ライフサイエンスコーパス: Poisson regression

1 d using generalized estimating equations for Poisson regression.

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

3 95% confidence intervals were calculated by Poisson regression.

4 ence risk ratios (PRRs) were estimated using Poisson regression.

5 2, with those in 1993-97 using multivariable Poisson regression.

6 Mortality rate ratios were estimated by Poisson regression.

7 ios adjusted for potential confounders using Poisson regression.

8 me periods were calculated using conditional Poisson regression.

9 95% confidence intervals) were estimated by Poisson regression.

10 rtality was estimated with spline fits using Poisson regression.

11 maternal death between the two surveys using Poisson regression.

12 one to donate were quantified using modified Poisson regression.

13 /mL by 15 months was assessed using modified Poisson regression.

14 standardized incidence were calculated using Poisson regression.

15 rent HPV infections per woman was studied by Poisson regression.

16 The risk of IPD was assessed using Poisson regression.

17 rand (Boostrix/Adacel), were estimated using Poisson regression.

18 os and 95% CIs were estimated using modified Poisson regression.

19 years and age-adjusted trends estimated from Poisson regression.

20 th disease occurrence were computed by using Poisson regression.

21 We calculated relative rates using Poisson regression.

22 age, sex, and co-morbidity using multilevel Poisson regression.

23 Trends over time were evaluated using Poisson regression.

24 est, and risk factors were investigated with Poisson regression.

25 d ARV discontinuations were identified using Poisson regression.

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

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

28 d factors associated with viral rebound with Poisson regression.

29 using relative and absolute risk models via Poisson regression.

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

31 gnosed asthma were computed using a modified Poisson regression.

32 h experiencing a serious adverse event using Poisson regression.

33 1 or PM2.5 were evaluated with a time-series Poisson regression.

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

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

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

37 CLABSI incidence rates were compared using Poisson regression.

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

39 ding meal using univariate and multivariable Poisson regressions.

40 idence intervals (CIs) were calculated using Poisson regression adjusted for age group, sex, race, an

41 tagonist users and nonusers, estimated using Poisson regression adjusted for age, calendar year, dise

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

43 ease and some follow-up at ages 35-74 years, Poisson regression (adjusted for age at risk, amount smo

44 idence intervals (CIs) were calculated using Poisson regression, adjusted for age, sex, co-morbidity,

45 ratio (IRR) was calculated using conditional Poisson regression, adjusted for possible confounders.

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

47 iness were estimated using repeated-measures Poisson regression, adjusting for confounders.

48 rates in women without celiac disease using Poisson regression, adjusting for sociodemographics, com

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

50 Rates of ADHD diagnosis were derived using Poisson regression analyses after adjustments for potent

51 Multivariable modified Poisson regression analyses were performed to assess the

52 Multivariable modified Poisson regression analyses were performed to assess the

53 Modified Poisson regression analyses were used to estimate relati

54 Poisson regression analyses were used to estimate the as

55 Poisson regression analyses were used to test whether Un

56 A time-series design, based on Joinpoint and Poisson regression analyses, was used to assess the chan

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

58 confidence intervals obtained in log-linear Poisson regression analyses.

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

60 oup than in the placebo group according to a Poisson regression analysis (roflumilast 0.805 vs placeb

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

62 We used modified Poisson regression analysis to evaluate the independent

63 Poisson regression analysis was performed to determine w

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

65 22 calendar years, 14 geographic areas, and Poisson regression analysis was used to quantify the eff

66 Poisson regression analysis, adjusted for biologically p

67 In Poisson regression analysis, nCRT was an independent pre

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

69 With the use of linear and Poisson regression analysis, we examined the association

70 Monthly SSI counts were analyzed using Poisson regression analysis.

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

72 Quasi-poisson regression and distributed lag nonlinear models

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

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

75 Poisson regression and purely temporal, spatial, and spa

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

77 ratios (IRRs) of MAARI were estimated using Poisson regression and stratified by statin use.

78 Temporal trends were tested using Poisson regression and summarized with quarterly inciden

79 We modelled incidence rate ratios with quasi-Poisson regression and we analysed parasite densities us

80 us non-PKD renal transplant recipients using Poisson regression, and we determined incidence rate rat

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

82 A distributed lag Poisson regression assessed cumulative effects for an 8-

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

84 Alternative methods (Poisson regression, autocorrelation analysis, and surrog

85 laria and non-malaria illnesses (computed by Poisson regression), by G6PD deficiency category.

86 Longitudinal transition models with modified Poisson regression calculated adjusted relative risks an

87 Using time series analysis and multilevel poisson regression clustering to the hospital level, we

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

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

90 Poisson regression compared the rate of bleeding, stroke

91 Incidence rate ratios were estimated using Poisson regression, comparing rates of events in the 2-y

92 In covariate adjusted Poisson regression, dabigatran (rate ratio, 1.48; 95% co

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

94 Rates were generated using Poisson regression estimated via generalized estimating

95 Modified Poisson regression evaluated prediabetes prevalence acro

96 patients during 1997-2011 was estimated with Poisson regression for all TDR mutations and individuall

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

98 egression was used for preterm delivery, and Poisson regression for other outcomes.

99 Poisson regression identified clinical, laboratory and d

100 years of calendar time were estimated using Poisson regression incidence rate ratios (IRRs), with su

101 hospitalization for HF was estimated with a Poisson regression model adjusting for comorbidity and c

102 We used a Poisson regression model and calculated the HIV incidenc

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

104 2016 were analyzed, including a multivariate Poisson regression model of incidence rates.

105 We fitted a space-time Poisson regression model that accounted for the complex

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

107 We used a Poisson regression model to examine the associations bet

108 We used a generalized estimating equation Poisson regression model to examine the effect of each s

109 rom the Swedish Family Cancer Database and a Poisson regression model was applied to estimate relativ

110 A longitudinal Poisson regression model was estimated controlling for t

111 A zero-inflated Poisson regression model was used for data analysis.

112 A Poisson regression model was used to examine the associa

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

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

115 sitemia were identified using a multivariate Poisson regression model.

116 terms of incidence rate ratio (IRR) using a Poisson regression model.

117 .85) tertiles of consumption in the adjusted Poisson regression model.

118 alyses were performed using a random-effects Poisson regression model.

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

120 Rs) and incidence rate ratios (IRRs) using a Poisson regression model.

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

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

123 Poisson regression modeling identified female sex (RR, 1

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

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

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

127 uated this outcome as a discrete variable in Poisson regression models and as a categorical variable

128 cific associations using confounder-adjusted Poisson regression models and pooled the city-specific e

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

130 Poisson regression models estimated trends in HCV incide

131 Multivariable Poisson regression models examined admission risk factor

132 Multilevel Poisson regression models fitted the association of ging

133 Poisson regression models of 30-day postoperative mortal

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

135 ere is a well-established association) using Poisson regression models that controlled for shared sea

136 Using Poisson regression models that controlled for temporal c

137 HIV-infected and -uninfected children using Poisson regression models that incorporated HIV prevalen

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

139 ated, followed by bivariate and multivariate Poisson regression models to assess the relationship bet

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

141 We used Poisson regression models to estimate the association be

142 We used Poisson regression models to identify factors associated

143 We then used Poisson regression models to identify factors independen

144 We used multivariable, modified Poisson regression models to identify patient and clinic

145 and pandemic) and RSV infection by applying Poisson regression models to monthly all-respiratory and

146 In this study, quasi-Poisson regression models were applied to weekly age- an

147 Multivariate Poisson regression models were fitted to estimate incide

148 Univariate and multivariate Poisson regression models were used for analyses.

149 Time-series Poisson regression models were used to analyze the assoc

150 Poisson regression models were used to assess the interv

151 Poisson regression models were used to calculate adjuste

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

153 Poisson regression models were used to calculate standar

154 Poisson regression models were used to compare outcomes

155 Poisson regression models were used to estimate adjusted

156 Poisson regression models were used to estimate incidenc

157 Poisson regression models were used to estimate incidenc

158 Poisson regression models were used to estimate the age-

159 Multivariable Poisson regression models were used to evaluate the simu

160 ed according to a multi-component scale, and Poisson regression models were used to examine associati

161 Joinpoint and Poisson regression models were used to test for temporal

162 Cox and Poisson regression models were used.

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

164 Hierarchical modified Poisson regression models with practice site as a random

165 Multivariable Poisson regression models with robust error estimates we

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

167 elative risks were calculated using modified Poisson regression models.

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

169 ation (0-5 and >/=6 months) was studied with Poisson regression models.

170 5% confidence intervals were estimated using Poisson regression models.

171 with history of falls was investigated using Poisson regression models.

172 he period 1981-2000 using log-rank tests and Poisson regression models.

173 t use of antidepressants were estimated with Poisson regression models.

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

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

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

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

178 ctious disease incidence was evaluated using Poisson regression models.

179 ries was estimated by multivariable modified Poisson regression models.

180 sed with multivariable hierarchical modified Poisson regression models.

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

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

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

184 Gender-stratified multivariable poisson regression of 151,209 women and 68,890 men were

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

186 ents with the CC or CT genotype (2.4-fold by Poisson regression [P<.0001] and 2.7-fold based on mean

187 ere was an increase in annual numbers of FK (Poisson regression, P = .0001).

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

189 motherapy for non-Hodgkin lymphoma (n = 158; Poisson regression Ptrend < .001), declined for ovarian

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

191 Multivariable Poisson regression showed a positive association between

192 5 years was investigated using multivariate Poisson regressions stratified according to initial Body

193 een cART regimens and KS using multivariable Poisson regression, stratified or adjusted for timing ar

194 Multivariable Poisson regression survival models and Cox analyses were

195 Using multivariable Poisson regression, the authors examined the association

196 After adjusting for confounding factors in Poisson regression, the relationship between linezolid u

197 als (CI) were estimated using random effects Poisson regression to account for clustering within gene

198 Using weighted multivariate modified Poisson regression to address generalizability of findin

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

200 We used Poisson regression to assess whether the numbers of case

201 We used Poisson regression to calculate incidence rates of demen

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 idence rate ratios (IRRs) were calculated by Poisson regression to determine differences in GW rates

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

210 We used Poisson regression to estimate crude prevalence and crea

211 We used a Poisson regression to estimate excess relative risk (ERR

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 on to estimate the incidence of diabetes and Poisson regression to estimate mortality.

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

216 We used Poisson regression to estimate the prevalence of RRT for

217 Using modified Poisson regression to estimate the relative risk of chil

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

219 in number of deaths and place of death, and Poisson regression to evaluate factors associated with c

220 onfidence intervals were calculated by using Poisson regression to evaluate lifetime use of 48 pestic

221 Predictive validity was assessed using Poisson regression to examine associations among admissi

222 infants using propensity scores, and we used Poisson regression to examine the effect of postnatal CM

223 We used descriptive measures and Poisson regression to identify factors associated with s

224 We used multilevel Poisson regression to model the relationship between DTC

225 We used multivariable Poisson regression to quantify the risk of not participa

226 We performed a time-series analysis using Poisson regression to relate monthly CFP call incidence

227 rental educational and employment status, by Poisson regression, to compare individuals with and with

228 Poisson regression was conducted to determine if antimic

229 Poisson regression was employed to determine the indepen

230 Poisson regression was used to analyze overall and subgr

231 Poisson regression was used to analyze the relation betw

232 Poisson regression was used to assess between-group diff

233 Poisson regression was used to assess differences betwee

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

235 l intake and hormone concentrations, whereas Poisson regression was used to assess RR of cycle-averag

236 Modified Poisson regression was used to assess sex differences in

237 Poisson regression was used to calculate crude and adjus

238 Poisson regression was used to calculate incidence rates

239 Poisson regression was used to calculate the incidence r

240 Poisson regression was used to compare incidence rates o

241 Poisson regression was used to compare rates between dia

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

243 Poisson regression was used to compute relative risks (R

244 Poisson regression was used to develop a risk score, ext

245 Generalized Poisson regression was used to estimate association betw

246 Poisson regression was used to estimate incidence rates.

247 Log-linear Poisson regression was used to estimate mortality rate r

248 Poisson regression was used to estimate prevalence ratio

249 Poisson regression was used to estimate rate ratios (RRs

250 Poisson regression was used to estimate relative risks (

251 Poisson regression was used to estimate relative risks (

252 Poisson regression was used to estimate the associations

253 Poisson regression was used to estimate the incidence ra

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

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

256 A quasi-Poisson regression was used to identify irregular cluste

257 Poisson regression was used to identify predictors of ES

258 Poisson regression was used to model dementia incidence

259 Conditional fixed-effects Poisson regression was used to model incidence rate rati

260 Mixed-effects Poisson regression was used to test the hypothesis that

261 Through poisson regression we calculated relative risks (RRs) fo

262 Using Poisson regression, we assessed the association between

263 Using Poisson regression, we calculated adjusted relative risk

264 Using Poisson regression, we compared risk of newly detected i

265 Using Poisson regression, we estimated incidence rate ratios f

266 Using city-season specific Poisson regression, we estimated PM2.5 effects on approx

267 Using multilevel Poisson regression, we examined temporal trends in risk-

268 Using a multilevel Poisson regression, we quantified ICU mortality-relative

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

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

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

272 Poisson regressions were applied to a Medicare populatio

273 Multivariable Poisson regressions were used to test the association be

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

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

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

277 isorder outcome were estimated by log linear Poisson regression with adjustments for the calendar per

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

279 We used Poisson regression with and without adjustment for ozone

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

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

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

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

284 Using Poisson regression with generalized estimating equations

285 Poisson regression with generalized estimating equations

286 Analysis included modified Poisson regression with generalized estimating equations

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

288 A Poisson regression with robust error variance was used t

289 tus and neonatal mortality, calculated using Poisson regression with robust error variance.

290 Multivariate Poisson regression with robust standard errors was used

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

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

293 012 and 2015 for several risk factors, using Poisson regression with robust variance and a bootstrap-

294 Poisson regression with robust variance estimation provi

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

296 We used Poisson regression with robust variance to identify dete

297 f HIV shedding were estimated using modified Poisson regression with robust variance.

298 We used Poisson regression with robust variances to derive incid

299 bers of hospitalizations were compared using Poisson regression with time offset.

300 ces (IRDs) of condyloma were estimated using Poisson regression with vaccine dose as a time-dependent