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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

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

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.

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

30 and gestational diabetes mellitus (GDM), and we used linear regression models to estimate associations with first-trimest

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

41 Multivariable hierarchical (2-level) regression models were used to calculate calendar-year rates

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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