ライフサイエンスコーパス: selection bias

1 ested cross-validation considering the model selection bias).

2 used as his or her own control to eliminate selection bias.

3 r treatment groups was performed to minimize selection bias.

4 ; there is evidence of a "healthy volunteer" selection bias.

5 ariable Cox regression to minimize treatment selection bias.

6 years from surgery, even when accounting for selection bias.

7 m which cases arose and the least subject to selection bias.

8 adjust for baseline covariates and potential selection bias.

9 ses quantified conditions that might lead to selection bias.

10 sed to account for potential confounding and selection bias.

11 procedure which, leads to the potential for selection bias.

12 ated variable and robust against information/selection bias.

13 s was observational, introducing significant selection bias.

14 ty scores were used to control for treatment selection bias.

15 sted that the results are robust to possible selection bias.

16 sion to critical care without this treatment selection bias.

17 2, excluding trial participants, to minimize selection bias.

18 ing prediction model and corrected for model-selection bias.

19 ed hemoglobin A1c reduction is likely due to selection bias.

20 analyses were used to account for treatment selection bias.

21 r the reporting of NIHSS data was subject to selection bias.

22 of immediate treatment because of potential selection bias.

23 core-based 1:1 matching to reduce intergroup selection bias.

24 y-weighted treatment estimates to adjust for selection bias.

25 consider the first of these 2 risks leads to selection bias.

26 Theoretically, such a procedure produces a selection bias.

27 nd has been claimed to be an artifact due to selection bias.

28 rate cohort mortality and age-related survey selection bias.

29 fter using a propensity score to correct for selection bias.

30 ing by the odds methods to reduce intergroup selection bias.

31 o the LES initiative, which ensured avoiding selection bias.

32 nstitution, and are subjected to significant selection bias.

33 This effect cannot be explained by selection bias.

34 analyses were used to account for treatment selection bias.

35 e have invested substantial effort to reduce selection bias.

36 5 preoperative risk variables to correct for selection bias.

37 ity-weighting adjustment to reduce treatment-selection bias.

38 onfounded by PET-induced stage migration and selection bias.

39 trimming was used to mitigate the effects of selection bias.

40 Stable rates of testing ruled out selection bias.

41 ability of treatment to adjust for treatment selection bias.

42 from the outcome analyses to avoid potential selection bias.

43 the literature are affected by a significant selection bias.

44 with potential for residual confounding and selection bias.

45 Observational studies are subject to selection bias.

46 olations in assumptions necessary to correct selection bias.

47 weighting was used to account for treatment selection bias.

48 variable to adjust for potential prehospital selection bias.

49 used to minimize the influence of treatment selection bias.

50 logistic regression to control for potential selection bias.

51 in Minneapolis and may have been subject to selection bias.

52 ulation, these findings may be the result of selection bias.

53 a larger population, presenting the risk of selection bias.

54 AG outcomes with enhanced matching to reduce selection bias.

55 uch as stochastic variation, confounding, or selection bias.

56 he first comparison to account for treatment selection bias.

57 inverse probability weighting to account for selection bias.

58 tudies remain uncertain because of potential selection bias.

59 BG after using propensity matching to reduce selection bias.

60 es in procedure-related complications and/or selection bias.

61 case series with their potential confounding selection bias.

62 e matching was used to account for treatment selection bias.

63 t literature is heterogeneous and at risk of selection bias.

64 whole or broad regions of organoids to avoid selection bias.

65 We hypothesized that this could be due to a selection bias.

66 using 38 baseline characteristics to reduce selection bias.

67 y, false-positive rates were not affected by selection bias.

68 nt therapy was used to account for potential selection bias.

69 evious reinterventions) was used to minimize selection bias.

70 unding, unmeasured comorbidity, or treatment selection bias.

71 ditioning on a collider generally results in selection bias.

72 orts had been made to remove confounding and selection biases.

73 using composite outcomes to circumvent these selection biases.

74 ally identical to that of several well-known selection biases.

75 mates because of insufficient sample size or selection biases.

76 ed matching analysis was used to account for selection biases.

77 es from observational studies with treatment selection biases.

78 To minimize the possibility of treatment selection bias, 1:1 nearest neighbor propensity score ma

79 dies were at high or unclear risk of patient selection bias (74%) or index test bias (67%).

80 To adjust for selection bias, a logistic regression model was created

81 To reduce any selection bias, a propensity score analysis was applied.

82 instrumental variable methods to account for selection bias, actual Medicare payments after each proc

83 idence interval: 1.26, 1.73), and the simple selection bias-adjusted odds ratio was 1.26 (95% confide

84 No evidence was found for an overall selection bias against acquiring an N-glycosylation site

85 d out-of-frame IgH rearrangements revealed a selection bias against long HCDR3 loops, suggesting thes

86 und that OT inhalation selectively reduced a selection bias against negatively valenced expressions.

87 The IPSWs can then be used to adjust for selection bias analytically.

88 In order to minimize selection bias and account for non-repairs, subjects in

89 nd high signal-to-noise ratios, without user selection bias and at fast timescales.

90 ilable studies are subject to a high risk of selection bias and clinical heterogeneity.

91 services researchers to limit the effects of selection bias and confounding is discussed.

92 Assuming residual selection bias and confounding were not large, the prese

93 ortional hazards regression, controlling for selection bias and confounding with the propensity score

94 ased survival likely reflects an artifact of selection bias and consequent stage migration.

95 unding factors such as use of RA medication, selection bias and differential RA diagnosis.

96 the relative weight placed on concerns about selection bias and generalizability, as well as pragmati

97 These challenges include selection bias and information bias, which cannot be sol

98 Study limitations included potential selection bias and lack of neonatal-specific data.

99 ion of this study, which may have introduced selection bias and limited the power of the regression a

100 torical controls suggest a potential patient selection bias and may preclude generalizability of resu

101 analyzing results, and (5) the potential for selection bias and other issues inherent to being unblin

102 ngeability due to unmeasured confounding and selection bias and potential violations of the consisten

103 Selection bias and residual confounding are potential li

104 s of patients but the admitted potential for selection bias and residual confounding, DES use was ass

105 However, such studies are prone to selection bias and residual confounding.

106 iables (SSV) model, also taking into account selection bias and stroke subtype.

107 Because of the potential for selection bias and the absence of a control group treate

108 t nonspecific serious outcomes suffered from selection bias and the lack of laboratory confirmation f

109 The retrospective trials are clouded by selection bias and the prospective studies are designed

110 esults were robust to corrections for sample-selection bias and to the exclusion of observations with

111 However, methods for addressing selection bias and unmeasured confounding are less devel

112 eneral bounding formulas for bias, including selection bias and unmeasured confounding.

113 igation is warranted on account of potential selection bias and unmeasured confounding.

114 ck of control groups, patient heterogeneity, selection bias, and choice of end points.

115 Examples include assessment for confounding, selection bias, and information bias.

116 ce, haemoglobin concentration, RBC exposure, selection bias, and information to guide design and econ

117 lidated exposure measurement error, measured selection bias, and measured time-fixed and time-varying

118 These threats include confounding, selection bias, and measurement error in either the expo

119 tions of the study include the potential for selection bias, and possible residual confounding in mul

120 ample sizes, longitudinal follow-up, lack of selection bias, and potential for complex, multivariable

121 biggest limitations were small sample size, selection bias, and short follow-ups.

122 with transfusion may have been influenced by selection bias, and they highlight the need for randomiz

123 of common inbred strains reflects historical selection biases, and existing recombinant inbred panels

124 ment, survival bias and competing risks, and selection bias arising from sample selection.

125 to the general population as there may be a selection bias as iron studies were done in a subset of

126 h mutations arise in bacteria with as little selection bias as possible [11, 12].

127 We account for confounding and selection bias as well as generalizability by standardiz

128 Nevertheless, instability and variable selection bias, as well as overfitting, are well-known p

129 use of PS-weighted methods reduced treatment selection bias at baseline and allowed valid assessment

130 ine the factors responsible for the observed selection biases at unexpected loci and whether these ar

131 Potential selection bias based on previous rituximab response and

132 This matching strategy is susceptible to a selection bias because inpatients that stay longer in th

133 favorable but such trials were affected by a selection bias because only chemosensitive patients actu

134 ces enormous methodological challenges, with selection bias being near the top of the list.

135 opensity score was calculated to account for selection bias between choice of laparoscopic versus ope

136 e quite modest and that there is evidence of selection bias between persons.

137 The results may be inflated by selection bias, bias in diet reporting, or residual conf

138 We estimated the magnitude of selection bias by calculating values of 13 health indica

139 Selection bias can result from an inaccurate sampling fr

140 Although selection bias cannot be excluded, these findings provid

141 pretations of these results, and the role of selection bias cannot entirely be dismissed on the basis

142 hey corrected the observed hazard ratios for selection bias caused by what they postulated was the no

143 gery has been previously studied, but cohort selection bias, completeness of follow-up, and collectio

144 Concerns about reverse causality and selection bias complicate the interpretation of studies

145 A form of selection bias, composition bias, arises dynamically at

146 notwithstanding the possibility of residual selection bias, conversion to treatment with nocturnal h

147 We also estimated a selection bias-corrected population mean NIHSS score of

148 ate of participation was low and, therefore, selection bias could have exaggerated these effects.

149 mild biliary pancreatitis appears safe, but selection bias could not be excluded.

150 This selection bias creates the well-known crossover paradox,

151 e older cancer population, difficulties with selection bias depending on inclusion criteria, physicia

152 tistically high-powered study with minimized selection bias, DNMT3A(mut) represent a frequent genetic

153 reached 60% in 85% of African countries, so selection bias does not appear to invalidate the measure

154 We examined possible selection bias due to 1) sampling decisions and 2) selec

155 ch case-control study, was designed to avoid selection bias due to differential participation and mis

156 ack of data on compliance, and potential for selection bias due to incomplete follow-up.

157 The authors assessed the possibility of selection bias due to less-than-100% enrollment of eligi

158 assumptions about unmeasured confounding or selection bias due to missing data (e.g., dropout).

159 did a secondary analysis that corrected for selection bias due to non-participation.

160 Potential self-selection bias due to nonconsenting patients.

161 : 0.73, 0.95), indicating little evidence of selection bias due to sampling decisions, and was simila

162 Selection bias due to voluntary participation and a rela

163 rimary limitation of this study is potential selection bias during the follow-up due to missing data

164 ploid) spores has the potential to introduce selection bias, especially when analyzing mutants with e

165 ional propensity scores were used to address selection bias for a retrospective cohort study of child

166 nomenon through principal stratification and selection bias for PEG treatment through generalized pro

167 l mucosal environments both imposed a strong selection bias for SIVsmE660 variants carrying I-A-K-N t

168 balance to describe measured confounding and selection bias for time-varying and other multivariate e

169 f viruses in both partners and demonstrate a selection bias for transmission of residues that are pre

170 pensity score analysis to minimize potential selection biases for allocation of treatment.

171 esidual confounders for illness severity and selection biases for CCM might exist that were inadequat

172 dings, without human preprocessing, enabling selection-bias-free estimates of oscillation rates.

173 sychological stress and contact lens use and selection bias from dropout.

174 e due to the laparoscopic approach itself or selection bias from healthier patients undergoing the le

175 However, when adjusted for the selection bias from monosomy 7, mutational status had no

176 to differentiate vaccine effectiveness from selection bias have been problematic.

177 ns can lead to faster study completion, less selection bias, higher-powered data, and enhanced subgro

178 To avoid selection biases, however, comparisons ideally involve a

179 We demonstrate modest, but important, selection bias in documented NIHSS data, which are missi

180 The need to address potential sample selection bias in future electronic health record-based

181 results highlight a critical need to address selection bias in integrative analysis and to use cautio

182 l, and potential sources and implications of selection bias in mobile phone data.

183 Concerns about selection bias in observational studies can be mitigated

184 udy is to quantify and correct for potential selection bias in observed NIHSS data.

185 However, there was likely a significant selection bias in patients chosen for surgical or medica

186 nts with low BMI, increased drug delivery or selection bias in patients with high BMI, and potential

187 To minimize selection bias in patients with limited life expectancy,

188 ffer methods for enrollment feedback to curb selection bias in recruitment.

189 bibliographical databases to reduce evidence selection bias in systematic reviews.

190 2002 and 2008, and controlling for nonrandom selection bias in technology adoption, we show that Bt h

191 The authors evaluated the role of selection bias in the 1999 Canadian case-control study o

192 marily by 1 clinical trial, possible patient selection bias in the ablation group, lack of patient-le

193 ant outcomes, though there is a high risk of selection bias in the available evidence.

194 To overcome potential selection bias in the data included in the IEDB, a strat

195 tations include the small size and potential selection bias in the discovery cohort.

196 error terms, which quantifies the degree of selection bias in the documentation of NIHSS.

197 y and attrition, we found little evidence of selection bias in the estimation of racial differences f

198 In per-protocol analyses, adjusting for self-selection bias in the intervention group, incidence of c

199 ions of the study are that we cannot exclude selection bias in the study design or socially desirable

200 ensity for medication use, which may reflect selection bias in treatment allocation in survival model

201 trials, as well as residual confounding from selection biases in observational studies.

202 y low sample size (in randomized trials) and selection bias (in observational studies).

203 "big-tree selection bias") in the dataset.

204 dy was to determine whether the magnitude of selection bias incurred by measuring child survival inte

205 lable data, while limited and complicated by selection bias, indicate that exposure to RBT represents

206 most exclusively of case series with risk of selection bias, indirect patient populations, and imprec

207 is no longer appropriate due to the gene-set selection bias induced during the construction of these

208 er-stress model and raises speculation about selection bias influencing these findings.

209 studies need to consider 3 types of biases: selection bias, information bias, and confounding bias.

210 in observational studies, the potential for selection bias inherent in the test-negative design brin

211 he limitations of missing data and potential selection biases inherent in registry and administrative

212 the context of small sample size and strong selection bias, inverse probability-of-censoring weights

213 In most empirical settings, selection bias is expected to have a limited impact on g

214 Here, an approach that minimizes selection biases is used to isolate a large cohort of br

215 tial challenges with this technology include selection bias, low retention rates, reporting bias, and

216 Heterogeneity within the data and inherent selection bias make inferences on effective beta-lactam

217 DD, we acknowledge the potential impact that selection bias may have had on our results because of po

218 sample was small; follow-up was limited; and selection bias may have occurred.

219 Despite significant success, selection bias may lead to inflated expectations of the

220 s revascularization strategies, but inherent selection bias may limit accuracy.

221 ge of mechanistic biases (e.g., confounding, selection bias, measurement error) to cover distortions

222 Evidence selection bias occurs when a systematic review does not

223 se probability weighting used to control for selection bias (odds ratio [OR] 0.74, 95% CI 0.66-0.83).

224 lton-Watson epidemic model combined with the selection bias of observing only large diffusions suffic

225 ells in the glands with a sexually dimorphic selection bias of TCR repertoires.

226 usly, given the nonrandomized nature and the selection bias of the study.

227 ng-term survival than donor factors; and (3) selection bias of transplanting only relatively healthy

228 ngth distribution, paired distance, and base selection bias of vsiRNA sequences reflect different pla

229 Thus, selection biases of which data we observe can radically

230 on-based study demonstrates the influence of selection bias on cost estimates in comparative effectiv

231 emble-based approach to minimize the feature selection bias on imputation.

232 ewer recommendations minimizes the effect of selection bias on publication decisions.

233 s heterogeneity, we quantified the impact of selection bias on the magnitude of ES estimates.

234 simulation studies to explore the impact of selection bias on the marginal hazard ratio for risk of

235 n of bottom-up contrast and top-down feature-selection biases on stimulus processing.

236 lyses were performed to adjust for potential selection bias, one using propensity score matching and

237 findings, we explored 2 potential sources of selection bias: one induced by self-referral of healthy

238 ot reach primary end points but may have had selection bias or been underpowered.

239 onal studies but many have not corrected for selection bias or independent predictors of outcome.

240 surgical resection of MCC may be a result of selection bias or unmeasured factors and not radiation t

241 through collider stratification bias (i.e., selection bias) or bias due to conditioning on an interm

242 Currently, the magnitude of any selection bias, particularly for subsequent time-to-even

243 To minimize selection bias, patients were propensity matched into 71

244 To control for selection bias, patients who received postoperative ther

245 To test for selection bias, payments for individuals who used the pl

246 bias, such as transmission ratio distortion, selection bias, population stratification, dynastic effe

247 es linked ISA to stent thrombosis, potential selection bias precluded definitive conclusions.

248 Selection bias precludes conclusions about whether use o

249 of method could suffer from over-fitting and selection bias problems.

250 tal inflammation, and recipient gender, this selection bias provides an overall transmission advantag

251 vance of miRNA* and the complexity of strand selection bias regulation.

252 Selection bias related to noncontact could not be entire

253 emerging analytic methods in the context of selection bias represents a noteworthy and pervasive cha

254 acteria in food choice assays, and that this selection bias requires bacterially produced tyramine an

255 is a single snapshot in time, is subject to selection bias resulting from tumor heterogeneity, and c

256 methodologic limitations including sampling selection bias, reverse causality, and collider bias hav

257 Heckman model found modest, but significant, selection bias (rho=0.19; 95% confidence interval: 0.09,

258 strong, which may be explained by potential selection bias, sample size issues, or a difference in u

259 Trial design features such as patient selection, bias, sample size calculation, selection of s

260 imitation: Important study heterogeneity and selection bias; scant evidence in primary and urgent car

261 cted data, in duplicate, related to items of selection bias (sequence generation, allocation concealm

262 s are based mainly on relatively few, small, selection-biased studies at experienced centers, and thu

263 In this commentary I discusses the selection bias that may arise in longitudinal analysis o

264 The latter introduces possible selection bias that may undermine the generalizability o

265 The ensuing selection bias that occurs due to this restriction has g

266 duce the propagation of byproducts and avoid selection bias that result from differences in PCR effic

267 tality in each cluster, we also adjusted for selection bias that resulted from the vaccination status

268 ssion models controlling for confounding and selection bias, the 30-d readmission rate was 47% lower

269 To differentiate vaccine effects from selection bias, the authors used logistic regression wit

270 opensity scores matching to reduce treatment selection bias, the study shows that PreRASi is associat

271 l research in this area, including extensive selection bias, the use of noncompositionally robust mea

272 Because of possible selection biases, these results must be interpreted with

273 But the success relies on the reduction of selection bias through methods such as propensity score

274 ty in results of previous studies was due to selection bias toward the null from use of referred cont

275 st pronounced in genes that are under strong selection biased towards females.

276 in the literature, along with its associated selection bias, under multiple mechanisms for right cens

277 ; rates of discovery were comparable, making selection bias unlikely and the results generalisable to

278 as an exposure and as a proposed instrument: selection bias, unmeasured confounding, lack of sufficie

279 ficial censoring with correction for induced selection bias using inverse probability-of-censoring we

280 iverbed sediment aliquots that avoids visual selection bias using state-of-the art automated micro-Fo

281 Evidence of selection bias was also identified using hospital-level

282 The extent of selection bias was determined by the magnitudes of genet

283 Selection bias was evident because individuals had to un

284 Selection bias was present for cancer diagnosis.

285 rition and reporting bias were high, whereas selection bias was unclear due to inadequate reporting.

286 The risk for bias, especially selection bias, was high.

287 To account for treatment selection bias, we additionally used a weighted Cox mode

288 Extreme selection bias, we are told, will not harm internal vali

289 To deal with potential selection bias, we designed an intent-to-treat study, wh

290 To overcome selection bias, we studied only deaths caused by the ind

291 We concluded that both types of selection bias were likely to have occurred in this stud

292 ications were identified but publication and selection biases were noted.

293 erefore is not a collider-can also result in selection bias when 1) the exposure has a non-null effec

294 but our results suggested a vulnerability to selection bias when there is extensive censoring.

295 studies of birth defects might be subject to selection bias when there is incomplete ascertainment of

296 Results may be affected by selection biases where less aggressive regimens are offe

297 s conducted in routine practice and had some selection bias, which is evidenced by the relatively lar

298 aneously, rapidly, economically, and without selection bias, while coregistering the genetic informat

299 this paper we describe the structure of this selection bias with examples drawn from commonly propose

300 ls are central to CER because of the lack of selection bias, with the recent development of adaptive