We proposed a train headway adjustment model based on linear quadratic optimal control.
Using a quadratic rather than linear adjustment for age in the DBP models yielded null results between DBP and both exposures (data not shown).
The authors estimate a log-linear partial adjustment dynamic model that is quadratic in per capita income.
The ITT estimate was obtained by fitting a linear regression model with adjustment for baseline variables.
In all multiple linear regression models adjustment will be made for potential confounding factors such as sex, gestation, age at follow-up, birthweight z-score, infant growth velocity, post discharge feeding pubertal status and whether mechanical ventilation was required in the neonatal period (which we used as a proxy for illness severity).
We conducted a retrospective analysis of patients presenting with AS and explored the trajectory of AS mean gradient over time using generalized estimating equations and fit a longitudinal linear regression model with adjustment for baseline clinical variables.
The association between expression of TCF7L2 assays and the counts of risk alleles (0, 1, 2) of the T2D-associated SNPs rs7903145 and rs12255372 was tested with univariate linear regression model with adjustment for covariates (age, sex, and log-transformed values of BMI and blood levels of glucose and HbA1c).
We obtained similar results in a linear regression model with adjustment for confounding variables.
Genotype phenotype association was tested using a linear regression model with adjustment for the first 10 PCs.
First, RSV positive samples were compared to controls using a multivariate linear regression model with adjustment for the RNA integrity number (RIN).
In the multiple linear regression model, after adjustment for age and gender, BMI, TSH, smoking, thyroid nodules, and positive thyroid autoantibodies influenced thyroid volume in the final regression model.
