p value: statistical evidence against null hypothesis, or say collect evidence to reject null hypothesis, or say the probability of data to reject the assumed wisdom. (1) probability value (2) p value for data, once data is collected under null hypothesis, data can not be changed. (3) used for rejecting null hypothesis.
parametric & non-parametric test? If we don't know the data follows which kind of distribution, no parameter, then using non-parametric test such as Wilcoxon rank tests directly.
Type I & Type II error, Power
mixed effect model: fixed effects, random effects, usually used in non-survival studies, continuous variables, not related to time.
Model selection/building with AIC: smaller AIC indicates better of several models, AIC acts as a guard against overfitting
CMH: for three way table common odds ratio test, testing indepandance.
Categorical: Proc Genmod
Study Design: Sample Size Calculation: Proc power Non-survival analysis sample size calculation Randomization
Effect size is the treatment difference caused by different treatments. It signifies the magnitude of treatment effects.
The difference can be measured in categorical variables or in continuous variables. Difference in proportion can be one type of effect size; difference in means can be another type.
Randomization can be done by proc plan
It can also be done by using randomization SAS functions.
Sample size can be calculated using proc power in SAS; or using special software like nQuery, PASS, East, etc.
The key is to find the number of subjects that would let us gain the statistical power for the clincial trial.