relationship between the dose and dropouts for any reason indicated optimal acceptability

136 due to adverse effects), and acceptability (dropouts for any reasons), all after a median of 8 weeks

137 rough recombinant RNA spike-ins, we estimate dropout-free technical reliability as low as ~250 copies

138 LD scores, will have an increase in waitlist dropout from 30% to 44%.

139 [CI], 0.06-1.85; I(2) = 0%) and of waitlist dropout from all causes (RR, 0.38; 95% CI, 0.060-2.370;

140 In one series, the rate of dropout from all causes at 6 months in T1 HCC patients w

141 Selective dropout from graft failure did not affect the cell loss

142 Carlo) accounted for missing data, selective dropout from graft failure, correlations between fellow

143 to side effects could be a factor affecting dropout from SLIT.

144 sidered the factors of poor adherence to and dropout from sublingual immunotherapy (SLIT) by verifyin

145 s a lack of information about survival after dropout from the liver transplant waiting list.

146 liver disease may lead to increased risk of dropout from the liver transplant waitlist.

147 tes of failure to adhere to the protocol and dropout from the study, the greater the risk of bias.

148 Dropout from the transplant list was equal in both group

149 5 to 34 years were less likely to experience dropout from the waiting list compared with those aged 1

150 ne drug screens and self-report) and time to dropout from treatment.

151 In this article, we propose a novel dual-dropout GCN (DDGCN) for learning more robust gene repres

152 olates showed results compatible with a mecA dropout genotype.

153 foveal avascular zone, perifoveal capillary dropout grade, and presence of morphologic features of d

154 se (DPT1) coverage (<=50%) and high relative dropout (>=30%) together drove low DPT3 coverage across

155 t, intention-to-treat analysis, and adequate dropout handling.

156 of LT (0.81 and 0.89; P = 0.02) and greater dropout (HR 1.99 and 1.43; P < 0.001).

157 ta, individuals had increased hazard for ART dropout if they were from the south (adjusted hazard rat

158 datasets show that scIGANs is effective for dropout imputation and enhances various downstream analy

159 branching in 17 (41.5%), regional capillary dropout in 9 (22.0%), terminal bulbing in 6 (14.6%), abn

160 loss of MMP-12 attenuated retinal capillary dropout in early OIR and mitigated pathological retinal

161 LRT mitigates tumor progression and waitlist dropout in HCC patients within MC, but data on its impac

162 nt study identifies the patterns of waitlist dropout in patients with HCC and provides evidence for t

163 Factors predicting dropout in the downstaging group included pretreatment a

164 To overcome the fMRI signal dropout in the neighborhood of the NFA, we combined high

165 MSC therapy reduced crypt dropout in the small intestine and promoted elevated exp

166 e dropout to address the issue that standard dropout in vanilla GCN is often inadequate in reducing o

167 enges caused by overdispersion and excessive dropouts in RNA-seq.

168 that RESCUE is a useful tool for mitigating dropouts in single-cell RNA-seq data.

169 There were 11 dropouts in the placebo group and nine in the pexidartin

170 There were no dropouts in this group and all 3 patients were assessed

171 h take advantage of the prevalence of zeros (dropouts) in scRNASeq data to identify features.

172 method for correcting false zeros (known as dropouts) in single-cell RNA-sequencing (scRNA-seq) data

173 Factors predicting dropout included sum of tumor number and largest tumor d

174 en varying background viral suppression, ART dropout, intervention effectiveness, and reduction in HI

175 Patient prognosis after dropout is dismal.

176 Ischemia (capillary dropout) is the only disease pathway that shows correlat

177 Median survival according to cause of dropout (LAD, EHD, or MVI) was 1.0, 4.4, or 3.3 months,

178 ipheral vascular changes including capillary dropout, late-phase angiographic posterior and periphera

179 network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in t

180 4 individuals, missing data and differential dropout, limited ethnic and racial diversity, and differ

181 ng 252 subjects, at 4 months, there were 30% dropouts, mainly due to side-effects.

182 ies aimed at preventing or delaying pericyte dropout may avoid or attenuate the retinal microangiopat

183 =2.98) and decreased likelihood of all-cause dropout (meta-regression: beta=-0.00093, 95% CI=-0.00165

184 missing data through a resilient, multimodal dropout method.

185 The rate of mecA/mecC dropout mutants was also evaluated.

186 ations of this study were the high number of dropouts (n = 93); possible underreporting of falls, fal

187 of errors such as false alleles and allelic dropouts (null alleles) in situations such as SNPs from

188 Abnormalities and vascular dropout observed within the choriocapillaris for pvOCT a

189 Dropouts occurred due to palpitations (one patient on el

190 plet MDA product to achieve a median allelic dropout of 15%, and using whole genome sequencing to ach

191 Results indicate that aging mice show dropout of meibomian glands with loss of gland volume an

192 magnetic resonance imaging leads to greater dropout of patients over time because of device implanta

193 Despite substantial dropout of patients, the healing pattern in event-free S

194 sease has long been attributed to an initial dropout of pericytes that enwrap the retinal microvascul

195 ieve comparable OS and DFS, despite the high dropout of the 2-stage strategy.

196 relativistic energies and produce a profound dropout of the ultra-relativistic radiation belt fluxes.

197 An increased risk of dropout on the waiting list can be expected, but with eq

198 Overall, only two dropouts (one rebound activity and one gastrointestinal

199 uggests that home CFU exposure may impact MG dropout, one of the DE measures, and may be a target for

200 Methods used to account for selective dropout only marginally changed these observed associati

201 d ratio=0.47; 95% CI=0.24, 0.92) to relapse (dropout or use cocaine) compared with the midazolam grou

202 of potential false negatives (due to allele dropout or variance in sequence coverage), false positiv

203 ies generally did not consider the impact of dropouts or isoform quantification errors, potentially c

204 ths; treatment failure; hospitalization; and dropout owing to any cause, non-adherence and intolerabi

205 should be regarded as a major risk factor of dropout owing to tumor progression and should be taken i

206 as the only other independent risk factor of dropout owing to tumor progression.

207 Group A exhibited 1 dropout patient and 1 failure, resulting in a survival r

208 good and poor adherence groups, except four dropout patients, the adherence tended to be poor in pat

209 We represent the dropout pattern by binarizing single-cell RNA-seq count

210 multiple published datasets that the binary dropout pattern is as informative as the quantitative ex

211 ring algorithm to cluster cells based on the dropout pattern.

212 ppear to be a major problem with a composite dropout percentage of 14% (95% CI:11.9-16).

213 ional method, called RESCUE, to mitigate the dropout problem by imputing gene expression levels using

214 We expect that recognizing the utility of dropouts provides an alternative direction for developin

215 r eventually leading to glomerular capillary dropout (rarefaction) and further increases in intraglom

216 baseline or during the study, or annualised dropout rate (7.7% [95% CI 6.2-9.5] for vaccine recipien

217 al group, those in the TES group had a lower dropout rate (hazard ratio=0.72, 95% CI=0.57, 0.92) and

218 arried out in order to establish the overall dropout rate among published double-blind, placebo-contr

219 Evaluation of the dropout rate and reasons for dropout are important not o

220 T1 HCC patients who did not receive LRT, the dropout rate at median follow-up of 2.4 years and the pr

221 on as it was likely impacted by an increased dropout rate before treatment, which led to crossover th

222 -month regimen, was associated with a higher dropout rate during treatment (5.0% vs. 2.7%) and more t

223 median adherence was 97.6% after 1 year; the dropout rate for treatment-related AEs was 0.9%.

224 he medical-therapy group), owing to a higher dropout rate in the medical-therapy group.

225 al-therapy group, P=0.009) owing to a higher dropout rate in the medical-therapy group.

226 culated as 35 patients for each group with a dropout rate of 10%.

227 ion group had a significantly higher overall dropout rate than the monotherapy groups but did not hav

228 Dropout rate was 8.3%, and nausea was the most frequentl

229 The dropout rate was higher in the lithium/sertraline combin

230 On an intention-to-treat basis, the dropout rate was higher in the M+ group and the 5-year a

231 However, the 6-month waitlist mortality/dropout rate was lower in group 2 at 1.2% than group 1 a

232 The dropout rate was more pronounced in the Everolimus group

233 nt 2-year OS and PFS rates allowing for that dropout rate were 59.6% and 30.7%, respectively, in the

234 Response rates and acceptability (dropout rate).

235 rcise training was well tolerated with a low dropout rate, and no major adverse events were related t

236 Interpretation is limited by the high dropout rate, null findings of prespecified analyses tha

237 Due to the high dropout rate, only 31 individuals were evaluated.

238 Small sample size and appreciable dropout rate.

239 q data, with increasing advantages at higher dropout rates (e.g., >60%).

240 ample weighting methods accounted for higher dropout rates among ethnic minorities and those with low

241 approach to reliably model the cell-specific dropout rates and amplification bias by use of external

242 hich is the first method to account for both dropout rates and complex scRNA-seq data distributions i

243 Secondary outcomes were dropout rates and measures of personality disorder trait

244 In contrast, psychotherapy trials had lower dropout rates and provided follow-up data.

245 mania or hypomania, and tolerability (using dropout rates as a proxy).

246 Although dropout rates did not differ between antidepressant and

247 Dropout rates did not differ between groups, and treatme

248 BT-I group, the standard DBT group had lower dropout rates from treatment (8 patients [24%] vs 16 pat

249 Relative dropout rates in placebo and active groups as well as re

250 Dropout rates in sublingual immunotherapy controlled stu

251 High treatment dropout rates suggest the importance of improving retent

252 The dropout rates were 49.4% in the MMT+P and 26.3% in the M

253 s reports of stomach upset and headache, yet dropout rates were comparable between groups.

254 Dropout rates were highest in the first 12 to 18 months

255 High dropout rates, dosing inequalities, small sample sizes,

256 randomized trial of antipsychotics with high dropout rates.

257 ll efficacy (primary outcome); responder and dropout rates; positive, negative, and depressive sympto

258 between good adherence, poor adherence, and dropout regarding level of understanding of the treatmen

259 n and combines boosting, bagging, and strong dropout regularization.

260 The flexible dropout-regularized combination approach is able to prod

261 We apply this dropout-regularized combination approach to two clinical

262 The dropout-regularized combination method also generates an

263 ple data set (for example, because of animal dropout), repeated-measures analysis of covariance may f

264 ch causes poor compliance and high treatment dropout, resulting in the development of drug-resistant

265 d expression data, (ii) a fast and efficient dropouts-saving expansion strategy for functional gene m

266 endent screens, BioID and an Epigenome shRNA dropout screen, to define ZEB1 interactors that are crit

267 s similar performance to Cas9 in genome-wide dropout screens but at greatly reduced library size, whi

268 rmed whole-genome small hairpin RNA (shRNA) "dropout screens" on 77 breast cancer cell lines.

269 multiple replicates with different rates of dropout, sporadic dropins, different amounts of DNA from

270 behavioral therapy was associated with fewer dropouts than pill placebo or medications.

271 fraction of missing data reported as zeros (dropouts) than traditional bulk RNA-seq, and unsupervise

272 ity analyses showed that, due to substantial dropout, these may be overestimated by <=1.2 kg, whereas

273 ing (outcome), co-interventions, compliance, dropouts, timing, and intention to treat.

274 ies with variable surveillance protocols and dropout to active treatment.

275 e-grained node dropout and fine-grained edge dropout to address the issue that standard dropout in va

276 es on intrinsic dynamic noise and eigenvalue dropout to find ground states more efficiently.

277 y apparent bias due to the effects of allele dropout typical of RAD data.

278 orthopaedic care group; the main reason for dropout was death.

279 Risk of dropout was higher for patients beyond Milan, but within

280 Dropout was lower in the schema therapy and clarificatio

281 Waitlist dropout was observed in 18.4% at a median of 11.3 months

282 The decline in waitlist mortality/dropout was only significant in the high Model for End-S

283 Survival of 148 individuals (58 died, 15 dropouts) was well described by an exponentially declini

284 om exceeding 50% and poor outcomes following dropout, we must explore innovative strategies for enhan

285 To account for dropouts, we applied a pattern-mixture model.

286 the impact from overdispersion and excessive dropouts, we propose to apply a robust model, quantile r

287 The most frequent reasons for dropout were adverse events or intercurrent illness: 27

288 ebo and active groups as well as reasons for dropout were also assessed.

289 Predictors of death after dropout were assessed by multivariable Cox regression.

290 I, 0.38-0.98) and undergoing treatment after dropout were protective factors (HR, 0.32; 95% CI, 0.21-

291 Dropouts were analysed in regards to allergen, formulati

292 Dropouts were similar between groups, and compliance and

293 single-cell RNA-seq analysis challenging is dropouts, where the data only captures a small fraction

294 dinal studies are complicated by participant dropout, which could be related to the presence of psych

295 The hazard of dropout will increase from 41% to 46% nationally.

296 oop between lamellar disruption and cellular dropout with GAG production and lost medial collagen tha

297 ical trials involving 10 to 120 patients (no dropouts) with early to moderately advanced prion diseas

298 The cumulative probability of dropout within 1 year and 3 years was 53.5% and 80.0%, r

299 phy demonstrated variable areas of capillary dropout within the superficial and deep retinal capillar

300 Submacular CC dropout without retinal pigment eipthelial (RPE) loss wa