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 motic leakage were determined using a binary logistic model.

4 contributor to the explanatory ability of a logistic model.

5 mum likelihood estimator for parameters in a logistic model.

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

7 model different from the conventional linear logistic model.

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

9 both symptoms was evaluated by a multinomial logistic model.

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

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

12 uted, according to a multivariate regression logistic model.

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

14 robust, Bayesian hierarchical five-parameter logistic model.

15 e likelihood of becoming newly sensitized in logistic models.

16 erature review were included in multivariate logistic models.

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

18 assified as users or nonusers in multinomial logistic models.

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

20 ure and mortality in Cox proportional hazard logistic models.

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

22 ithin 1 year were assessed with hierarchical logistic models.

23 traditional stratified analysis and standard logistic models.

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

25 Scale) were also introduced in multivariable logistic models.

26 ies using 2-level hierarchical multivariable logistic models.

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

28 y hospital or patient characteristics in our logistic models.

29 ssessed by using univariate and multivariate logistic models.

30 ation were determined with backward stepwise logistic modeling.

31 ed in-hospital mortality using multivariable logistic modeling.

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

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

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

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

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

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

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

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

40 Multivariable logistic models adjusted for possible confounders (mater

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

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

43 In simple logistic models, adolescent anxiety or depressive disord

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

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

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

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

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

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

50 Multivariate hierarchical logistic models and stepwise linear regression models ad

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

67 Logistic model confirmed that maintenance of higher oxyg

68 A logistic model confirmed the joint significance of geogr

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

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

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

72 Logistic models correctly predicted a blood lead elevati

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

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

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

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

77 A principal components logistic model discriminated healthy people from patient

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

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

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

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

82 Logistic model fitting of simple coal workers' pneumocon

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

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

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

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

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

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

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

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

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

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

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

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

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

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

97 Multivariable hierarchical logistic models identified predictors of revascularizati

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

115 A multivariable logistic model, optimized for assessment of corticostero

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

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

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

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

120 A logistic model performed a conditional multivariate anal

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

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

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

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

125 Logistic models predicting 30-day readmission were compa

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

127 In logistic modeling, previous mammography alone explained

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

129 Logistic models provided the adjusted odds of study end

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

131 Our logistic models reveal two alternative requirements for

132 Multivariable logistic modeling revealed a lower baseline platelet cou

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

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

135 The logistic model showed good discrimination ability (area

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

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

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

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

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

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

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

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

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

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

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

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

148 In logistic models that included maternal sociodemographic

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

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

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

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

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

154 Under logistic models, this biotic rebound should be exponenti

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

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

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

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

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

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

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

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

163 Constrained logistic models to predict bacterial infection were fit

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

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

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

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

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

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

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

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

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

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

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

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

176 Iterative logistic modeling was performed to investigate variables

177 Random effects logistic modeling was used for data analysis.

178 Sequential logistic modeling was used to assess outcome.

179 Using multivariable logistic models we determined factors independently asso

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

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

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

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

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

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

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

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

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

189 Logistic models were fit for screening mammography, and

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

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

192 Logistic models were fitted to relate adult overweight a

193 Log-logistic models were fitted to the survival distribution

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

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

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

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

198 Logistic models were used to determine the association b

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

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

201 Logistic models were used to predict independent surviva

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

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

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

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

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

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

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

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