When should I use covariates?
Covariates are commonly used as control variables. For instance, use of a baseline pre-test score can be used as a covariate to control for initial group differences on math ability or whatever is being assessed in the ANCOVA study.
How do covariates affect power?
Adjustment for prognostic covariates led to substantial increases in power for most outcomes. The median increase in power was from 80% to 92.6% (range 80.6 to 99.4%), and power was increased to over 85% and 95% for 8 of 12 and 5 of 12 outcomes, respectively.
How many times do you need to estimate an interaction?
You need 16 times the sample size to estimate an interaction than to estimate a main effect. So, the 10% of results which do appear to be statistically significant give an estimate of 2.4, on average, which is over 3 times higher than the true effect.
What do you mean by interaction effect in ANOVA?
Interaction effects represent the combined effects of factors on the dependent measure. When an interaction effect is present, the impact of one factor depends on the level of the other factor. Part of the power of ANOVA is the ability to estimate and test interaction effects.
When to include confounding variable in multivariable logistic regression?
If you are analyzing data using multivariable logistic regression, a rule of thumb is if the odds ratio changes by 10% or more, include the potential confounder in the multi-variable model. The question is not so much the statistical significance, but the amount the confounding variable changes the effect.
How to estimate the effect of YBAR _ 1?
– The estimate of the main effect is ybar_1 – ybar_2, which has standard error sqrt(sigma^2/(N/2) + sigma^2/(N/2)) = 2*sigma/sqrt(N); for simplicity I’m assuming a constant variance within groups, which will typically be a good approximation for binary data, for example.