ライフサイエンスコーパス: selection bias

1 cumstances since the MLR models suffer from "selection bias".

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

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

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

5 of immediate treatment because of potential selection bias.

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

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

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

9 Theoretically, such a procedure produces a selection bias.

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

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

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

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

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

15 nstitution, and are subjected to significant selection bias.

16 This effect cannot be explained by selection bias.

17 evious reinterventions) was used to minimize selection bias.

18 e have invested substantial effort to reduce selection bias.

19 unding, unmeasured comorbidity, or treatment selection bias.

20 5 preoperative risk variables to correct for selection bias.

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

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

23 ditioning on a collider generally results in selection bias.

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

25 Stable rates of testing ruled out selection bias.

26 ability of treatment to adjust for treatment selection bias.

27 the literature are affected by a significant selection bias.

28 with potential for residual confounding and selection bias.

29 Observational studies are subject to selection bias.

30 olations in assumptions necessary to correct selection bias.

31 variable to adjust for potential prehospital selection bias.

32 used to minimize the influence of treatment selection bias.

33 logistic regression to control for potential selection bias.

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

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

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

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

38 n of the transgene, and is therefore free of selection bias.

39 ive risk factors to correct for and minimize selection bias.

40 y people who died in a hospital suffers from selection bias.

41 r treatment groups was performed to minimize selection bias.

42 terone may, however, suffer from inadvertent selection bias.

43 inferences have been hampered by recall and selection bias.

44 diagnostic accuracy, case ascertainment, and selection bias.

45 such populations might have been affected by selection bias.

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

47 voids the perfect balance that can result in selection bias.

48 ariable Cox regression to minimize treatment selection bias.

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

50 adjust for baseline covariates and potential selection bias.

51 sed to account for potential confounding and selection bias.

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

53 ated variable and robust against information/selection bias.

54 s was observational, introducing significant selection bias.

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

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

57 sion to critical care without this treatment selection bias.

58 2, excluding trial participants, to minimize selection bias.

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

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

61 using composite outcomes to circumvent these selection biases.

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

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

64 es from observational studies with treatment selection biases.

65 iological samples with low levels of subject selection biases.

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

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

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

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

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

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

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

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

74 include interviewer administration or risk a selection bias against subjects with older age, minority

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

76 for RDD indicate a substantial potential for selection bias and a need to seek alternative sources of

77 This will obviate the unavoidable selection bias and allocation bias intrinsic to postrand

78 quantitatively analyze because of unintended selection bias and allocation bias.

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

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

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

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

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

84 data cannot completely adjust for potential selection bias and confounding, these results must be va

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

86 onfounding by unmeasured extraneous factors, selection bias and differential misclassification of exp

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

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

89 urgery (CABG) mortality might result in case selection bias and in denial of care to or out migration

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

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

92 using study designs that minimize potential selection bias and maximize the quality of exposure asse

93 udies were limited by small sample sizes and selection bias and none compared the diagnostic performa

94 rt of inclusion/exclusion criteria, avoiding selection bias and permitting fair comparisons between p

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

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

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

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

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

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

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

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

103 were retrospective, potentially affected by selection bias, and based on radical prostatectomy sampl

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

105 cuum, and 11-gauge vacuum devices, with mild selection bias, and for each lesion, biopsy was performe

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

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

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

109 ertainty due to biases, such as confounding, selection bias, and measurement error.

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

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

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

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

114 subjects and gives careful consideration of selection bias; and employment of multivariate data mode

115 usual care, making it impossible to exclude selection bias as an explanation for the results.

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

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

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

119 However, selection bias associated with case ascertainment, and d

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

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

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

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

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

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

126 Observations are subject to patient selection biases but are useful for generating hypothesi

127 series may merely reflect a healthy patient selection bias, but is also consistent with an antitumor

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

129 n and improved survival are causal or due to selection bias by indication, clinical trials are warran

130 tal use differed by 36% after adjustment for selection bias by means of the two-stage technique.

131 The data presented here suggest that selection bias can account for at least some of the obse

132 Selection bias can result from an inaccurate sampling fr

133 Although selection bias cannot be excluded, these findings provid

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

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

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

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

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

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

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

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

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

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

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

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

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

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

148 Potential self-selection bias due to nonconsenting patients.

149 ve limited long-term follow-up and potential selection bias, early results suggest that toxicity, cos

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

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

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

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

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

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

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

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

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

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

160 We took account of potential selection bias from missing data and recruitment of new

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

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

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

164 Selection bias, however, confounds interpretation of the

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

166 indings for most other variables may reflect selection bias in controls.

167 chnique can be used to control for potential selection bias in dental research when randomization is

168 initions, residual confounding, or potential selection bias in different studies.

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

170 ate the use of this technique to control for selection bias in examining the effects of the The Suppl

171 Selection bias in favor of GTP as an initiating nucleoti

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

173 e methods can deal with both confounding and selection bias in genetic-association studies, making fa

174 Concerns about selection bias in observational studies can be mitigated

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

176 that this latter effect was due to clinical selection bias in our sample.

177 In order to adjust for potential selection bias in outpatient treatment, propensity score

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

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

180 The BLogReg algorithm is also free from selection bias in performance estimation, a common pitfa

181 f fear of genetic testing can be alleviated, selection bias in research could be reduced.

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

183 2002 and 2008, and controlling for nonrandom selection bias in technology adoption, we show that Bt h

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

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

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

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

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

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

190 Selection bias in the odds ratio occurs when participati

191 ubstances under study, revealing a potential selection bias in the reporting of research results.

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

193 two-stage technique was used to control for selection bias in WIC participation, the potentially end

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

195 ity analysis was done to adjust for presumed selection biases in the prescription of lipid-lowering a

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

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

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

199 With respect to internal validity, selection bias, information bias, and confounding are pr

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

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

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

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

204 Selection bias is a common concern in epidemiologic stud

205 However, selection bias is evident and further investigation is r

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

207 ized studies so that the impact of treatment selection bias is minimized.

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

209 taken to eliminate or minimize the effect of selection bias, it should be noted that patients with st

210 Possible selection bias limits any conclusions about relative eff

211 A selection bias limits cross-sectional studies, since pre

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

213 General results for understanding when selection bias may affect studies involving gene-environ

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

215 een case-control studies in which recall and selection bias may influence the results.

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

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

218 Genotype selection bias may limit inferences from these studies.

219 Selection bias may play an important role in choosing th

220 Self-selection bias may threaten the internal validity of epi

221 This phenomenon, which we call selection bias, may explain the perceived non-random dis

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

223 Evidence selection bias occurs when a systematic review does not

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

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

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

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

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 ndergoing CABG, computer-matched to minimize selection bias, off-pump surgery led to decreased mortal

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

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 re attributable to unmeasured confounders or selection biases, or are manifest across a range of SDB

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

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

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

246 Using nested cross-validation to avoid selection bias, performance estimation for SLogReg on th

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 To minimize selection bias, propensity score to undergo valve surger

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

252 conflict resolution situations, in which the selection bias puts the irrelevant information in the pr

253 Although there could be selection bias regarding referral of patients, our data

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

255 d controls does not appear to be affected by selection bias related to community characteristics.

256 Selection bias related to noncontact could not be entire

257 In an attempt to further control for selection bias related to the choice of therapy, multiva

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

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

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

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

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

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

264 Selection bias stems from an absence of comparability be

265 of the three main types of systematic error: selection bias (test-referral bias, spectrum bias), misc

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

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

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

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

270 ndicating that both molecular mechanisms and selection biased the D segment usage.

271 studies provide direct evidence that thymic selection biases the naive peripheral T cell repertoire

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

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

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

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

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

277 ironment interactions will not be subject to selection bias under the assumption that genotype does n

278 used to adjust for measured confounding and selection bias under the four assumptions of consistency

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

280 rminal-catastrophe rate that is free of such selection bias, using calculations based on the relative

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

282 Significant selection bias was detected in all studies included for

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

284 Selection bias was evident because individuals had to un

285 Selection bias was present for cancer diagnosis.

286 at WIC participation was not random and that selection bias was present.

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

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

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

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

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

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

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

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

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 al components needed to resolve the negative selection bias, which attentional modulation can be addr

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