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1 f treatment delay on treatment effectiveness using logistic regression models.
2 diseases and ADHD in offspring were analyzed using logistic regression models.
3 e compared with linear and generalized linear mixed-effects regression models.
4 d patient outcomes between 1994 and 2012 using adjusted Cox regression models.
5 ing the 52 SNPs to all phenotypes using logistic and linear regression models.
6 nsity statin and nonstatin LLT use in hierarchical logistic regression models.
7 n daughters at midlife using quantile, linear, and logistic regression models.
8 novo SCAD were tested using univariate and multivariate Cox regression models.
9  statistical methods using 2 independently derived logistic regression models (a de novo model and an a priori model deve
10                                             Multiple linear regression models adjusted for potential confounders were use
11                                        Conditional logistic regression models adjusting for risk factors evaluated associ
12  for asthma at ages 5-9 years were calculated using Poisson regression models and pooled.
13                                                      Linear regression models and the t-test were employed to compare sig
14                                                          In regression models, APOE-e4 dose and age both consistently inc
15                Such method consists of fitting whole-genome regression models by subsampling observations in each round o
16                   We used published data to create logistic regression models comparing annual trends in the representati
17                                  Partial least square (PLS) regression models confirmed reliability of detection and spec
18 imated hazard ratios (HR) and 95% CIs with multivariate Cox regression models fitting stromal TILs as a continuous variab
19 line and postguideline periods in the hierarchical logistic regression models for all of the risk groups.
20                                       We used mixed-effects regression models for ordered-categorical outcome variables t
21                                                             Regression models gave r>0.77 confirming that Se dose and dev
22                                                    Logistic regression models identified characteristics associated with
23 elationship by oxidative stress, and the utility of complex regression models in capturing mediated associations when rep
24                      Multivariable Cox proportional hazards regression models on the risk of a disease milestone and deat
25                                           Multiple logistic regression models revealed that combining the features Teta1
26                                                    Multiple regression models (standardized regression coefficients [SRCs
27                                                    Logistic regression models tested any independent relationship between
28                            We used Cox proportional hazards regression models to assess the association between later-gen
29                                            We used logistic regression models to estimate associations of PFASs (log10-tr
30 and gestational diabetes mellitus (GDM), and we used linear regression models to estimate associations with first-trimest
31                              We used Bayesian mixed-effects regression models to estimate mortality overall and from each
32                                       We fitted a series of regression models to estimate the proportion of moderate or s
33  effects, cell types, and covariates, we used robust linear regression models to examine associations of prenatal lead ex
34              Hazard ratios were estimated with weighted Cox regression models using Barlow weights to account for the cas
35                                                  Using beta regression models, we analysed the outcome data released by N
36                                      Using Cox and binomial regression models, we compared the 2 randomization groups.
37                                Pooled multivariate logistic regression models were constructed for each infection-burden
38                                                Longitudinal regression models were constructed to assess associations bet
39                                                             Regression models were developed to assess the relation betwe
40                                                    Multiple regression models were fitted to estimate genetic effects on
41                        Multivariable hierarchical (2-level) regression models were used to calculate calendar-year rates
42                                                         Cox regression models were used to calculate hazard ratios (HRs)
43                                          Time-dependent Cox regression models were used to calculate hazard ratios (HRs)
44                                               Mixed-effects regression models were used to compare PRO scores across proc
45                                        Conditional logistic regression models were used to estimate odds ratios (ORs) tha
46                                      Multivariable binomial regression models were used to evaluate the effects of oral h
47                                Multiple linear and logistic regression models were used to examine relations of plasma me
48 fferences in percent effect changes in conditional logistic regression models with and without additional adjustment for
49         We trained Gaussian process (GP) classification and regression models with expression and localization data from
50  HIV shedding (VL > 40 copies/mL) were estimated by Poisson regression models with generalized estimating equations and r

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