csdvsmdd.t {MDD} | R Documentation |
For a trial evaluated using a t-test with equal variances, calculates the probabilities of either having a clinically significant difference declared statistically insignificant, or of having a statistically significant difference declared clinically insignificant. Also ouputs the power of the test.
csdvsmdd.t(n1, n2, sigma, ate, csd, alpha = 0.05, alternative = "two.sided")
n1 |
number of subjects in control group. |
n2 |
number of subjects in treatment group. |
sigma |
anticipated common standard deviation. |
ate |
anticipated treatment effect. |
csd |
clinically significant difference. |
alpha |
significance level. |
alternative |
indicates the alternative hypothesis and must be one of "two.sided" ,
"greater" , or "less" . You can specify just the initial letter. |
The “clinically significant difference” is the smallest difference that will be meaningful either clinically or commercially. This is usually obtained from the clinical scientists and/or business partners you are working with.
The “anticipated treatment effect” is the quantity on which the study will be powered; it may, in general, be different from the clinically significant difference.
A list with the following three components:
statsig.not.clinsig |
the probability that the trial will have a statistically significant but clinically insignificant result. |
clinsig.not.statsig |
the probability that the trial will have a clinically significant but statistically insignificant result. |
power |
the power of the test. |
Don Barkauskas (barkda@wald.ucdavis.edu)
csdvsmdd.satter
for unequal variances
gui.csdvsmdd
for a GUI version
#Note that, unlike for csdvsmdd.chisq and csdvsmdd.fisher, #at most one of the first two components can be zero. csdvsmdd.t(20, 20, .35, .4, .3) csdvsmdd.t(20, 20, .35, .4, .1)