ライフサイエンスコーパス: Poisson

1 Poisson analysis demonstrated secular trends in demograp

2 Poisson models provided standardized mortality ratios (S

3 Poisson models were constructed to estimate direct prote

4 Poisson multivariable regression analysis was performed

5 Poisson regression analysis was used to estimate the inc

6 Poisson regression examined trends in LT registration, b

7 Poisson regression models estimated adjusted relative ri

8 Poisson regression models estimated prevalence ratios (P

9 Poisson regression models were fitted to determine the e

10 Poisson regression models were used to compare baseline

11 Poisson regression models were used to evaluate associat

12 Poisson regression models were used to identify changes

13 Poisson regression models were used to identify changes

14 Poisson regression models with robust error variance wer

15 Poisson regression models with robust standard errors we

16 Poisson regression models with robust variance were used

17 Poisson regression revealed that VT was a significant pr

18 Poisson regression was used to assess temporal change ov

19 Poisson regression was used to compare incidence rates b

20 Poisson regression was used to determine prevalence rati

21 Poisson regression was used to estimate county-specific

22 Poisson regression was used to evaluate the association

23 Poisson regression was used to evaluate the effect of ag

24 Poisson regression with cluster robust SEs was used to a

25 Poisson regression with cluster robust standard errors w

26 Poisson regression with robust error variance, clustered

27 Poisson regression with robust error variance, clustered

28 Poisson regression with robust standard errors was used

29 Poisson renewal theory provides an evolutionarily preser

30 A Poisson distribution formalism, based on the generalized

31 A Poisson model fitted to our data predicted 16% P. falcip

32 A Poisson model was further applied to assess particle-par

33 A Poisson surface reconstruction algorithm generates a fin

34 ory (i.e., [Formula: see text] states) and a Poisson clock.

35 trains of stimuli to motor nerves timed as a Poisson process and coherence analysis, we also examined

36 Modeling dynamics as a Poisson process enables connecting the picosecond timesc

37 spreading dynamics cannot be described as a Poisson random process and the corresponding event time

38 e used generalized linear models, assuming a Poisson distribution and log link function, with single-

39 ts will occur at constant rate as given by a Poisson distribution.

40 to peripheral stimulation is simulated by a Poisson process generating nerve fiber spike trains at v

41 e individual reproduction number following a Poisson lognormal distribution.

42 of both constitutive and periodic genes is a Poisson process with transcription rates scaling with ce

43 (i.e. the SSF) is likelihood-equivalent to a Poisson model with stratum-specific fixed intercepts.

44 In addition, we used a Poisson generalized linear model to estimate excess perf

45 We used a Poisson regression tree model to estimate an optimal VDP

46 confidence intervals were estimated using a Poisson distribution.

47 analyses evaluated day 0-69 findings using a Poisson regression model accounting for overdispersion.

48 ot in outbreak over the same period, using a Poisson regression model adjusting for correlation withi

49 arkov models (HMMs), especially those with a Poisson density governing the latent state-dependent emi

50 Generalized estimating equations with a Poisson loglinear model were used to assess the impact o

51 ntain ~1 billion bacterial phylotypes with a Poisson lognormal diversity distribution.

52 r switching heating/cooling on or off with a Poisson rate, r, when the load leaves the comfort zone.

53 A generalised additive Poisson time-series model was applied to estimate the re

54 Adjusted Poisson regression models accounted for 14 resident cova

55 Adjusted Poisson regression was used to assess associations betwe

56 tiveness (VE), using unadjusted and adjusted Poisson regression of cytology (HSIL) and histopathology

57 Multivariable-adjusted Poisson regression was used to identify factors associat

58 d by trajectory group and performed adjusted Poisson regression to estimate adjusted incidence rate r

59 sis were respectively analysed using Cox and Poisson regression.

60 and 10 year of follow-up using logistic and Poisson regression models.

61 Linear mixed and Poisson mixed models were used to compare outcomes with

62 using negative binomial regression model and Poisson regression model with a robust variance estimato

63 gistic regression for AF detection rate, and Poisson regression modelling for CHA2DS2-VASc scores.

64 Extended Cox regression and Poisson regression were used for statistical analysis.

65 Mixed model linear regressions and Poisson regressions were used to estimate continuous and

66 -15) using data from a systematic review and Poisson regression.

67 x proportional hazards, competing risks, and Poisson regression.

68 Coarsened exact matching techniques and Poisson regression models were used to estimate the risk

69 families, we used Mantel-Haenszel tests and Poisson regressions to estimate incidence rate ratios fo

70 ur NTCP models, Lyman, Logistic, Weibull and Poisson, were fit to the population data.

71 verse probability of treatment weighting and Poisson regression to evaluate RVE in preventing influen

72 Alterations of Young's modulus (YM) and Poisson's ratio (PR) in biological tissues are often ear

73 species delimitation analyses were applied: Poisson tree processes (PTP), automatic barcode gap disc

74 llowing and preceding a baseline assessment (Poisson beta-coefficient, 1.39; P=0.0003).

75 on, which derives from waiting times between Poisson events.

76 and 30-day readmission risk using bivariable Poisson, Fine and Gray, and log-binomial regression mode

77 and 30-day readmission risk using bivariable Poisson, Fine-Gray, and log-binomial regression models.

78 Both Poisson frameworks preserve the coding accuracy and robu

79 The number of events was analyzed by Poisson regression and other outcomes with repeated-meas

80 tage renal disease (ESRD) were calculated by Poisson regression stratified by age and adjusted for et

81 Annual rates estimated by Poisson regression were stratified by sex, care setting,

82 s of no return to baseline were estimated by Poisson regression with robust error variance and adjust

83 morbid neurologic function were estimated by Poisson regression with robust error variance.

84 pre-vaccination (baseline) were evaluated by Poisson regression.

85 stent with measurements limited primarily by Poisson counting statistics, i.e., the number of uranium

86 the tracer follows a non-Markovian coloured Poisson process that accounts for all empirical observat

87 e normalization of read counts to a compound Poisson distribution empirically derived from UMI datase

88 al logistic regression model and conditional Poisson regression model were used to estimate the risks

89 ty if neurons are endowed with conditionally Poisson firing.

90 Using a mixed-effect Poisson model, we showed that, despite some variations o

91 this work, a network with a giant effective Poisson's ratio on a soft substrate is found to be under

92 Conditional fixed-effects Poisson regression models were used to determine inciden

93 for incident tuberculosis with mixed-effects Poisson regression models.

94 y state and age group, we used mixed-effects Poisson regression to determine individual-level and dis

95 ing time-dependent Ginzburg Landau equation, Poisson's equation and semiconductor charge equations.

96 a, and used generalized estimating equations Poisson regression models to estimate incidence rate rat

97 gle-molecule recognition through equilibrium Poisson sampling (SiMREPS).

98 of transmission were calculated using exact Poisson methods.

99 ov chain Monte Carlo methods estimated extra-Poisson variation at aliquot, batch, and lab levels.

100 Within-batch testing had 2.5-fold extra-Poisson variation (95%CI 2.1,3.5) for next-gen assays.

101 Between-lab variation increased extra-Poisson variation to 3.4-fold (95% CI 2.6,5.4).

102 We fitted Poisson regression models with year as the exposure and

103 Generalized estimating equations for Poisson regression were used to investigate the relation

104 A 5% claims sample was used for Poisson regression models to quantify visit trends.

105 tation count statistics by (1) using a Gamma-Poisson mixture model to capture the mutation-rate heter

106 We then fit two geostatistical Poisson models to both data-sets and compare the paramet

107 Such a giant Poisson's ratio has the same effect in other systems.

108 We used a Bayesian hierarchical Poisson meta-regression model to estimate the pooled cum

109 stimated viral loads based on the single-hit Poisson model were compared, and a hybrid Poisson digita

110 it Poisson model were compared, and a hybrid Poisson digital model for calibrated viral load estimati

111 models non-expressed genes by zero-inflated Poisson distributions and models expressed genes by trun

112 Zero-inflated Poisson regression analyses showed that the likelihood o

113 ratio (MR) was estimated using zero-inflated Poisson, adjusted for maternal age and income.

114 omial (ASHIC-PM) model and the zero-inflated Poisson-multinomial (ASHIC-ZIPM) model.

115 iking activity using a fractal inhomogeneous Poisson process with dynamic rate, which is the product

116 incidence overall (incidence rate ratio (IRR)Poisson = 0.97, 95% CI: 0.90, 1.04) but was associated w

117 yndrome and outcome using generalized linear Poisson regression adjusted for age, injury mechanism, I

118 We show how linear Poisson modelling advances pLSA, giving covariances on m

119 incidence rate ratios obtained in log-linear Poisson regression analysis.

120 Adjusted linear, Poisson, and mixed-effects regression models were used,

121 Bivariate analyses using log-linked Poisson regression with generalized estimating equations

122 topping the beach can be modeled as a marked Poisson process with exponentially distributed sizes or

123 energy decomposition by molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method suggests

124 imulations combined with Molecular Mechanics-Poisson Boltzmann Surface Area calculations identified t

125 Mixed Poisson regression models were fitted to examine associa

126 Modified Poisson regression estimated perinatal mental illness ri

127 Modified Poisson regression estimated perinatal mental illness ri

128 Modified Poisson regression models were fit to obtain relative ri

129 Modified Poisson regression models were used to estimate relative

130 Modified Poisson regression was used to assess the effect of cran

131 Modified Poisson regression was used to compare risks of the outc

132 Modified Poisson regression was used to model adjusted risk facto

133 Modified Poisson regression with robust variance estimation was u

134 A modified Poisson model was used to identify factors associated wi

135 eralized estimating equations and a modified Poisson model.

136 their patients was examined using a modified Poisson regression model.

137 s, were calculated in STATA using a modified Poisson regression with robust error variances to obtain

138 who remained to be treated using a modified Poisson regression.

139 who remained to be treated using a modified Poisson regression.

140 We applied modified Poisson regression models to assess the strength of asso

141 Multivariable modified Poisson regression models adjusting for confounding by a

142 were evaluated using multivariable modified Poisson regression models.

143 ctions were characterised by use of modified Poisson models and compared with and without adjustment

144 d non-Indigenous children by use of modified Poisson regression.

145 Logistic or modified Poisson regression, as appropriate, was used to estimate

146 Site-specific modified Poisson regression models were constructed to assess the

147 numerical simulations based on the modified Poisson-Nernst-Planck model and showed that the results

148 We used modified Poisson regression with robust variance estimation to es

149 ntion-to-treat (ITT) analyses using modified Poisson generalised linear mixed effects models.

150 sk ratios (RRs) were obtained using modified Poisson regression and weighted risk differences (RDs) u

151 s analyzed in separate models using modified Poisson regression with interaction terms.

152 al and FVC of <80% predicted) using modified Poisson regression, adjusting for relevant confounders.

153 (adults only) were calculated using modified Poisson regression, with 2009-2010 as baseline.

154 were also assessed in donors using modified Poisson regression.

155 A multilevel Poisson regression model was used to estimate the risks

156 -adjusted DDLT rates using nested multilevel Poisson regression with random intercepts for DSA and tr

157 ates per active person-year using multilevel Poisson regression and empirical Bayes methods.

158 vival to hospital discharge using multilevel Poisson regression models.

159 Multiple Poisson regression indicated that a 1-standard-deviation

160 Multivariable Poisson regression estimated the adjusted effect of BMI

161 Multivariable Poisson regression models with robust error variance wer

162 Multivariable Poisson regression models, disaggregated by key populati

163 Multivariable Poisson regression was used to estimate the incidence ra

164 Multivariable Poisson regression was used to examine the relation betw

165 In a multivariable Poisson regression model adjusting for potential confoun

166 lence ratios with 95% CIs from multivariable Poisson regression.

167 Among HEU, multivariable Poisson regression models were fit to evaluate associati

168 Among HEU, multivariable Poisson regression models were fitted to evaluate associ

169 In multivariable Poisson regression models, vignette portrayals of untrea

170 rounding buffer zones, through multivariable Poisson regression analyses.

171 We used multivariable Poisson and ordered logit regression models to estimate

172 The analysis used multivariable Poisson regression adjusted for historical clinic-level

173 We used multivariable Poisson regression models to calculate adjusted incidenc

174 Survey of Family Growth using multivariable Poisson regression with multiple covariates and adjustme

175 es status was adjusted for in a multivariate Poisson regression model.

176 Univariate and multivariate Poisson regression models were fit for the outcomes.

177 is modeled using a mixture of K Multivariate Poisson Log-Normal distributions and parameters are esti

178 A mixture of multivariate Poisson-log normal (MPLN) model is developed for cluster

179 oamplification in dPCR based on multivariate Poisson statistics and suggest reducing the digital occu

180 quipment use was examined using multivariate Poisson regression with generalized estimating equations

181 combinations of properties (such as negative Poisson ratios) that do not occur in conventional solids

182 sordered stacks, renders remarkable negative Poisson's ratios ranging from -0.25 to -0.55.

183 hene films also yields finely-tuned negative Poisson's ratios.

184 First, we incorporated non-Poisson spiking into both models and found that more neu

185 Theoretical calculations using a nonlinear Poisson-Boltzmann equation give excellent agreement with

186 We thus (i) detail a Log-normal-Poisson (LNP) background model that accounts for this va

187 onic anhydrase IX, and we developed a novel, Poisson-based statistical framework to analyze the resul

188 tion camera images contain a large amount of Poisson noise.

189 ly, we propose two alternate formulations of Poisson balanced spiking networks: (1) a "local" framewo

190 cal Master Equations for GRNs as mixtures of Poisson distributions and obtain explicit formulas for t

191 n (ICR), a continuum model based on a set of Poisson-Nernst-Planck and Stokes-Brinkman equations was

192 that obtained from the numerical solution of Poisson-Nernst-Planck equations in axisymmetric domain.

193 5 compared with previous 3-year intervals on Poisson regression model (P=0.001).

194 ge reconstruction was performed: an ordinary Poisson ordered-subsets expectation maximization algorit

195 Piecewise Poisson regression with a discontinuity was used to esti

196 trends were estimated by linear or piecewise Poisson regression.

197 f volumetric compression ratio in predicting Poisson's ratio outcomes in the manufacture process.

198 Quasi-Poisson generalized additive models explored association

199 We used linear and quasi-Poisson regression models to explore the associations be

200 asis' (RAMMIE) method and the improved quasi-Poisson regression-based method known as 'Farrington Fle

201 ge involved a county-level time series quasi-Poisson regression, using a distributed lag nonlinear mo

202 Using quasi-Poisson regression models, we estimated rate ratios (RRs

203 cific associations were estimated with quasi-Poisson regression models and then pooled by random-effe

204 lation could be modeled as self-regenerating Poisson renewal processes, producing exponential distrib

205 nal hazards regression, logistic regression, Poisson regression, and generalized linear model with ga

206 d as 1 - relative risk derived from a robust Poisson regression model adjusted for age.

207 ted PrEP uptake and continuation, and robust Poisson regression methods were used to identify correla

208 low fracture surface energy despite similar Poisson's ratio to that of many ductile metallic and org

209 tion in the rate of exocytosis beyond simple Poisson dynamics may be needed to fully account for the

210 We modeled the dynamics as a two-state Poisson process and calculated the kinetic rates from th

211 We used a two-state Poisson process to describe the dynamics and calculate t

212 Sub-Poisson field with much reduced fluctuations in a cavity

213 ode has been utilized to generate highly sub-Poisson fields.

214 The highly sub-Poisson photon statistics were not deteriorated by simul

215 Here, we report sub-Poisson field lasing in a microlaser operating with hund

216 Multivariable survey Poisson regressions were applied to estimate RR and 95%

217 ciated with MVPA were estimated using survey Poisson regressions.

218 Bayesian hierarchical spatio-temporal Poisson models were used to fit the MiP incidence rate a

219 The Poisson mixed-effects models (PMM) can be an appropriate

220 The Poisson models indicated a slightly increasing trend in

221 The model mimics the cell elongation and the Poisson effect (necking) that occur in actual archentero

222 ile is consistently assessed by applying the Poisson equation to all the charges present in the syste

223 ults and experimental data, we determine the Poisson ratio of the cortex in a frequency-dependent man

224 regime analogous to trends reported for the Poisson ratio of glassy materials.

225 two models under the Bayesian framework: the Poisson-multinomial (ASHIC-PM) model and the zero-inflat

226 -state mRNA distribution is a mixture of the Poisson and negative hypergeometric distributions, which

227 probability matrix in the likelihood of the Poisson based HMM is replaced by the observed transition

228 ovel quasi-biophysical interpretation of the Poisson generalized linear model (GLM) as a special case

229 of effect size, due to the properties of the Poisson-binomial distribution.

230 known, and numerous simulations based on the Poisson-Nernst-Planck formalism provide details of the o

231 ilities, the system was used to overcome the Poisson loading problem by sorting for droplets containi

232 We find that the Poisson ratio of the cortex decreases in the measured fr

233 und TF always monotonically decreases to the Poisson limit with increasing decoy numbers.

234 decoy numbers, before decreasing back to the Poisson limit.

235 results and present the modifications to the Poisson maximum likelihood estimation (MLE) sCMOS analys

236 dized incidence rate ratios (IRRs) using the Poisson distribution were calculated comparing STEMI rat

237 gression/improvement were analysed using the Poisson model and the Kaplan-Meier method, respectively.

238 ng 100 percent positive partitions, with the Poisson distribution showing that an average of only 3 m

239 equation, which is solved together with the Poisson equation, leading to analytical formulas for the

240 The approach is coupled with the Poisson-Boltzmann-drift-diffusion (PBDD) equations to pr

241 ine (3TC), and others were estimated through Poisson regression models.

242 We used a Bayesian space-time Poisson regression model to examine the relationship bet

243 MAPS adopts a zero-truncated Poisson regression framework to explicitly remove system

244 ns of an isotropic actin cortex with tunable Poisson ratio to measured cellular force response.

245 ppression CD4 count <200 and >=200 cells/uL, Poisson regression models estimated hospitalization inci

246 view were also consistent with an underlying Poisson renewal process.

247 mation times were consistent with underlying Poisson renewal processes (human: lambda(f), 4.2%/ms+/-1

248 ly high stiffness-to-weight ratio or unusual Poisson's ratio.

249 We used Poisson and linear regression models to calculate catego

250 We used Poisson generalized estimating equation regression model

251 We used Poisson models to estimate prevalence rate ratios, compa

252 We used Poisson regression modeling to calculate the prevalence

253 We used Poisson regression models stratified by gender to test i

254 We used Poisson regression models with log link functions to est

255 We used Poisson regression models, reporting B coefficients, to

256 We used Poisson regression to estimate incidence rate ratios (IR

257 We used Poisson regression to evaluate relative VE (RVE) in prev

258 We used Poisson regression with adjustment for individual and ar

259 We used Poisson regression with random effects for state and yea

260 avior, and clinical characteristics, we used Poisson regression with robust variance to estimate prev

261 ios (IRRs) associated with abortion, we used Poisson regression with the logarithm of woman-years at

262 We used Poisson to estimate adjusted risk differences and risk r

263 Using Poisson distributions, probabilities of individual fello

264 Using Poisson regression, we assessed the association of socio

265 Using Poisson regression, we calculated incidence rates (IRs)

266 Using Poisson regression, we estimated incidence rate ratios (

267 the increase in 2017-2019 vs 2014-2016 using Poisson regression.

268 blocker use and outcomes were analyzed using Poisson regression model with robust standard errors for

269 tiation and continuation were assessed using Poisson regression in univariate and multivariate analys

270 infection in each period were assessed using Poisson regression.

271 HF re-hospitalizations were calculated using Poisson regression models.

272 ce rate ratios (aIRRs) were calculated using Poisson regression, adjusting for demographic and clinic

273 Rs) and 95% confidence intervals (CIs) using Poisson regression, controlling for potential confoundin

274 nces in obesity at age 4 were compared using Poisson regression models.

275 tes of pcsAION and sAION were compared using Poisson regression models.

276 ulated, and trend tests were conducted using Poisson regression.

277 We analysed the data using Poisson and piecewise exponential multilevel models to a

278 s of viral suppression were determined using Poisson regression models incorporating RDS-II weights.

279 s and rate differences were determined using Poisson regression.

280 ociations between risk factors and DGF using Poisson multivariate regression and between DGF and graf

281 revalence ratios (PRs) and differences using Poisson regression, also examining sex-specific relation

282 alent and incident DSPN were estimated using Poisson regression models with a robust error variance a

283 d incidence rate ratios were estimated using Poisson regression with robust variance while accounting

284 os for women versus men were estimated using Poisson regression.

285 oup, and relative rates were estimated using Poisson regression.

286 dence rate ratio (IRRs) were estimated using Poisson regressions, adjusted for sociodemographics, com

287 and individual outcomes were examined using Poisson regression models.

288 n, region, and ethnicity were examined using Poisson regression, taking clustering within general pra

289 ors for ESBL-E acquisition in hospital using Poisson regression.

290 ons, and mortality between HEU and HUU using Poisson regression.

291 calculated the yearly SSTI incidences using Poisson regression with clustering by patient.

292 fic mental disorder, we estimated MRRs using Poisson regression models, adjusting for sex, age, and c

293 s, diagnoses, operations, and outcomes using Poisson analysis.

294 , death) was compared between regimens using Poisson regression.

295 ence after IRS were assessed by season using Poisson regression models with robust standard errors, c

296 Retrospective cohort study using Poisson regression and inverse probability of treatment

297 Subgroups were compared with Poisson regression.

298 lyzed for incident asthma exacerbations with Poisson regression models that included clinical measure

299 were compared between treatment groups with Poisson regression and one-inflated beta regression, res

300 work unifies balanced spiking networks with Poisson generalized linear models and suggests several p