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1  risk using bivariable Poisson, Fine-Gray, and log-binomial regression models.
2 lamivudine (3TC), and others were estimated through Poisson regression models.
3 action of feeding study intake variation explained by these regression models.
4 erosis Risk in Communities study using multivariable linear regression models.
5 ncy for each participant was estimated by means of land-use regression models.
6 ences between the two study periods were assessed using Cox regression models.
7  IBBR, PMRT, and PROs were investigated using mixed-effects regression models adjusted for clinically-relevant confounder
8 at ages 8 (n = 5,276) and 15 (n = 3,446) years using linear regression models adjusted for potential confounders.
9 eatinine and cystatin C) and ACR with cancer risk using Cox regression models adjusted for potential confounders.
10                                 We fitted mixed-effects Cox regression models adjusting for multiple pregnancies per indi
11                                                    Adjusted regression models and a meta-analysis were performed.
12                                           Adjusted logistic regression models and meta-analyses were performed.
13  (i.e. LAZ < - 2) and persistence from 12 to 24 months into regression models and tested for the mediating effect of low
14                                    We created multivariable regression models at the year, day, and visit level after adj
15                                                    Logistic regression models combining T2-weighted SI and T2-weighted he
16                                         Linear and quantile regression models estimated the association between PPYEd and
17                                                    Logistic regression models evaluated the relation of baseline weight s
18 e compared by transition status, and multivariable logistic regression models examined factors associated with satisfacti
19                                              We fit Weibull regression models for time to viral load >1000 copies/mL (tre
20                                                             Regression models identified 17 variables that were significa
21                                                             Regression models identified variables associated with discha
22                                                             Regression models indicated that %ViableSperm of bulls was re
23 nsrepression and predicted response to ICS through logistic regression models.Measurements and Main Results: We identifie
24                          By fitting three multiple logistic regression models (one for each delivery mode), we calculated
25                                             In multivariate regression models, strength was associated with FA (b = -0.00
26 s the postoperative outcomes, we used multivariate logistic regression models to adjust for clinical and demographic cova
27   For both individual descriptors and clusters, we used Cox regression models to assess associations with time from biops
28 azard models, and, in a post-hoc analysis, we used logistic regression models to assess the association between demograph
29                                    We used time-varying Cox regression models to examine the association between 1- and 3
30                                           Here, we use beta regression models to study the socioeconomic and geographic d
31 validated AUC for glaucoma versus nonglaucoma from logistic regression models using MRW-BMO values from all sectors was 0
32 ere assessed by permutational multivariate ANOVA and hurdle regression models using the negative binomial distribution.
33                                                         Cox regression models were applied to analyze the association bet
34           Individual univariable and multivariable logistic regression models were assessed for each time-weighted-averag
35                               Bayesian linear mixed effects regression models were constructed to evaluate associations b
36                                                             Regression models were fitted to assess association between r
37                                                    Logistic regression models were fitted to determine the association wi
38                                         Three complementary regression models were generated for number of patients seen,
39                                           Multivariable Cox regression models were used to assess associations of asthma
40                      Univariable and multivariable logistic regression models were used to assess predictors of mortality
41                             Adjusted path analysis logistic regression models were used to assess the role of pre-pregnan
42                                      Unconditional logistic regression models were used to estimate odds ratios and 95% c
43                                         Polytomous logistic regression models were used to estimate ORs and 95% CIs among
44                                    Generalized linear mixed regression models were used to examine the association betwee
45                                                      Random regression models were used to jointly analyse live body weig
46                                    Cox proportional hazards regression models were used to obtain age- and sex-adjusted e
47                                                      Linear regression models were used to relate measures of neonatal CB
48 formation criterion in a stepwise fashion to build logistic regression models, which were then translated into prediction
49  blood culture results among outpatients using mixed-effect regression models with a random effect for study site hospita
50    Hazard ratios for mortality were calculated by using Cox regression models with emphysema as the main predictor.