ライフサイエンスコーパス: Poisson regression

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

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

3 Subgroups were compared with Poisson regression.

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

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

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

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

8 as an absolute margin of 0.5% determined by Poisson regression.

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

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

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

12 d factors associated with viral rebound with Poisson regression.

13 were also assessed in donors using modified Poisson regression.

14 using relative and absolute risk models via Poisson regression.

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

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

17 gnosed asthma were computed using a modified Poisson regression.

18 h experiencing a serious adverse event using Poisson regression.

19 1 or PM2.5 were evaluated with a time-series Poisson regression.

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

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

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

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

24 CLABSI incidence rates were compared using Poisson regression.

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

26 d using generalized estimating equations for Poisson regression.

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

28 95% confidence intervals were calculated by Poisson regression.

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

30 trends were estimated by linear or piecewise Poisson regression.

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

32 sis were respectively analysed using Cox and Poisson regression.

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

34 s and rate differences were determined using Poisson regression.

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

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

37 infection in each period were assessed using Poisson regression.

38 ciated with MVPA were estimated using survey Poisson regressions.

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

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

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

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

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

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

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

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

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

48 Multivariable modified Poisson regression analyses were performed to assess the

49 rounding buffer zones, through multivariable Poisson regression analyses.

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

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

52 We used modified Poisson regression analysis to evaluate the independent

53 Poisson regression analysis was performed to determine w

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

55 Poisson regression analysis was used to estimate the inc

56 22 calendar years, 14 geographic areas, and Poisson regression analysis was used to quantify the eff

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

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

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

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

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

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

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

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

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

66 Poisson regression and purely temporal, spatial, and spa

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

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

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

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

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

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

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

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

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

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

77 Modified Poisson regression estimated perinatal mental illness ri

78 Modified Poisson regression estimated perinatal mental illness ri

79 Multivariable Poisson regression estimated the adjusted effect of BMI

80 Rates were generated using Poisson regression estimated via generalized estimating

81 Poisson regression examined trends in LT registration, b

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

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

84 Poisson regression identified clinical, laboratory and d

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

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

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

88 5 compared with previous 3-year intervals on Poisson regression model (P=0.001).

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

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

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

92 In a multivariable Poisson regression model adjusting for potential confoun

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

94 2016 were analyzed, including a multivariate Poisson regression model of incidence rates.

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

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

97 A longitudinal Poisson regression model was estimated controlling for t

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

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

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

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

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

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

104 els with results from an unweighted adjusted Poisson regression model.

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

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

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

108 sitemia were identified using a multivariate Poisson regression model.

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

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

111 We used Poisson regression modeling to calculate the prevalence

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

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

114 Adjusted Poisson regression models accounted for 14 resident cova

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

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

117 Multivariable modified Poisson regression models adjusting for confounding by a

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

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

120 Poisson regression models estimated adjusted relative ri

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

122 Poisson regression models estimated prevalence ratios (P

123 Poisson regression models estimated trends in HCV incide

124 Multivariable Poisson regression models examined admission risk factor

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

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

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

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

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

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

131 We used multivariable Poisson regression models to calculate adjusted incidenc

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

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

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

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

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

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

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

139 Modified Poisson regression models were fit to obtain relative ri

140 Poisson regression models were fitted to determine the e

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

142 Mixed Poisson regression models were fitted to examine associa

143 Poisson regression models were used to assess the interv

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

145 Poisson regression models were used to compare baseline

146 Poisson regression models were used to compare outcomes

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

148 Poisson regression models were used to estimate incidenc

149 Modified Poisson regression models were used to estimate relative

150 Poisson regression models were used to estimate the age-

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

152 Poisson regression models were used to evaluate associat

153 Multivariable Poisson regression models were used to evaluate the simu

154 Poisson regression models were used to identify changes

155 Poisson regression models were used to identify changes

156 Cox and Poisson regression models were used.

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

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

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

160 Multivariable Poisson regression models with robust error estimates we

161 Poisson regression models with robust error variance wer

162 Multivariable Poisson regression models with robust error variance wer

163 Poisson regression models with robust standard errors we

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

165 Poisson regression models with robust variance were used

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

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

168 Multivariable Poisson regression models, disaggregated by key populati

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

170 In multivariable Poisson regression models, vignette portrayals of untrea

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

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

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

174 were evaluated using multivariable modified Poisson regression models.

175 vival to hospital discharge using multilevel Poisson regression models.

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

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

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

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

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

181 ctious disease incidence was evaluated using Poisson regression models.

182 ries was estimated by multivariable modified Poisson regression models.

183 and individual outcomes were examined using Poisson regression models.

184 sed with multivariable hierarchical modified Poisson regression models.

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

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

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

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

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

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

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

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

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

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

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

196 Poisson regression revealed that VT was a significant pr

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

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

199 Multivariable Poisson regression survival models and Cox analyses were

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

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

202 We used Poisson regression to calculate prevalence ratios for th

203 We used Poisson regression to calculate the annual percentage re

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

205 We used Poisson regression to compare the annual relative increa

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

221 Poisson regression was used to analyze overall and subgr

222 Poisson regression was used to analyze the relation betw

223 Adjusted Poisson regression was used to assess associations betwe

224 Poisson regression was used to assess between-group diff

225 Poisson regression was used to assess differences betwee

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

227 Poisson regression was used to assess temporal change ov

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

229 Poisson regression was used to calculate crude and adjus

230 Poisson regression was used to calculate incidence rates

231 Poisson regression was used to compare incidence rates b

232 Poisson regression was used to compare rates between dia

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

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

235 Poisson regression was used to compute relative risks (R

236 Poisson regression was used to determine prevalence rati

237 Poisson regression was used to estimate county-specific

238 Poisson regression was used to estimate prevalence ratio

239 Poisson regression was used to estimate relative risks (

240 Poisson regression was used to estimate relative risks (

241 Multivariable Poisson regression was used to estimate the incidence ra

242 Poisson regression was used to evaluate the association

243 Poisson regression was used to evaluate the effect of ag

244 Multivariable Poisson regression was used to examine the relation betw

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

246 Modified Poisson regression was used to model adjusted risk facto

247 Poisson regression was used to model dementia incidence

248 Using Poisson regression, we assessed the association between

249 Using Poisson regression, we assessed the association of socio

250 Using Poisson regression, we calculated adjusted relative risk

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

252 Using Poisson regression, we estimated incidence rate ratios (

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

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

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

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

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

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

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

260 Multivariable Poisson regressions were used to test the association be

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

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

263 Piecewise Poisson regression with a discontinuity was used to esti

264 We used Poisson regression with adjustment for individual and ar

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

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

267 Poisson regression with cluster robust SEs was used to a

268 Poisson regression with cluster robust standard errors w

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

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

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

272 Poisson regression with generalized estimating equations

273 Analysis included modified Poisson regression with generalized estimating equations

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

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

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

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

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

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

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

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

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

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

284 Poisson regression with robust error variance, clustered

285 Poisson regression with robust error variance, clustered

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

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

288 Poisson regression with robust standard errors was used

289 Multivariate Poisson regression with robust standard errors was used

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

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

292 012 and 2015 for several risk factors, using Poisson regression with robust variance and a bootstrap-

293 Poisson regression with robust variance estimation provi

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

295 Modified Poisson regression with robust variance estimation was u

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

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

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

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

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