ライフサイエンスコーパス: Poisson

1 Poisson log-linear regression models controlling for tem

2 Poisson modeling was used to estimate the mortality rate

3 Poisson models assessed changes in incidence over time.

4 Poisson models fit with generalized estimating equations

5 Poisson models were used to evaluate the association bet

6 Poisson regression analysis was performed to determine w

7 Poisson regression and purely temporal, spatial, and spa

8 Poisson regression assessed trends in 6- and 12-month co

9 Poisson regression identified clinical, laboratory and d

10 Poisson regression models estimated trends in HCV incide

11 Poisson regression models were used to assess the interv

12 Poisson regression models were used to compare outcomes

13 Poisson regression models were used to estimate incidenc

14 Poisson regression models were used to estimate the age-

15 Poisson regression was employed to determine the indepen

16 Poisson regression was used to analyze overall and subgr

17 Poisson regression was used to analyze the relation betw

18 Poisson regression was used to assess between-group diff

19 Poisson regression was used to assess differences betwee

20 Poisson regression was used to calculate crude and adjus

21 Poisson regression was used to calculate incidence rates

22 Poisson regression was used to compare rates between dia

23 Poisson regression was used to compute relative risks (R

24 Poisson regression was used to estimate prevalence ratio

25 Poisson regression was used to estimate relative risks (

26 Poisson regression was used to estimate relative risks (

27 Poisson regression was used to model dementia incidence

28 Poisson regression with generalized estimating equations

29 Poisson regression with robust variance estimation provi

30 Poisson regressions were applied to a Medicare populatio

31 Poisson's ratios were derived from the calculated elasti

32 C removal rates were 0.52/1000 CVC days (95% Poisson CLs: 0.17, 1.21/1000 CVC days) and 1.72/1000 CVC

33 with an incidence of 1.0/1000 CVC days (95% Poisson CLs: 0.4, 2.07/1000 CVC days; P = 0.005).

34 1/1000 CVC days) and 1.72/1000 CVC days (95% Poisson CLs: 0.89, 3.0/1000 CVC days) in the taurolidine

35 1000 central venous catheter (CVC) days [95% Poisson confidence limits (CLs): 2.12, 2.71 episodes/100

36 t the portfolio of research activities and a Poisson model to examine trends.

37 We applied a Poisson regression model to analyze the longitudinal cha

38 uirements needed to observe any process as a Poisson point process.

39 By assuming a Poisson distribution for the number of DNA fragments pre

40 ays of follow-up in each interval assuming a Poisson distribution.

41 (C98)RhuA lattices display a Poisson's ratio of -1-the lowest thermodynamically possi

42 We fit a Poisson model to estimate the risk of HO-CDI associated

43 nalysis of SCR events, which do not follow a Poisson process observed in other eukaryotic cells.

44 ed over a given time (10 and 20 s) follows a Poisson distribution.

45 the leading edge of binding events follows a Poisson point process, which means signals from multiple

46 es were calculated under the assumption of a Poisson distribution.

47 Here, we present a Poisson mixed model with two random effects terms that a

48 Recently, Witten proposed a Poisson linear discriminant analysis for RNA-Seq data.

49 ws nodes to arrive in batches according to a Poisson process and to form hyperedges with existing bat

50 te ratios (IRRs) by serotype and age using a Poisson model.

51 -cohort (APC) analysis was performed using a Poisson regression model.

52 risk of incident HF was analyzed by using a Poisson regression model.

53 3 through February 2014, were assessed via a Poisson generalized linear mixed model regression for CL

54 We report that a BSSVS approach with a Poisson prior demonstrates less bias toward sample size

55 phils >/=300 cells per muL), analysed with a Poisson regression model corrected for overdispersion wi

56 red formed an incompressible material with a Poisson's ratio of 0.5.

57 AESOP utilizes PDB2PQR and Adaptive Poisson-Boltzmann Solver to generate grid-based electros

58 tcome of HIV incidence with cluster-adjusted Poisson generalised estimated equations in the intention

59 Multivariable adjusted Poisson models were used to estimate the relationship be

60 A risk-adjusted Poisson model evaluated the ratio of observed to expecte

61 22 calendar years, 14 geographic areas, and Poisson regression analysis was used to quantify the eff

62 as heritability of traits with binomial and Poisson distributions are special cases of our expressio

63 he United States using negative binomial and Poisson regression models.

64 diovascular death was assessed using Cox and Poisson regression analyses.

65 Cox and Poisson regression models were used.

66 ic dynamics with constant rate (5-40 Hz) and Poisson-distributed (4 Hz mean) trains of presynaptic in

67 age and sex, were examined using linear and Poisson regression models.

68 owth and obesity were assessed by linear and Poisson regression with robust standard errors, adjustin

69 Linear and Poisson regressions were used, with adjustment for mater

70 Multivariable logistic and Poisson regression were used to assess the impact of the

71 Multiple logistic and Poisson regression were used to estimate effect sizes.

72 or machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30-

73 ical compositions between cities, we applied Poisson time-series regression models to estimate associ

74 t only steady-states but also transients are Poisson distributed.

75 rming within each city were characterized as Poisson regression coefficients describing change in abu

76 A Bayesian Poisson meta-analysis was performed on 88 surveys conduc

77 We introduce a beta-Poisson mixture model that can capture the bimodality of

78 anscripts are well characterized by the beta-Poisson model; the model-fit from BPSC is better than th

79 months after each of these were analysed by Poisson regression with invasive interval cancer screen

80 idence rate ratios (IRRs) were calculated by Poisson regression analysis.

81 95% confidence intervals were calculated by Poisson regression.

82 risk factor associations were determined by Poisson regression (plaque presence), negative binominal

83 , and socioeconomic status were estimated by Poisson regression distribution models.

84 edding (VL > 40 copies/mL) were estimated by Poisson regression models with generalized estimating eq

85 edication, and comorbidity were estimated by Poisson regression models.

86 relative risks of outcomes were estimated by Poisson regression models.

87 cidence rate ratios (IRRs) were estimated by Poisson regression.

88 d then, in multivariate analyses, calculated Poisson proportional incidence rate ratios to estimate t

89 with the rate of invasive interval cancers (Poisson regression coefficient -0.084 [95% CI -0.13 to -

90 tive binomial mixed model (NB-fit), compound Poisson mixed model (CP-fit), and the variance stabilizi

91 a under either negative binomial or compound Poisson mixed models, are provided in the R package Heri

92 he data) is incorrect, and (ii) the compound Poisson process prior model (which describes the prior d

93 We used a conditional Poisson regression to estimate incidence rate ratios.

94 ing confounders were included in conditional Poisson models.

95 rate ratios were computed using conditional Poisson regression with robust standard errors.

96 rast to other processes under consideration (Poisson, Wiener, or Ornstein-Uhlenbeck process).

97 n of negatively charged lipids is consistent Poisson-Boltzmann theory, taking into account charge reg

98 element simulation of unsteady fully coupled Poisson-Nernst-Planck (PNP) equations.

99 accounting for the competing risk of death; Poisson regression was used to compare rates of NCD occu

100 shed that, for thermo-oxidative degradation, Poisson distribution represented a very successful appro

101 Time-dependent Poisson regression was used to evaluate the effect that

102 Furthermore, we show that an over-dispersed Poisson model is comparable to the celebrated Negative B

103 egative stiffness (NS) element types display Poisson's ratio values of -1 and NS values over two orde

104 atric CDI was evaluated using a mixed-effect Poisson model.

105 s determined using multilevel, mixed-effects Poisson regression.

106 y variants all take the form of the familiar Poisson law of rare events, under a nonlinear rescaling

107 We fit Poisson harmonic regression models to surveillance data

108 risk groups (</=1%, >1%-5%, >5%) and fitted Poisson models to assess whether CVD and CKD risk group

109 ith incidence rates were assessed by fitting Poisson regression models.

110 We show that the fluctuating Poisson-Nernst-Planck (PNP) equations for charged multis

111 of RNA sequencing has been assumed to follow Poisson distributions.

112 n along shafts, and within synapses, follows Poisson statistics, establishing that stochastically dic

113 d using generalized estimating equations for Poisson regression.

114 is better than the fit of the standard gamma-Poisson model in > 80% of the transcripts.

115 ly simple data distribution (e.g., Gaussian, Poisson, negative binomial, etc.), which may not be well

116 die log-link for time spent holding the gun; Poisson log-link for pulling the trigger).

117 nt-based methods rely on simple hierarchical Poisson models (e.g. negative binomial) to model indepen

118 ount of hydrogen has a high density and high Poisson ratio as well as extremely low sound velocities

119 er of compounds with a near-zero homogeneous Poisson's ratio, which are here denoted "anepirretic mat

120 inorganic materials with auxetic homogenous Poisson's ratio in polycrystalline form.

121 roximation with an assumption of independent Poisson observations; 2) a particle filtering method; an

122 resources were estimated using zero-inflated Poisson distribution regression models adjusted for pati

123 und that both the marginalized zero-inflated Poisson model and the negative binomial model can provid

124 In this article, we propose a zero-inflated Poisson model for analyzing the Tn-seq data that are hig

125 eling the sampling times as an inhomogeneous Poisson process dependent on effective population size.

126 Moreover, inhomogeneous Poisson spiking behavior is sufficient to account for th

127 id phase transition occurring in interacting Poisson networks.

128 lass cluster analysis and generalized linear Poisson model were used.

129 nfounding factors using a generalized linear Poisson model, in high-risk cluster the prevalence of ne

130 ce rate ratios were calculated in log-linear Poisson regression analyses.

131 nt discharge data, multistate and log-linear Poisson regression models were used to calculate hospita

132 dow approach to mine results from non-linear Poisson-Boltzmann (NLPB) calculations on DNA structures

133 Generalized linear models with log link, Poisson distributions, and robust standard errors were u

134 nce intervals were estimated from log-linked Poisson regression with generalized estimating equations

135 bic-restricted splines and multivariable log-Poisson regression with empirical standard errors were u

136 A longitudinal Poisson regression model was estimated controlling for t

137 The analyses used marginal Poisson and Cox proportional hazards regression, account

138 distribution the more general Conway-Maxwell-Poisson distribution is used instead.

139 ights an identifiability problem: a measured Poisson steady state is consistent with a large variety

140 A multivariable, repeated-measures Poisson model was used to examine the independent associ

141 ea improvement were examined with a modified Poisson regression model.

142 gnosed asthma were computed using a modified Poisson regression.

143 g Fisher's exact test and bivariate modified Poisson regression.

144 Using hierarchical modified Poisson regression models adjusted for patient and pract

145 sed with multivariable hierarchical modified Poisson regression models.

146 Analysis included modified Poisson regression with generalized estimating equations

147 Multivariable modified Poisson regression analyses were performed to assess the

148 Multivariable modified Poisson regression analyses were performed to assess the

149 ries was estimated by multivariable modified Poisson regression models.

150 We used modified Poisson generalised estimating equations to obtain preva

151 (CIMT) at baseline (2004) and used modified Poisson regression (robust error variance) to estimate p

152 We used modified Poisson regression analysis to evaluate the independent

153 We used modified Poisson regression models to assess the associations bet

154 We used modified Poisson regression to assess the relationship between ra

155 Our primary analysis used modified Poisson regression to determine the association between

156 elative risks were calculated using modified Poisson regression models.

157 se within anatomic strata) by using modified Poisson regression were assessed.

158 P. vivax parasite prevalence, and multilevel Poisson regression models showed that such differences w

159 various scientific disciplines: multinomial, Poisson, hypergeometric, and Bernoulli product.

160 Multiple Poisson regression models adjusted for age, sex, smoking

161 In multiple Poisson regression analysis, the incidence rate ratio in

162 Multivariable Poisson log-linear regression was used to estimate adjus

163 Multivariable Poisson regression adjusted for sex, age, weight group,

164 Multivariable Poisson regression models examined admission risk factor

165 Multivariable Poisson regression models were used to evaluate the simu

166 Multivariable Poisson regression models with robust error estimates we

167 Multivariable Poisson regression survival models and Cox analyses were

168 Multivariable Poisson regressions were used to test the association be

169 ding meal using univariate and multivariable Poisson regressions.

170 Analysis was by multivariable Poisson regression with adjustment for maternal characte

171 atios were estimated following multivariable Poisson regression, adjusting for age, sex, ethnicity, s

172 On multivariable Poisson regression, asplenia was the only predictive var

173 ce ratios (SIRs) and, for SCC, multivariable Poisson regression analysis of SIR ratios, adjusting for

174 and vaccine eligibility using multivariable Poisson regression with an offset for person-years.

175 HIV incidence estimated using multivariable Poisson regression with generalized estimating equations

176 Multivariate Poisson regression with robust standard errors was used

177 2016 were analyzed, including a multivariate Poisson regression model of incidence rates.

178 sitemia were identified using a multivariate Poisson regression model.

179 tiveness was estimated by using multivariate Poisson regression models; effectiveness was allowed to

180 Plasmonic nanostructures with a negative Poisson ratio are demonstrated, having the unusual mecha

181 Auxetic materials exhibiting a negative Poisson's ratio are of great research interest due to th

182 rchitected lattice system showing a negative Poisson's ratio over a wide range of applied uniaxial st

183 Materials with a negative Poisson's ratio, also known as auxetic materials, exhibi

184 bases for target properties such as negative Poisson's ratio by using stability and structural motifs

185 d configurations, auxeticity (i.e., negative Poisson's ratio), bistability, and self-locking of Origa

186 el mechanical properties, including negative Poisson's ratios, negative compressibilities and phononi

187 e family of materials that manifest negative Poisson's ratio, which causes an expansion instead of co

188 of graphene, but also exhibit novel negative Poisson's ratio (NPR) behaviors due to the presence of b

189 They exhibit an intrinsic in-plane negative Poisson's ratio, which is dominated by electronic effect

190 d structure and also has an unusual negative Poisson's ratio.

191 3D graphene metamaterial (GM) with negative Poisson's ratio and superelasticity is highlighted.

192 arious degenerations such as Gaussian noise, Poisson noise, speckle noise and pupil location error, w

193 The concept of Poisson's ratio is extended to the cylindrical structure

194 ling was found, and there was no evidence of Poisson noise limiting behavior.

195 and were related to SGA risk with the use of Poisson regression with confounder adjustment; linear sp

196 ads through statistical likelihoods based on Poisson field models.

197 impact of including PSF modeling in ordinary Poisson ordered-subset expectation maximization reconstr

198 of this method is described by Nernst-Planck-Poisson finite element simulations, and both amperometri

199 egy rapidly constructs a model that predicts Poisson-Schrodinger simulations of devices, and that sim

200 Quasi-Poisson time series regression models were applied to es

201 We used a quasi-Poisson generalised additive mixed model to model the mo

202 A facility-level fixed-effects quasi-Poisson regression model was used to examine the inciden

203 d-lag nonlinear modeling integrated in quasi-Poisson regression was used to examine the exposure-lag-

204 to stochastic perturbations and randomized, Poisson on/off switching policy.

205 rates between QFT strata using a two-sample Poisson test.

206 EC is created, which solves the Schrodinger, Poisson and drift-diffusion equations self-consistently.

207 1 or PM2.5 were evaluated with a time-series Poisson regression.

208 applicable to concentrated solid solutions (Poisson-Cahn theory) was applied to describe quantitativ

209 In contrast to the standard Poisson model, theory and experiment show that nonlatchi

210 econd, based on results from the first step, Poisson regression analysis was used to derive the final

211 We fit a joint marginal structural Poisson model to account for time-varying confounding af

212 spatial correlations resulting from the sub-Poisson distribution of the spacing between topological

213 The Poisson assumption may not be as appropriate as the nega

214 The Poisson distribution and negative binomial distribution

215 The Poisson model showed that influenza and pneumonia mortal

216 The Poisson process theory is used to describe the arrival p

217 The Poisson-Plus Model accommodates for this underestimation

218 een the negative binomial classifier and the Poisson classifier is explored, with a numerical investi

219 ease of the shear modulus diminishes and the Poisson's ratio becomes negative-meaning that cerium bec

220 o be available, but rather than assuming the Poisson distribution the more general Conway-Maxwell-Poi

221 he reversible distance change induced by the Poisson's ratio difference between the core fiber (silve

222 efficiency of this device is dictated by the Poisson's ratio of the electrodes.

223 e distribution of the model by employing the Poisson process theory and the characteristic equation.

224 Results indicate some deviation from the Poisson distribution, which is strongest for the virulen

225 Deviations from the Poisson form occur only under two conditions, promoter f

226 significant overdispersion (invalidating the Poisson regression model) and residual autocorrelation (

227 sing a latent variable representation of the Poisson distribution.

228 tive explanation for the universality of the Poisson steady state.

229 implemented to quantify the evolution of the Poisson's ratio and reveal the underlying mechanisms res

230 his work presents a new investigation of the Poisson's ratios of a family of cellular metamaterials b

231 Based on the Poisson multivariate longitudinal Generalized Estimating

232 The data analysis suggests that the Poisson assumption of the standard approach does lead to

233 he paradox is resolved by realizing that the Poisson steady state generalizes to arbitrary mRNA lifet

234 And, by using the Poisson process theory and a continuity technique, the h

235 ments are described by a new model where the Poisson ratio drives transverse motion, resulting in the

236 While the Poisson's ratio of cells and other biological materials

237 The mean-variance method applied to Poisson distributed data is a special case of these prop

238 GM presents a tunable Poisson's ratio by adjusting the structural porosity, ma

239 data for 18 studies, we performed univariate Poisson models on individual studies and a meta-analysis

240 ty altogether or only model its unstructured Poisson-like aspects.

241 quasicrystals, they provide us with unusual Poisson's formulas, and they might give us an unconventi

242 or decompensations, excluding HCC) and used Poisson regression to estimate incidence rate ratios.

243 infection with the general population, used Poisson regression to evaluate anal cancer incidence amo

244 We used Poisson and Cox regression to evaluate pre- and posttrea

245 We used Poisson regression models to compare the incidences of p

246 We used Poisson regression models to estimate the association be

247 We used Poisson regression to calculate prevalence ratios for th

248 We used Poisson regression to calculate the annual percentage re

249 We used Poisson regression to compare the annual relative increa

250 We used Poisson regression to compare the frequency of days on w

251 te of the first offered appointment; we used Poisson regression to compare the proportion of women wh

252 We used Poisson regression to estimate crude prevalence and crea

253 We used Poisson regression to estimate relative risks and 95% co

254 We used Poisson regression with robust variances to derive incid

255 dPCR uses Poisson statistics to estimate the number of DNA fragmen

256 open source R package 'twoddpcr', which uses Poisson statistics to estimate the number of molecules i

257 Using Poisson models, we analyzed their 10 927 INRs to determi

258 Using Poisson regression models that controlled for temporal c

259 Using Poisson regression with generalized estimating equations

260 Using Poisson regression, we assessed the association between

261 Using Poisson regression, we calculated adjusted relative risk

262 Mortality rates were analyzed using Poisson regression and indirect standardization.

263 ed incidence rates of WL were analyzed using Poisson regression.

264 Associations were assessed using Poisson regression with robust variance estimation.

265 and severity scores were calculated by using Poisson and linear regression, respectively.

266 ty was analyzed in 381 participants by using Poisson regression models.

267 nce rate ratios (IRRs) were calculated using Poisson regression for DLBCL risk in relation to HLA mis

268 thma at ages 5-9 years were calculated using Poisson regression models and pooled.

269 mple) by treatment arm were calculated using Poisson regression.

270 Incidence rate ratios were calculated using Poisson regressions while adjusting for sociodemographic

271 CLABSI incidence rates were compared using Poisson regression.

272 s with no recorded thyroid dysfunction using Poisson regression models.

273 lization rates and costs was estimated using Poisson and linear regression, respectively.

274 t recent baseline tests were estimated using Poisson generalized estimating equations.

275 Associations were estimated using Poisson log-linear models controlling for continuous air

276 concurrent medications were estimated using Poisson regression and inverse probability of treatment

277 and year of diagnosis, were estimated using Poisson regression models.

278 ence rate ratios (IRRs) were estimated using Poisson regression.

279 f molecules per partition is estimated using Poisson statistics, and then converted into concentratio

280 ctious disease incidence was evaluated using Poisson regression models.

281 h experiencing a serious adverse event using Poisson regression.

282 Time trends were explored using Poisson regression and reported as annual percent change

283 012 and 2015 for several risk factors, using Poisson regression with robust variance and a bootstrap-

284 Rates were generated using Poisson regression estimated via generalized estimating

285 (hs-cTnT) concentrations (>/=14 ng/L) using Poisson and multinomial logistic regressions, respective

286 omist-drawn blood cultures was modeled using Poisson regression to compare the 12-month intervention

287 easing complexity: frequentist models (using Poisson and negative binomial regressions), and several

288 r age, sex, race/ethnicity, and season using Poisson regression.

289 fferences by AIDS status and over time using Poisson regression.

290 derestimation of target quantity, when using Poisson modeling, especially at higher concentrations.

291 The technique utilizes Poisson maximum likelihood for better signal modeling, a

292 using relative and absolute risk models via Poisson regression.

293 of quantitative microchimerism as a rate via Poisson or negative binomial model with the rate of dete

294 ze nuclear state, estimating nuclear volume, Poisson's ratio, apparent elastic modulus and chromatin

295 eometric mean reproductive hormones, whereas Poisson regression was used to assess risk of sporadic a

296 ons and delivery outcomes were assessed with Poisson regression or analysis of variance.

297 n process, combining standard diffusion with Poisson-distributed jumps.

298 We calculated HIV incidence with Poisson regression modelling as events per person-years

299 d factors associated with viral rebound with Poisson regression.

300 well as a number of materials with near-zero Poisson's ratio.