powerEpiInt.default0 {powerSurvEpi} | R Documentation |
Power Calculation Testing Interaction Effect for Cox Proportional Hazards Regression
Description
Power calculation testing interaction effect for Cox proportional hazards regression with two covariates for Epidemiological Studies. Both covariates should be binary variables. The formula takes into account the correlation between the two covariates.
Usage
powerEpiInt.default0(n,
theta,
p,
psi,
G,
rho2,
alpha = 0.05)
Arguments
n |
integer. total number of subjects. |
theta |
numeric. postulated hazard ratio. |
p |
numeric. proportion of subjects taking the value one for the covariate of interest. |
psi |
numeric. proportion of subjects died of the disease of interest. |
G |
numeric. a factor adjusting the sample size. The sample size needed to
detect an effect of a prognostic factor with given error probabilities has
to be multiplied by the factor |
rho2 |
numeric. square of the correlation between the covariate of interest and the other covariate. |
alpha |
numeric. type I error rate. |
Details
This is an implementation of the power calculation formula derived by Schmoor et al. (2000) for the following Cox proportional hazards regression in the epidemiological studies:
where both covariates and
are binary variables.
Suppose we want to check if
the hazard ratio of the interaction effect to
is equal to
or is equal to
.
Given the type I error rate
for a two-sided test, the power
required to detect a hazard ratio as small as
is
where is the
-th percentile of the standard normal distribution,
is the proportion of subjects died of
the disease of interest, and
and
,
,
,
and
, and
If and
are uncorrelated, we have
leading to
. For
, we have
.
Value
The power of the test.
References
Schmoor C., Sauerbrei W., and Schumacher M. (2000). Sample size considerations for the evaluation of prognostic factors in survival analysis. Statistics in Medicine. 19:441-452.
See Also
powerEpiInt.default1
, powerEpiInt2
Examples
# Example at the end of Section 4 of Schmoor et al. (2000).
powerEpiInt.default0(n = 184,
theta = 3,
p = 0.61,
psi = 139 / 184,
G = 4.79177,
rho2 = 0.015^2,
alpha = 0.05)