ライフサイエンスコーパス: binomial

1 CIs are binomial 95% CI.

2 expectation least square (ELS) algorithm and binomial analysis of three-point gametes (BAT) for estim

3 We used log-binomial and multinomial regression to calculate adjuste

4 ar model and the generalized logit model for binomial and multinomial variables adjusted for age, sex

5 antities such as heritability of traits with binomial and Poisson distributions are special cases of

6 an areas in the United States using negative binomial and Poisson regression models.

7 ed versus count-based (particularly Negative-Binomial-based) models for eQTL mapping.

8 del is comparable to the celebrated Negative Binomial, but much easier to estimate.

9 The relationship between the negative binomial classifier and the Poisson classifier is explor

10 d the generalized linear model with negative binomial count distribution, not zero-inflated, as a sui

11 pooled using procedures for meta-analysis of binomial data and analysed using random-effects models.

12 g random-effects models for meta-analyses of binomial data.

13 on model was performed using fatality as the binomial dependent variable and treatment in a US-MTF or

14 scribed by Taylor's law (TL) or the negative binomial distribution (NBD).

15 dure for power estimation using the negative binomial distribution and assuming a generalized linear

16 We calculated 95% CIs assuming a binomial distribution and did random-effects meta-regres

17 ighted random-intercept models with negative binomial distribution and logistic-regression models to

18 terpretation for features using a correlated binomial distribution and scales efficiently to analyze

19 er estimates from the zero-inflated negative binomial distribution are an unreliable indicator of zer

20 The Poisson distribution and negative binomial distribution are commonly used to model count d

21 ression with the use of a bivariate negative binomial distribution for paired designs.

22 parameter [Formula: see text] of a negative-binomial distribution is estimated at 0.06 and 0.2 for J

23 rediction using deep neural networks and the binomial distribution model.

24 with being drawn from an underlying negative binomial distribution than either a log-normal distribut

25 ditive model framework based on the negative binomial distribution that allows flexible inference of

26 and brings them together under a correlated binomial distribution to create an efficient hypothesis

27 as additional mechanistic complexity and the binomial distribution was no longer valid.

28 Spectral counts modeled as a negative binomial distribution were used for statistical comparis

29 eneralized estimating equation models with a binomial distribution were used to study longitudinal as

30 on may not be as appropriate as the negative binomial distribution when biological replicates are ava

31 The model uses the negative binomial distribution with gamma priors to model sequenc

32 ralized linear mixed model assuming negative binomial distribution with log link function on 3-time r

33 major-component distribution is similar to a binomial distribution with low error and low reference b

34 cell-free HIV-1 infection follows a negative-binomial distribution, and our model reproduces these da

35 logues in the 95%(13)C extracts, follows the binomial distribution, showing mirrored peak pairs for t

36 d retention in the mother cell) according to binomial distribution, thus limiting equal segregation o

37 assessed by using the Student t test, exact binomial distribution, two-sample test of proportions, a

38 RISPRBetaBinomial or CB(2) Based on the beta-binomial distribution, which is better suited to sgRNA d

39 ell doublets are modeled by employing a Beta-binomial distribution.

40 of DGE tools that are based on the negative binomial distribution.

41 ting the secondary case data with a negative binomial distribution.

42 th more closely than the often-used negative binomial distribution.

43 epsilona values obeyed laws of binomial distribution.

44 t size, due to the properties of the Poisson-binomial distribution.

45 hurdle regression models using the negative binomial distribution.

46 ed generalized logistic, gamma, and negative binomial distributions as models for compound behavior.

47 seq data by sex revealed underlying negative binomial distributions which increased statistical power

48 ions, QNB is based on 4 independent negative binomial distributions with their variances and means li

49 covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent ne

50 on the translation of rate comparison to two binomial distributions.

51 tribution (e.g., Gaussian, Poisson, negative binomial, etc.), which may not be well met by the datase

52 imated dispersion parameters in the negative binomial framework.

53 Negative binomial generalized estimating equations (GEEs) were us

54 we propose a Bayesian hierarchical negative binomial generalized linear mixed model framework that c

55 We used a binomial generalized linear model (log-binomial model) t

56 ng generalized linear model (BGLM), Negative binomial generalized linear model (NBGLM), Boosting gene

57 nd to specific taxon abundances, by negative binomial generalized linear models.

58 ed on annualized and trend-adjusted Negative Binomial Harmonic Regression (NBHR) models augmented wit

59 then compares methylation levels using beta-binomial hierarchical modeling and Wald tests.

60 data better than either ANOVA or a Negative Binomial (in a generalized linear model).

61 Negative binomial incident rate ratio for falls was 1.07 (95% CI

62 Binomial logistic regression and Cox proportional hazard

63 of different SUA levels were estimated by a binomial logistic regression model.

64 Binomial logistic regression was used to analyze correla

65 raits, and non-native species richness using binomial logistic regression.

66 ress the above issues for linear (Gaussian), binomial (logistic), and multinomial GLMs.

67 oint modeling framework that uses a negative binomial mixed effects model to determine longitudinal t

68 s show that, among the methods, the negative binomial mixed model (NB-fit), compound Poisson mixed mo

69 We fitted a binomial mixed model combining the site-level podoconios

70 We propose a fast zero-inflated negative binomial mixed modeling (FZINBMM) approach to analyze hi

71 In this article, we propose negative binomial mixed models (NBMMs) for detecting the associat

72 approach is based on zero-inflated negative binomial mixed models (ZINBMMs) for modeling longitudina

73 ods, including linear mixed models, negative binomial mixed models and zero-inflated Gaussian mixed m

74 inomial mixed models, zero-inflated negative binomial mixed models, and zero-inflated Gaussian mixed

75 unctions for setting up and fitting negative binomial mixed models, zero-inflated negative binomial m

76 In this article, we propose a negative binomial mixed-effect model (NBMM) to identify DE genes

77 erefore, the PMM is replaced by the negative binomial mixed-effects model (NBMM).

78 Using a negative binomial mixed-effects regression model we evaluated the

79 e non-CpG DNA methylation at telomere fits a binomial model and may result from a random process with

80 zero-inflated Poisson model and the negative binomial model can provide unbiased and consistent estim

81 eveloped a new classifier using the negative binomial model for RNA-seq data classification.

82 sence) gene expression data and, employing a binomial model for the co-expression of pairs of genes,

83 ntly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome

84 The method's likelihood function follows a binomial model of mRNA capture, while priors are estimat

85 eneral practice appointment using a negative binomial model offset by number of appointments made.

86 We apply a zero-inflated negative binomial model previously used to standardize catch per

87 rWeele provided do not correspond to the log-binomial model specified by these authors for the mediat

88 e introduction was estimated from a negative binomial model that adjusted for secular trend, seasonal

89 We constructed a binomial model to investigate the association between a

90 were modelled using a zero-inflated negative binomial model using age, sex, smoking, and FEV(1) % pre

91 catchment area (HFCA) level, with a negative binomial model using generalized estimating equations.

92 A multilevel negative binomial model was fitted to the data, which showed that

93 itionally, a stepwise zero-inflated negative binomial model was used to assess predictors of exacerba

94 A log-binomial model was used to estimate the association of d

95 A log-binomial model was used to estimate the association of d

96 A log-linear binomial model was used to estimate the relative risk of

97 PRs) of each phenotype of asthma using a log-binomial model with 95% CIs.

98 these findings, we propose a desirable beta-binomial model with a dynamic overdispersion rate on the

99 ochimerism as a rate via Poisson or negative binomial model with the rate of detection defined as a c

100 sed a binomial generalized linear model (log-binomial model) to examine the association of SOT status

101 nto the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersi

102 nstruct the classifier by fitting a negative binomial model, and propose some plug-in rules to estima

103 According to a negative binomial model, the mean time to resolution of bacteremi

104 Using a negative binomial model, we investigated the effect of IPTp-SP do

105 indirect effects that truly pertain to a log-binomial model.

106 nt hHF events were analyzed using a negative binomial model.

107 variables and in-hospital mortality in a log-binomial model.

108 eloped an efficient method oxBS-MLE based on binomial modeling of paired bisulfite and oxidative bisu

109 We used negative binomial modeling to determine whether there were differ

110 In multivariate negative binomial modeling, 26 bacterial taxa were differentially

111 were identified using zero-inflated negative binomial modeling.

112 tamivir with mortality was assessed with log-binomial models and a competing risks analysis estimatin

113 For the period 1997-2016, negative binomial models estimated associations between weekly co

114 were calculated using multivariable negative binomial models for 2 metrics: days of therapy (DOT) per

115 were calculated using multivariable negative binomial models for two metrics: days of therapy (DOT)/1

116 We estimated panel-negative binomial models on a subset of beneficiaries to compare

117 (REDITs), a suite of tests that employ beta-binomial models to identify differential RNA editing.

118 We utilized zero-truncated negative binomial models to identify triggers associated with inh

119 Mixed effects models and log binomial models were used to assess the association of m

120 ence-in-differences, zero-inflated, negative-binomial models were used to evaluate the impact of stre

121 Negative binomial models were used to examine the associations of

122 Negative binomial models were used with year and county-level fix

123 We used log binomial models with generalized estimating equations to

124 We compared relapse rates with negative binomial models, and estimated cumulative hazards with c

125 hout prevalent AF/AFL using Cox and negative binomial models, respectively.

126 lization rates were estimated using negative binomial models.

127 Correlates of MAN were assessed by using log-binomial models.

128 Chi-square tests and a log-binomial multivariable model were used to compare treatm

129 Alternatively, binomial N-mixture models enable abundance estimation fr

130 Although Negative Binomial (NB) regression has been generally accepted in

131 and secondary outcomes were derived using a binomial-normal random-effects model.

132 ulated sequencing data under either negative binomial or compound Poisson mixed models, are provided

133 re significant in linear (P = 0.005) and log-binomial (P = 0.015) models, which were then stratified.

134 the intervention on RTAs by use of negative binomial panel regression and on alcohol consumption out

135 a hurdle model, which is a mixture between a binomial peptide count and a peptide intensity-based mod

136 Burden test and binomial performance deviation analysis also converged s

137 ome) of the conventional burden test and the binomial performance deviation analysis overlapped signi

138 was assessed, and in addition, an algorithm (binomial performance deviation analysis) was established

139 Latin binomials, popularised in the 18th century by the Swedis

140 The MNELN was estimated using the binomial probability law.

141 We propose a simple binomial probability metric to ascertain translation pro

142 variable, which is then used to predict the binomial probability of successful quantitative analysis

143 inpatient treatment were calculated, and 95% binomial proportion CIs were obtained using the Clopper-

144 % (95% CI, 13% to 36%; one-sided P = .03) by binomial proportional estimate using the prespecified en

145 2% (95% CI, 28% to 56%; one-sided P = .9) by binomial proportional estimate using the prespecified en

146 ical analyses were performed by using simple binomial proportions to quantify sensitivity, specificit

147 ated by using a test for differences between binomial proportions.

148 and subsequent) revascularizations (negative binomial rate ratio, 0.64 [95% CI, 0.56-0.74]; P<0.0001)

149 lysis (clinical studies) or multivariate log-binomial regression (surveys) to obtain summarised dose-

150 -high, highest) using zero-inflated negative binomial regression (total charges).

151 HiC-DC uses hurdle negative binomial regression account for systematic sources of va

152 Negative binomial regression analyses showed an association betwe

153 We conducted negative binomial regression analyses, including city as a random

154 sis for remission and zero-inflated negative binomial regression analysis for alcohol consumption.

155 Negative binomial regression analysis indicated that the particip

156 Log-binomial regression analysis was used to estimate relati

157 for all persons aged 60 y and over; negative binomial regression analysis was used to estimate the ti

158 Zero-inflated negative binomial regression analysis was used to test for an ass

159 ing the independent t test, Wald chi(2), and binomial regression analysis.

160 Negative binomial regression and Andersen-Gill analyses which inc

161 and birth cohort were modeled using negative binomial regression and change-point methods.

162 he primary outcome and was analysed with log-binomial regression and General Estimating Equations to

163 We used log-binomial regression and generalized estimating equations

164 workers and non-shift workers using negative binomial regression and linear mixed-model analysis.

165 We used negative binomial regression and multilevel logistic regression t

166 We did negative binomial regression and principal component analyses to

167 iatric patient-care locations using negative binomial regression applied to nationally aggregated AU

168 Using interval-censored survival and binomial regression approaches a multi-model framework w

169 Negative binomial regression demonstrated that older adults with

170 Log-binomial regression evaluated associations with adverse

171 Using a negative binomial regression fitted to egg count data, we found t

172 We used negative binomial regression for numbers of moderate and severe e

173 tential confounders, we developed a negative binomial regression framework for uniformly processing S

174 ts were examined separately using multilevel binomial regression in which within-unit changes in pres

175 led for biomarker associations with negative binomial regression including clinical covariates (age,

176 ore than 100 residents represented, negative binomial regression indicated that infection risk differ

177 Log binomial regression methods were used to assess associat

178 intervals (CI) were estimated using negative binomial regression model and Poisson regression model w

179 Whitney test; Spearman's correlation and log-binomial regression model estimated the association betw

180 Using a propensity-adjusted log-binomial regression model stratified by type of surgical

181 ic Surveillance System, we fitted a negative binomial regression model to estimate the change in mort

182 uscitation registry, we developed a negative binomial regression model to estimate the incidence of i

183 A stratified log-binomial regression model was used to estimate relative

184 ion indicated a significant difference), and binomial regression model were used to analyze differenc

185 general experimental design, based on a beta-binomial regression model with 'arcsine' link function.

186 3 (July 2013-December 2016) using a negative binomial regression model, adjusting for seasonality.

187 d relapse rate was assessed using a negative binomial regression model.

188 etaregression was performed using a negative binomial regression model.

189 We used negative binomial regression modeling to determine whether daily

190 We used negative binomial regression modeling to identify healthcare faci

191 ery and the risk of birth defects, using log-binomial regression models adjusted for maternal age, co

192 This study used negative binomial regression models and geolocated gun homicide i

193 ability of treatment-weighted linear and log-binomial regression models and pooled using a random-eff

194 Negative binomial regression models estimated effect of age at on

195 included descriptive statistics and negative binomial regression models to assess correlates of PrEP

196 Binomial regression models were used to assess eosinophi

197 Adjusted negative binomial regression models were used to calculate the ra

198 Multivariate log-binomial regression models were used to estimate relativ

199 Negative binomial regression models were used to evaluate baselin

200 Multivariable binomial regression models were used to evaluate the eff

201 Log-binomial regression models were used to examine trends i

202 ends in rates were determined using negative binomial regression models with procedure count as the d

203 In binomial regression models, PSs indexing 6 risk factors

204 Using Cox and binomial regression models, we compared the 2 randomizat

205 g bivariable Poisson, Fine and Gray, and log-binomial regression models.

206 ted populations were estimated with negative binomial regression models.

207 using bivariable Poisson, Fine-Gray, and log-binomial regression models.

208 n prevalence of these risk factors using log binomial regression models.

209 ons and attributes were included in negative binomial regression models.

210 We used negative binomial regression to account for correlations among re

211 Negative binomial regression to account for multiple primary even

212 comes, except for falls, which used negative binomial regression to allow for multiple events, adjust

213 We then used zero-inflated negative binomial regression to analyze age-adjusted associations

214 We performed negative binomial regression to assess factors associated with L-

215 f sepsis and severe sepsis and used negative binomial regression to assess for trends over time.

216 f sepsis and severe sepsis and used negative binomial regression to assess for trends over time.

217 We used log-linear binomial regression to estimate risk ratios (RRs) and 95

218 es of antimicrobial resistance, and negative binomial regression to examine trends in icidence of blo

219 We used binomial regression to identify characteristics independ

220 We used log-binomial regression to identify determinants of beta-HPV

221 We used negative-binomial regression to model the association of cigarett

222 (95% CI) of pre-eclampsia and GHTN with log-binomial regression using generalized estimating equatio

223 Negative binomial regression was used to analyze changes from bas

224 Log-binomial regression was used to assess the association b

225 Multivariable log-binomial regression was used to assess the associations

226 Log-binomial regression was used to calculate unadjusted and

227 Log-binomial regression was used to estimate prevalence rati

228 tionships between time and outcome; negative binomial regression was used to evaluate effects on work

229 Multivariable logistic and negative binomial regression was used to evaluate the impact of t

230 Binomial regression was used to examine associations bet

231 Multivariate log-binomial regression was used to investigate the associat

232 Negative binomial regression was used to model the number of anti

233 Negative-binomial regression was used to relate antibiotic use to

234 Linear time trends were compared by negative binomial regression with a log link function.

235 e restrictions was assessed using a negative binomial regression with generalized estimating equation

236 restrictions were assessed using a negative binomial regression with generalized estimating equation

237 alyses (logistic and zero-truncated negative binomial regression) were conducted adjusting for socio-

238 ile on initial DMT was modeled with negative binomial regression, adjusted for PS-quintile.

239 We evaluated these endpoints using log-binomial regression, adjusting for the imbalanced baseli

240 linear regression, logistic regression, beta-binomial regression, and Bayesian estimation.

241 earm death rates were analyzed with negative binomial regression, and data on firearm-related mass ki

242 lytic streptococci were calculated using log-binomial regression, controlling for age, transfer statu

243 eptible isolates was estimated with negative-binomial regression, overall and per genotype.

244 Using log-binomial regression, the corresponding unadjusted risk r

245 In a negative binomial regression, time to recovery was 60% to 95% lon

246 he vaccine efficacy, as assessed by negative binomial regression, was 4.4% (95% confidence interval [

247 Using negative binomial regression, we determined characteristics assoc

248 Using negative binomial regression, we modeled the read depth signal wh

249 Pearson residuals from "regularized negative binomial regression," where cellular sequencing depth is

250 We developed a negative binomial regression-based Integrative Method for mutatio

251 specific outcomes were modeled with negative binomial regression.

252 s were analyzed using multivariable negative binomial regression.

253 d differences in total events using negative binomial regression.

254 confidence intervals, was assessed with log binomial regression.

255 ion, and length of stay (LOS) using negative binomial regression.

256 on and weighted risk differences (RDs) using binomial regression.

257 ion, and length of stay (LOS) using negative binomial regression.

258 RP) of AD was calculated by using log-linear binomial regression.

259 ression and delirium duration using negative binomial regression.

260 8-day mortality using multivariable negative binomial regression.

261 f all-cause 30-day postoperative death using binomial regression.

262 ibroid prevalence and tumor number using log-binomial regression.

263 calculated adjusted relative risks using log-binomial regression.

264 aracteristics were modeled by using negative binomial regression.

265 dB/year) progression were compared using log-binomial regression.

266 lyzed as detectable or undetectable with log-binomial regression.

267 tum were evaluated by using multivariate log-binomial regression.

268 ps using multivariable logistic and negative binomial regression.

269 ive risk (RR) of ASD was estimated using log-binomial regression.

270 organs or regions and compared with negative binomial regression.

271 dings from a colonoscopy with the use of log binomial regression.Overall, 3340 participants (20.4%) h

272 589), was associated with LOS (LOS: negative binomial regression; LOS >/=2 weeks: logistic regression

273 Using mixed-effects negative binomial regressions accounting for time trends and clus

274 tic regressions, and zero-truncated negative binomial regressions were applied.

275 equentist models (using Poisson and negative binomial regressions), and several Bayesian models.

276 s and diarrhea and [Formula: see text] using binomial regressions, adjusting for potential confounder

277 ed interactions and conducted linear and log-binomial regressions.

278 zed linear regression (logistic and negative binomial, respectively), and yearly trends in different

279 yzed by using logistic (asthma) and negative binomial (respiratory symptoms) regressions, adjusting f

280 Here, we explore another method, inverse binomial sampling (IBS), which can estimate the log-like

281 we first show that our method, based on beta-binomial sampling, accurately recovers transmission bott

282 commercialization were compared by negative binomial segmented regression models.

283 nstrate that the tool utilizing networks and binomial statistical tests can identify interesting stru

284 und behavior in HTS, we assessed an existing binomial survivor function (BSF) model of "frequent hitt

285 With modeling and real datasets, the exact binomial test (EBT) showed an advantage in balancing the

286 M5B, NSD2, FOXP1, MED13L, DYRK1A; one-tailed binomial test P <= 4.08E-05) contributed to the connecto

287 ar to that of random guess from a one-sample binomial test.

288 s evaluated by using a Clopper-Pearson exact binomial test.

289 ds, e.g. chi 2 test, Fisher's exact test and Binomial test.

290 riant and reference read counts, followed by binomial tests for genotype and allelic status at SNV po

291 with high BPE (moderate or marked) by using binomial tests of proportions.

292 Statistical comparisons were made with exact binomial tests or repeated-measures analysis of variance

293 reader and for readers combined using exact binomial tests.

294 es a novel reparametrization of the negative binomial to provide flexible generalized linear models (

295 e hierarchical Poisson models (e.g. negative binomial) to model independent over-dispersion, but do n

296 er, it is more complicated to model negative binomial variables because they involve a dispersion par

297 e read depth within a region is a mixture of binomials, which in simulations matches the read depth m

298 hannels, the Fab-HA binding distribution was binomial with a maximum of three Fab-HA bound.

299 re quite different, a zero-inflated negative binomial (ZINB) model could reasonably explain the PvDMF

300 e conditions based on Zero-Inflated Negative Binomial (ZINB) regression.