ライフサイエンスコーパス: logistic model

1 adjusting for confounders in a multivariable logistic model).

2 e I diastolic dysfunction (P < 0.050 for all logistic models).

3 the basis of beta estimates derived from the logistic model.

4 uted, according to a multivariate regression logistic model.

5 uses was calculated by fitting the data to a logistic model.

6 robust, Bayesian hierarchical five-parameter logistic model.

7 = 66) were analyzed by using a multivariate logistic model.

8 contributor to the explanatory ability of a logistic model.

9 as a discrete trait in a class C regressive logistic model.

10 model different from the conventional linear logistic model.

11 er the Gompertz model, it declines under the logistic model.

12 s for the mediator variable, but rather to a logistic model.

13 onse kinetic model and an empirical modified logistic model.

14 mined cause of death were identified using a logistic model.

15 motic leakage were determined using a binary logistic model.

16 mum likelihood estimator for parameters in a logistic model.

17 both symptoms was evaluated by a multinomial logistic model.

18 nt selection were studied in a mixed-effects logistic model.

19 ies using 2-level hierarchical multivariable logistic models.

20 year were compared using the chi(2) test and logistic models.

21 y hospital or patient characteristics in our logistic models.

22 ssessed by using univariate and multivariate logistic models.

23 e likelihood of becoming newly sensitized in logistic models.

24 hours, 24 hours, and 30 days in multivariate logistic models.

25 assified as users or nonusers in multinomial logistic models.

26 cases and 1,385 controls) using multivariate logistic models.

27 ure and mortality in Cox proportional hazard logistic models.

28 tics was examined by using repeated-measures logistic models.

29 ithin 1 year were assessed with hierarchical logistic models.

30 traditional stratified analysis and standard logistic models.

31 l analysis based on Classification trees and logistic models.

32 cancer (n=545) was observed in multivariate logistic models.

33 with D/R seropositivity were assessed using logistic models.

34 ns, adjusted odds ratios were estimated from logistic models.

35 erature review were included in multivariate logistic models.

36 Scale) were also introduced in multivariable logistic models.

37 ation were determined with backward stepwise logistic modeling.

38 ed in-hospital mortality using multivariable logistic modeling.

39 , age at menarche, and parity in conditional logistic model].

40 tcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models in

41 A three-parameter logistic model (3PL) for polytomous response was calcula

42 In the multivariate logistic model, a history of vomiting, lower platelet co

43 d HCC development was assessed using ordinal logistic models according to five periods of time to dia

44 In 75 patients, multivariable logistic modeling accurately identified the 40 patients

45 try LDL-C was associated with SVR in a joint logistic model adjusted for HCV genotype, race, and prio

46 ere assigned -1 day in a bayesian cumulative logistic model adjusted for treatment location, age, sex

47 lated to mild-to-moderate persistent asthma (logistic model adjusted odds ratio = 1.55 (95% CI = 1.26

48 of type 2 diabetes were obtained from pooled logistic models adjusted for nondietary and dietary cova

49 Multivariable logistic models adjusted for possible confounders (mater

50 t predictor of mortality in the multivariate logistic model (adjusted black/white OR 1.29 [1.21, 1.38

51 Among people with diabetes, in multivariable logistic models adjusting for race, sex, education, CVD

52 In a multivariate logistic model, adjusting for differences in patient and

53 In simple logistic models, adolescent anxiety or depressive disord

54 were analyzed by a conditional multivariate logistic model after matching on a propensity score of b

55 A related but more complex three-parameter logistic model allows for subsequent leveling off in mor

56 A logistic model also confirmed that the Nut-Catheter Angl

57 Kendall Tau-B was 0.639 for the logistic model and 0.582 for the Ocular Trauma Score.

58 re surgery was created using a multivariable logistic model and a greedy matching algorithm with a 1:

59 analyzed under the Bayesian paradigm, using logistic model and areas under the receiver operating ch

60 's performance was compared with that of the logistic model and conventional eGFR-based assessment.

61 om cases and controls were used to develop a logistic model and deterioration index to predict patien

62 bles (not growth phase) for inclusion in the logistic model and nomogram.

63 A logistic model and receiver operating characteristics an

64 tcome, a number of proposed estimators use a logistic model and rely on specific assumptions or appro

65 Multivariable analysis using a logistic model and the hospital as a random variable was

66 of prevalent tuberculosis with mixed-effects logistic models and estimated adjusted hazard ratios (HR

67 endotoxin concentrations were analyzed using logistic models and forward stepwise linear regression.

68 Multilevel logistic models and intraclass correlation coefficient w

69 Multivariate hierarchical logistic models and stepwise linear regression models ad

70 es were tested using multivariate linear and logistic models and structural equation models.

71 sessed using generalized estimating equation logistic models and with asthma hospital admissions usin

72 erent ages with ALS risk using unconditional logistic models and with survival after ALS diagnosis us

73 re assessed by including a product term in a logistic model, and additive interactions were assessed

74 y outcomes were modeled using a hierarchical logistic model, and count outcomes were modeled using hi

75 score was then given to each patient using a logistic model, and propensity matching was performed us

76 e probit structural equation models, 2-stage logistic models, and generalized method of moments estim

77 Following a second logistic model applied to the matched groups to adjust f

78 eek 52 corticosteroid-free remission (n=386, logistic model area under the curve [AUC] 0.70, 95% CI 0

79 Adjusted logistic models assessed the associations of MPO with ma

80 The Bayesian logistic model (AUC = 0.79) outperformed the Framingham

81 and propensity scores were calculated with a logistic model based on patient disease and sociodemogra

82 When comparing multilevel with simple logistic models, beta values were 4 to 5 times lower and

83 In the multivariate logistic model, BRCA1 status (odds ratio [OR] = 3.16; 95

84 d in the specific context of odds ratios and logistic modeling but is more widely applicable.

85 tive to the number of variables p, fitting a logistic model by the method of maximum likelihood produ

86 A crucial mathematical distortion under the logistics model, called "absence of collapsibility," is

87 More complex logistic models can be used for choices between good bun

88 ation-averaged effects, while random-effects logistic models can be used to estimate subject-specific

89 Finally, logistic models can quantify the explanatory power of ne

90 ore, gender, and AF types in a multivariable logistic model, Chicken Wing morphology was found to be

91 rm birth risk differences were computed from logistic model coefficients, comparing neighborhoods in

92 Producing a logistic model combining mean sphere, corneal power, Gul

93 Multiple logistic models compared HIV outcomes for participants f

94 In logistic models, compared with patients receiving neithe

95 e results from stratified analysis, standard logistic models, conditional logistic models, the GEE mo

96 Logistic model confirmed that maintenance of higher oxyg

97 A logistic model confirmed the joint significance of geogr

98 re assessed independently in a multivariable logistic model containing the following variables: gende

99 TH and outcomes, we created 2 multivariable logistic models controlling for confounders.

100 tion with unplanned pregnancy was studied in logistic models controlling for demographic and socioeco

101 Logistic models correctly predicted a blood lead elevati

102 In the final multivariate logistic model, cumulative hours of day care attendance

103 In the stepwise logistic model, delayed admission, advancing age, higher

104 Logistic modeling demonstrated that among participants t

105 In a multivariable logistic model, depression (odds ratio [OR] 2.16, 95% co

106 A three-covariate multinomial logistic model derived from a triple-phase 4D CT scan ca

107 A principal components logistic model discriminated healthy people from patient

108 on step, however, must be added to penalized logistic modeling due to a large number of genes and a s

109 Here, I describe and discuss different logistic models, emphasizing the underlying assumptions

110 A pooled logistic model estimated the per-protocol difference bet

111 hospital and OPO random effects, multilevel logistic models estimated that compared with Black patie

112 A logistic model evaluated association between index treat

113 ed differences by age group and by race, and logistic models examined predictors of multiple victims,

114 Multivariable logistic models examined the utility of these markers in

115 er and clinical TNM stage in a multivariable logistic model, factors significantly associated with no

116 The exponential and-more notably-logistic models failed to describe the experimental data

117 the association calculated on the basis of a logistic model fitted with weighted observations.

118 d by comparing the estimated coefficients in logistic models fitted to the data.

119 Logistic model fitting of simple coal workers' pneumocon

120 l is a modification of the Olson conditional logistic model for affected relative pairs.

121 Moreover, the final logistic model for AgP diagnosis included gender (p = 0.

122 In this article, assuming a logistic model for disease risk for different study desi

123 conjunction with a deterministic generalised logistic model for humans-forest interaction and we eval

124 Assuming a proportional odds logistic model for risk of a common outcome, we propose

125 the independent predictors in a multivariate logistic model for the diagnosis of NASH.

126 ty and malignancy was examined by creating a logistic model for the prospectively assessed data set.

127 We developed hierarchical multivariate logistic models for (1) superficial SSI, (2) deep/organ-

128 They developed logistic models for each of 16 health indicators to exam

129 tion data failed to improve physiology-based logistic models for hospital and 1-yr survival (p > .15

130 nd significance of associations, and general logistics modelling for direction and significance of th

131 A mixed-effects logistic model found that use of the TA (odds ratio [OR]

132 porating prior biological knowledge within a logistic modeling framework by using network-level const

133 SN status for inclusion in a multiple binary logistic model from which a nomogram was elaborated.

134 A 48-hour simulation showed that the logistic model had a higher area under the curve than th

135 The modified Logistic model has never been used in food drying areas

136 and dispersal, we show that distance-driven logistic models have strong power to predict dispersal p

137 In a hierarchical logistic model, heart failure (odds ratio, 3.06, 95% con

138 Using a logistic model, heart rate (HR) and mean arterial pressu

139 In the prospective logistic model, high density (odds ratio, 6.6), irregula

140 Multivariable hierarchical logistic models identified predictors of revascularizati

141 prediction, murine studies, and time-course logistic modeling, identified metabolites were screened

142 because of possible misspecification of the logistic model: If the underlying model is linear, the l

143 on for sepsis assessed using a mixed-effects logistic model in a 3-level hierarchical structure based

144 The c-statistic for the multivariable logistic model in our cohort was 0.64 (95 % CI = 0.61-06

145 The final logistic model in T2 for RP included the following items

146 The final logistic model in the final examination included: 1) for

147 ximation I consider a spatial single-species logistic model in which offspring are dispersed across a

148 Potential covariates considered in the logistic model included age, sex, race, history of reope

149 not undergoing bone densitometry in adjusted logistic models included male patient sex and premenopau

150 Multivariable logistic model including serum MIOX, discharge serum cre

151 Logistic models, including both exposures, compared the

152 ve (AUC) and goodness of fit of prespecified logistic models, including pretreatment (eg, age, cancer

153 ated TM by univariate analysis; therefore, a logistic model incorporating these effects was construct

154 In the multivariable logistic model, independent predictors of seroconversion

155 Logistic modeling indicates hepatic SHH expression and p

156 stic mixed models and those from conditional logistic models indicates that there is little or no bia

157 Using a multivariate binary logistic model, log MR-proADM (odds ratio 4.22) and log

158 duration and recency of exposure to ERT in a logistic model may leave the mistaken impression that it

159 Logistic models may be used to quantify a variety of beh

160 Against the predictions of the linear logistic model, neither all-cause nor cardiovascular dea

161 and [Formula: see text] Under the classical logistic model of population growth with linear density

162 the number of expected outcomes based on the logistic model of the French study with observed outcome

163 The logistic model of total disease burden (severe and exten

164 A multivariate logistic model of variables available at the time of arr

165 Multivariate logistic models of early mortality (<72 hrs) and overall

166 The authors fit multivariate logistic models of preterm birth, stratified by neighbor

167 ntrolling splicing, we trained a multinomial logistic model on sets of PTBP1 regulated and unregulate

168 First, we developed linear logistic models on national and regional geographic scal

169 SPAGE fits a genotype-independent logistic model only once across the genome-wide analysis

170 A multivariable logistic model, optimized for assessment of corticostero

171 A multivariable logistic model, optimized for prior corticosteroid use,

172 ral decisions based either on a multivariate logistic model or on the criterion of an FEV1 of less th

173 ors apply constant, linear, exponential, and logistic models or approaches based on socioeconomic var

174 002 and 2018, were computed using multilevel logistic models (overall and by FAS groups).

175 In multivariable logistic models, patients with risk, injury, failure, lo

176 A logistic model performed a conditional multivariate anal

177 onal value of ACT guidance, we analyzed with logistic modeling peri-percutaneous coronary interventio

178 Four multivariate logistic models (phenotype; genotype; phenotype + genoty

179 and random-intercept fixed-slope multilevel logistic models portrayed different structural realities

180 For every 5 mg/dL increase in sTTR levels, a logistic model predicted a 31.6% relative reduction in o

181 An age-, income-, and state-adjusted logistic model predicting mammography use for 2.9 millio

182 Logistic models predicting 30-day readmission were compa

183 A log-logistic model predicts a dose affecting 50% of animals

184 In logistic modeling, previous mammography alone explained

185 rall sensitivity/specificity results for the logistic model produced using the ConeLocationMagnitudeI

186 Logistic models provided the adjusted odds of study end

187 environmental stochasticity, the stochastic logistic model, quantitatively predicts the three macroe

188 omputed on the basis of the relative size of logistic model regression coefficients.

189 Our logistic models reveal two alternative requirements for

190 Multivariable logistic modeling revealed a lower baseline platelet cou

191 Multivariate logistic modeling revealed that older age (> or =0.55 ye

192 A significant interaction in the logistic model showed a differential effect of sport and

193 The logistic model showed good discrimination ability (area

194 The final multivariate logistic model showed that the number of teeth (OR = 1.0

195 The multivariate logistic model showed that the type of leak, namely a hi

196 Mixed-effects logistic modeling showed similar odds of high FT over ti

197 The 2-stage logistic models showed standard errors up to 40% larger

198 s and generalized estimating equations (GEE) logistic models showed that reinfection risk was signifi

199 phPad Prism(R) version 7 five-parameter (5P) logistic model, SigmaPlot(R) version 14.0, Microsoft Exc

200 A logistic model suggested that the interplay between the

201 We propose a logistic model taking quality scores as covariates.

202 In a logistic model that adjusted for age, gender, body mass

203 In a conditional logistic model that adjusted for vehicle (rollover, weig

204 In a logistic model that adjusts for bNAb epitopes and challe

205 In a multivariable mixed-effects logistic model that controlled for all individual- and c

206 e primary analysis was a bayesian cumulative logistic model that included all patients enrolled with

207 ment change was calculated from a multilevel logistic model that included variables assessing clinica

208 plements a latent-variable proportional-odds logistic model that relates inheritance patterns to the

209 propose a latent-variable, proportional-odds logistic model that relates inheritance patterns to the

210 k for senior faculty women were confirmed in logistic models that accounted for a wide range of other

211 In logistic models that included age, body mass index, soci

212 In logistic models that included maternal sociodemographic

213 isks persisted even in the most conservative logistic models that removed the shared effects of comor

214 serologic phenotypes was studied in separate logistic models that were adjusted for covariates.

215 stochastic properties can be obtained from a logistic model, that is, without interactions, even the

216 In a logistic model, the case management effect was limited t

217 In a multiple logistic model, the independent effects of HbA(1c) level

218 lysis, standard logistic models, conditional logistic models, the GEE models, and random-effects mode

219 Logistic models then determined feature selection with b

220 Under logistic models, this biotic rebound should be exponenti

221 g for relatedness, and then used an adjusted logistic model to evaluate the effect size of the varian

222 nalyses were performed using a 2-level mixed logistic model to examine the independent associations a

223 We used a standard logistic model to infer the district-specific yellow fev

224 ses of TL encephalitis and used multivariate logistic modeling to identify radiologic predictors of H

225 Data were fitted to logistic models to derive instantaneous growth and N cap

226 derived for three antibiotics by fitting log-logistic models to end points calculated from minimum in

227 ifferences in bivariate frequencies and used logistic models to examine adjusted associations with 2-

228 L (7.9% of the sample) and used multivariate logistic models to identify independent predictors of hy

229 Constrained logistic models to predict bacterial infection were fit

230 s were developed using multivariable ordinal logistic models to predict the cognitive status using UE

231 The logistic model tree (LMT) method yielded the best result

232 In this paper, we propose a new model of Logistic Model Tree for predicting miRNA-Disease Associa

233 According to the modified logistic model, tryptophan concentration was critical fo

234 point nomogram was constructed employing the logistic model using a weighted point system.

235 Our results show that a Bayesian logistic model using full-information continuous predict

236 ach physiologic variable was included in the logistic model using indicator variables; none was signi

237 A logistic model using pPIB was able to classify 90.5% of

238 tem-scale evaluation, and detailed last-mile logistics modeling using the city of San Francisco as an

239 with willingness to take MDR TPT, a marginal logistic model was fitted using generalized estimating e

240 Finally, interaction under a logistic model was highly significant, as TNF2A carriage

241 A weighted logistic model was optimized to predict vision outcomes.

242 The linear logistic model was rejected by the Framingham data.

243 A logistic model was set up using the same variables.

244 A logistic model was used for the probability of cure, mix

245 A multivariate logistic model was used to adjust for risk factors and d

246 sured at three different visits, and a mixed logistic model was used to assess left ventricular hyper

247 A multivariate logistic model was used to derive the odds ratios of a p

248 A multilevel logistic model was used to estimate the association of c

249 An additive logistic model was used to examine the joint effects of

250 A multivariable logistic model was used to generate risk score values an

251 A multinomial logistic model was used to identify covariates associate

252 A multivariable logistic model was used to identify factors mediating 90

253 Iterative logistic modeling was performed to investigate variables

254 Random effects logistic modeling was used for data analysis.

255 Sequential logistic modeling was used to assess outcome.

256 Using multivariable logistic models we determined factors independently asso

257 Finally, by using a logistic model, we predict that 75% of discoverable gene

258 Using Bayes' theorem in a logistic model, we used 8 baseline predictors-age, sex,

259 Using logistic modeling, we analyzed this deletion in a large

260 the occurrence of periodontitis in the final logistic model were: MetS (odds ratio [OR] = 2.02; P = 0

261 Contingency tests and binary logistic modeling were used to identify baseline predict

262 Multivariable logistic models were constructed combining conventional

263 After adjudication, simple and multiple logistic models were constructed to determine baseline v

264 Hierarchical logistic models were constructed to estimate association

265 Logistic and multinomial logistic models were constructed to estimate the associa

266 Among children with asthma, logistic models were created to examine the effects of u

267 Multivariable hierarchical logistic models were developed to identify system and ph

268 Both standard and hierarchical logistic models were developed to predict readmission ri

269 Multilevel, multivariable logistic models were employed, with odds ratios (ORs) us

270 Logistic models were estimated for dichotomous outcomes,

271 Episode-specific logistic models were estimated regressing hospital parti

272 Bivariate and multivariable logistic models were estimated to examine temporal trend

273 Logistic models were fit for screening mammography, and

274 "achieved" or "not achieved." Multivariable logistic models were fitted adjusting for age, overweigh

275 Logistic models were fitted for these outcomes as a func

276 Multivariable logistic models were fitted to examine the effects of pa

277 Logistic models were fitted to relate adult overweight a

278 Log-logistic models were fitted to the survival distribution

279 Logistic models were then used to assess the impact of e

280 Mixed-effects logistic models were used for multivariable analyses.

281 Linear and logistic models were used to assess associations between

282 McNemar tests or hierarchical logistic models were used to assess associations between

283 Multivariate multinomial logistic models were used to assess changes in RWHAP/Unc

284 Multivariable Cox and logistic models were used to assess long-term clinical o

285 Logistic models were used to determine the association b

286 Logistic regression and mixed logistic models were used to estimate the odds of being

287 linear growth-curve models and hierarchical logistic models were used to examine relations between i

288 Mixed-effects logistic models were used to identify potential factors

289 Logistic models were used to predict independent surviva

290 were well correlated with Verma and modified Logistic models which gave the best fitting for CD and H

291 Although interaction tests based on the logistic model-which approximates the multiplicative ris

292 odel: If the underlying model is linear, the logistic model will be misspecified.

293 In a multivariable logistic model with a spline transformation for ACT, the

294 istic curve for MR-proADM, NTproBNP, and the logistic model with both markers were 0.77, 0.79, and 0.

295 The logistic model with both markers yielded a larger area u

296 We developed a multilevel logistic model with both state- and nested county-level

297 Consider a logistic model with independent features in which n and

298 associations were tested using hierarchical logistic models with log volume as the exposure, adjusti

299 In weighted logistic models with NHWs as the reference group and con

300 luated using generalized estimating equation logistic models with robust standard errors.