We extend the definition of adjusted treatment means in the analysis of covariance to deal with the case where some of the covariates are influenced by treatments or where some of the factors are observational. In these cases, comparison of treatment means adjusted to a common value of the covariate may be inappropriate. Partially adjusted means are defined and it is shown that special cases include the usual adjusted means (adjusted to a common value for each of the covariates) and unadjusted means. In fact, in a multifactorial experiment, one can, by appropriate choice of adjustment, compare adjusted means for one factor but unadjusted means for the second factor. Partially adjusted means can be computed by any linear models software whic...
When building models to investigate outcomes and variables of interest, researchers often want to ad...
Background: It has long been advised to account for baseline covariates in the analysis of confirmat...
When building models to investigate outcomes and variables of interest, researchers often want to ad...
By slightly reframing the concept of covariance adjustment in randomized experiments, a method of ex...
Adjustment for baseline covariates in randomized trials has been shown to lead to gains in power and...
Introduction & Objective: Unadjusted analyses, fully adjusted analyses, or adjusted analyses bas...
Observational studies are nonrandomized experiments in which treated and control groups may differ w...
There is considerable debate regarding whether and how covariate adjusted analyses should be used in...
Many real data sets that would normally lend themselves to being analyzed by an analysis of covarian...
Linear regression adjustments for pre-treatment covariates are widely used in economics to lower the...
Linear regression adjustments for pre-treatment covariates are widely used in economics to lower the...
For fixed-effect models one can always, according to the Gauss-Markov Theorem, uniquely determine in...
Randomized experiments are the gold standard for causal inference, and justify simple comparisons ac...
Background: It has long been advised to account for baseline covariates in the analysis of confirmat...
A common design in social psychology involves the use of two independent variables, an experimental ...
When building models to investigate outcomes and variables of interest, researchers often want to ad...
Background: It has long been advised to account for baseline covariates in the analysis of confirmat...
When building models to investigate outcomes and variables of interest, researchers often want to ad...
By slightly reframing the concept of covariance adjustment in randomized experiments, a method of ex...
Adjustment for baseline covariates in randomized trials has been shown to lead to gains in power and...
Introduction & Objective: Unadjusted analyses, fully adjusted analyses, or adjusted analyses bas...
Observational studies are nonrandomized experiments in which treated and control groups may differ w...
There is considerable debate regarding whether and how covariate adjusted analyses should be used in...
Many real data sets that would normally lend themselves to being analyzed by an analysis of covarian...
Linear regression adjustments for pre-treatment covariates are widely used in economics to lower the...
Linear regression adjustments for pre-treatment covariates are widely used in economics to lower the...
For fixed-effect models one can always, according to the Gauss-Markov Theorem, uniquely determine in...
Randomized experiments are the gold standard for causal inference, and justify simple comparisons ac...
Background: It has long been advised to account for baseline covariates in the analysis of confirmat...
A common design in social psychology involves the use of two independent variables, an experimental ...
When building models to investigate outcomes and variables of interest, researchers often want to ad...
Background: It has long been advised to account for baseline covariates in the analysis of confirmat...
When building models to investigate outcomes and variables of interest, researchers often want to ad...