ライフサイエンスコーパス: binomial

1 linical outcome in 73 % of dogs (p = 0.0262, binomial).

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

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

6 Negative binomial and zero-inflated negative binomial regression

7 and biological variation by utilizing a beta-binomial approach across biological samples for a CpG si

8 generalized linear mixed model with a quasi-binomial approach was used to examine associations betwe

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

10 this database were updated using a negative binomial Bayesian meta-regression tool for 187 countries

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

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

13 ution of all the metabolites should give the binomial coefficients found in Pascal's triangle.

14 Sensitivity, specificity, and binomial confidence intervals were calculated for the pr

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

16 ne expression analysis based on the negative binomial distribution (DESeq) or Empirical analysis of D

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

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

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

20 CANOES models read counts using a negative binomial distribution and estimates variance of the read

21 simulation experiments based on the negative binomial distribution and our proposed nonparametric sim

22 ome among biological samples with a negative binomial distribution and uses a local variance estimati

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

24 rst embryonic cleavage division, following a binomial distribution pattern.

25 By using the Binomial distribution rather than a normal approximation

26 mutation counts of the elements with a beta-binomial distribution to handle overdispersion.

27 empirical Bayesian method based on the beta-binomial distribution to model paired data from high-thr

28 The binomial distribution used to test hypotheses about sequ

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

30 s in the data, a single GAM using a negative binomial distribution was suitable to make predictions o

31 A beta-binomial distribution was used to estimate the probabili

32 zed estimating equations assuming a negative binomial distribution were used to estimate relative rat

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

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

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

36 pression in RNA-seq data based on a negative binomial distribution, and in paired data based on a bet

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

38 By using binomial distribution, Clopper-Pearson confidence interv

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

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

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

42 a likelihood function based on the negative binomial distribution, use a regularization approach to

43 particular, we consider weights based on the binomial distribution, where the median of the p-values

44 d on statistical models such as the negative binomial distribution, which is employed by the tools ed

45 ribution, and in paired data based on a beta-binomial distribution.

46 termined by using an exact method based on a binomial distribution.

47 ng generalized linear models with a negative binomial distribution.

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

49 -Seq reads were assumed to follow a negative binomial distribution.

50 epsilona values obeyed laws of binomial distribution.

51 hedral species, which do not follow a simple binomial distribution.

52 -Seq reads were assumed to follow a negative binomial distribution.

53 Exact binomial distributions were used to establish 95% confid

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

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

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

57 consistent with both log-normal and negative binomial distributions, while the mean-variance relation

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

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

60 s a generalized linear model of the negative binomial family to characterize count data and allows fo

61 imated dispersion parameters in the negative binomial framework.

62 at was less than or equal to 45% using a log binomial generalised linear model it was found that part

63 ase of seroprotection was modeled with a log binomial generalized linear model, and data were pooled

64 We compared a range of negative binomial generalized linear models fitted to the meningi

65 d with active season adult survival rates in binomial generalized linear models.

66 ing counts are described by a lognormal-beta-binomial hierarchical model, which provides a basis for

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

68 Braun and Schmidt assert that the binomial is more appropriate for analysing the data than

69 ne expression and combine that with negative binomial likelihood for the count data.

70 we used random-effects models with the exact binomial likelihood method.

71 The parameters, P and Q, of this binomial likelihood model can be inferred using slow sam

72 rk meta-analysis of published trials using a binomial likelihood model to assess the risk of serious

73 oss the landscape by maximizing a product of binomial likelihoods penalized by nearest neighbor inter

74 In this paper, we propose a negative binomial linear discriminant analysis for RNA-Seq data.

75 A negative binomial log linear regression model was fitted to the d

76 eonates with normal Apgar score (7-10) using binomial log-linear modelling with adjustment for confou

77 We performed multivariate stepwise binomial logistic regression analyses to study clinical

78 However, binomial logistic regression analysis identified periphe

79 Binomial logistic regression and Cox proportional hazard

80 Binomial logistic regression models were also fitted usi

81 ursing assistants using chi square tests and binomial logistic regression models.

82 ace, and other underlying conditions through binomial logistic regression.

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

84 ns of an extension of the maximum-likelihood-binomial method for quantitative trait loci to multivari

85 We further compare different Negative Binomial methods with a recently-described zero-inflated

86 A hierarchical negative binomial mixed effects model tested the relationship bet

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

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

89 We built negative binomial mixed models to examine health-system factors a

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

91 Using a binomial mixture model, the BEAT package aggregates meth

92 With a negative binomial model adjusted for site, the event rate for the

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

94 ILI for each school district, and a negative binomial model compared three levels of school closure:

95 uencing read count data (based on a Negative Binomial model for instance) are already available in RN

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

97 -dispersion, and in such cases, the negative binomial model has been used as a natural extension of t

98 roaches; thus, we conclude that the negative binomial model may be most appropriate for analyzing qua

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

100 The negative binomial model remains the more accessible of these 2 ap

101 In this article, we use a mixture of binomial model to characterize bisulfite-sequencing data

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

103 Furthermore, a nonclassical negative-binomial model was shown to correctly describe the inter

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

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

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

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

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

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

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

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

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

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

114 ity outcomes used paired, weighted, negative binomial models or frailty proportional hazards models a

115 We devised beta-binomial models to characterize methylation data around

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

117 Multilevel negative binomial models were used to assess changes in catheter

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

119 Log-binomial models were used to estimate risk ratios (RRs)

120 Multivariable negative binomial models were used to examine factors associated

121 Negative binomial models were used to examine the associations of

122 Zero-inflated negative binomial models with robust standard errors clustered on

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

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

125 ng generalized estimating equations with log-binomial models.

126 he introduction of PCV13 and used a negative binomial multiple regression model to estimate how much

127 n legislation in effect by means of negative binomial multivariable estimation with state and time fi

128 A negative binomial multivariable model was used to control for pot

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 ASE testing is implemented through binomial or beta-binomial tests of sequence read counts

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

134 previously reported by genome-wide studies (binomial p = 0.0009).

135 lly consistent effect estimates in our GWAS (binomial P = 9.7 x 10(-7)), five of which reached genome

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

137 ost statistical support for a combination of binomial partitioning of mtDNAs at cell divisions and ra

138 Using a conventional binomial probability model, several genes were found mut

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

140 on a change-point model on a bivariate mixed Binomial process, which explicitly models the copy numbe

141 e I error) of 0.05 using an exact test for a binomial proportion.

142 % (95% CI, 13% to 36%; one-sided P = .03) by binomial proportional estimate using the prespecified en

143 2% (95% CI, 28% to 56%; one-sided P = .9) by binomial proportional estimate using the prespecified en

144 Binomial proportions and exact 95% confidence intervals

145 edefined sensitivity analysis using negative binomial regression (0.823 vs 0.959; 0.858 [0.740-0.995]

146 deployment CAPS using zero-inflated negative binomial regression (ZINBR), a procedure designed for di

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

148 Rs were assessed using multivariate negative binomial regression adjusted for sex, age group, time to

149 Negative binomial regression analyses showed an association betwe

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

151 Negative binomial regression analysis indicated that the particip

152 gistic regression and zero-inflated negative binomial regression analysis of participants in the Nati

153 Negative binomial regression analysis tested direct and synergist

154 examine time trends in suicide, and negative binomial regression analysis to study sex- and age-speci

155 Log-binomial regression analysis was used to estimate relati

156 Log binomial regression analysis was used to estimate the as

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

158 In log-binomial regression analysis, age <37 years (adjusted pr

159 Using multivariate repeated measures log-binomial regression analysis, we examined associations o

160 ltiple allergic disorders were tested in log-binomial regression analysis.

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

162 were estimated with a zero-inflated negative binomial regression and a hurdle model, respectively.

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

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

165 We used log-binomial regression and generalized estimating equations

166 Negative binomial regression and tooth-specific logistic regressi

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

168 In this work we investigated the use of beta-binomial regression as a general approach for modeling w

169 Negative binomial regression demonstrated that older adults with

170 hich uses a hidden Markov model and negative binomial regression framework to identify regions of dis

171 tes of suicide were estimated using negative binomial regression incidence rate ratios (IRRs).

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

173 In the multivariate logarithm-binomial regression model adjusted for baseline cardiova

174 -month follow-up period, analysed with a log-binomial regression model adjusted for stratification fa

175 and all-cause) using a multivariate negative binomial regression model of monthly hospitalization rat

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

177 antimicrobial utilization, using a negative binomial regression model to assess the impact of the in

178 At both ages, using a beta-binomial regression model to control for potential confo

179 to US Census Bureau data and used a negative binomial regression model to evaluate the significance o

180 A log-binomial regression model was used to calculate the rela

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

182 services; these were entered into a negative binomial regression model with variables to control for

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

184 etaregression was performed using a negative binomial regression model.

185 We used negative-binomial regression modeling to estimate the incidence o

186 We used negative binomial regression modeling to identify healthcare faci

187 In multivariate negative binomial regression models adjusted for age, gender, rac

188 The negative binomial regression models also indicated that responden

189 cular trends (period effects) using negative binomial regression models and for birth cohort effects

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

191 Log-binomial regression models identified factors associated

192 Negative binomial regression models showed an independent associa

193 Multilevel negative binomial regression models were then used to investigate

194 Log-binomial regression models were used for statistical ana

195 Negative binomial regression models were used to analyze the coun

196 Negative binomial regression models were used to assess the assoc

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

198 Negative binomial and zero-inflated negative binomial regression models were used to estimate inciden

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

200 Log-binomial regression models were used to estimate the ass

201 Negative binomial regression models were used to estimate the inc

202 Negative binomial regression models were used to evaluate baselin

203 Multivariable binomial regression models were used to evaluate the eff

204 o test for linear trends and lagged negative binomial regression models were used to model the interr

205 Multivariable log-binomial regression models with generalized estimating e

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

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

208 Using negative binomial regression models, we estimated the incidence r

209 ects logistic regression models and negative binomial regression models.

210 ti-Ethnic Study of Atherosclerosis using log-binomial regression models.

211 ted populations were estimated with negative binomial regression models.

212 ounders and cluster by mixed-effect negative binomial regression on all malaria attacks for both year

213 We used log-binomial regression to calculate adjusted relative risks

214 We used multivariable linear and log-binomial regression to calculate effect estimates and 95

215 We used negative binomial regression to estimate crude and age-, sex-, an

216 We used binomial regression to estimate risk ratios (RRs) and ri

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

218 We used binomial regression to identify characteristics independ

219 We used log-binomial regression to model the proportion of breast ca

220 trends in sexual behavior, we used negative binomial regression to model the relationship between ti

221 Negative binomial regression was used for the primary analysis.

222 Multilevel negative binomial regression was used to analyse all-cause and no

223 Negative binomial regression was used to analyze changes from bas

224 Negative binomial regression was used to analyze incidence and so

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

226 Negative binomial regression was used to assess the association b

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

228 Log-binomial regression was used to calculate unadjusted and

229 Multivariate negative binomial regression was used to compare CT usage among d

230 Negative binomial regression was used to compare outcome exacerba

231 Negative binomial regression was used to estimate county-level su

232 Binomial regression was used to estimate crude and adjus

233 Log-binomial regression was used to estimate prevalence rati

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

235 Binomial regression was used to examine associations bet

236 Negative binomial regression was used to examine the relationship

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

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

239 Binomial regression with a log link function and robust

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

241 Linear and binomial regression with generalized estimating equation

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

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

244 ratios (RRs) were calculated using negative binomial regression, controlling for emotional and behav

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

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

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

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

249 The primary analysis method was a negative binomial regression, with the number of copies as the ou

250 calculated adjusted relative risks using log-binomial regression.

251 aracteristics were modeled by using negative binomial regression.

252 o BMI change categories were calculated with binomial regression.

253 ding the interview date were evaluated using binomial regression.

254 est reaction of >/=3 mm) were analysed using binomial regression.

255 were compared between groups using negative binomial regression.

256 . aureus colonization was assessed using log-binomial regression.

257 ctors for infection, using multivariable log-binomial regression.

258 tivariable analyses were performed using log-binomial regression.

259 participant and were analyzed using negative binomial regression.

260 idence, and antiretroviral treatment, by log-binomial regression.

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

262 introduction of PCV10 were calculated by log-binomial regression.

263 d MN frequency were estimated using negative binomial regression.

264 ression and delirium duration using negative binomial regression.

265 atios (PRRs) of GUD were estimated using log binomial regression.

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

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

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

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

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

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

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

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

274 y equivalent to generalized linear models of binomial responses that include a complementary, log-log

275 Log-binomial risk ratios comparing intervention arms against

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

277 commercialization were compared by negative binomial segmented regression models.

278 of replacement and silent mutations using a binomial statistical analysis.

279 es, we propose a new method that applies the binomial statistical framework to mutations identified b

280 lar release counts at simple synapses follow binomial statistics with a maximum that varies from 2 to

281 In keeping with binomial statistics, this increases the relative precisi

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

283 Using a recessive model and a binomial test for rare, presumed biallelic, variants, we

284 ichment methods, Fisher's exact test and the binomial test implemented in Genomic Regions Enrichment

285 hild-mother-father genotype data that uses a binomial test to identify chromosomes with a significant

286 on in most cancer types studied (P < 10(-9), binomial test), reflecting its important role in cellula

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

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

289 vel and is more powerful than the individual binomial testing procedure.

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 ting is implemented through binomial or beta-binomial tests of sequence read counts of alternative al

293 5 mL/min/1.73 m(2) [eGFR]) using McNemar and binomial tests.

294 hool graduates, and U.S. population by using binomial tests; with adjustment for multiple comparisons

295 ) of the point estimate as calculated by the binomial theorem, indicating mutual independence.

296 ch lower probabilities than are predicted by binomial theory, supporting the conclusion that they are

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

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

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

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