powerSurvEpi (version 0.0.6)

powerEpi.default: Power Calculation for Cox Proportional Hazards Regression with Two Covariates for Epidemiological Studies (Covariate of interest should be binary)

Description

Power calculation for Cox proportional hazards regression with two covariates for epidemiological Studies. The covariate of interest should be a binary variable. The other covariate can be either binary or non-binary. The formula takes into account competing risks and the correlation between the two covariates.

Usage

powerEpi.default(n, theta, p, psi, rho2, alpha = 0.05)

Arguments

n

total number of subjects

theta

postulated hazard ratio

p

proportion of subjects taking the value one for the covariate of interest.

psi

proportion of subjects died of the disease of interest.

rho2

square of the correlation between the covariate of interest and the other covariate.

alpha

type I error rate.

Value

• The power of the test.

Details

This is an implementation of the power calculation formula derived by Latouche et al. (2004) for the following Cox proportional hazards regression in the epidemiological studies: $$h(t|x_1, x_2)=h_0(t)\exp(\beta_1 x_1+\beta_2 x_2),$$ where the covariate $X_1$ is of our interest. The covariate $X_1$ should be a binary variable taking two possible values: zero and one, while the covariate $X_2$ can be binary or continuous.

Suppose we want to check if the hazard of $X_1=1$ is equal to the hazard of $X_1=0$ or not. Equivalently, we want to check if the hazard ratio of $X_1=1$ to $X_1=0$ is equal to $1$ or is equal to $\exp(\beta_1)=\theta$. Given the type I error rate $\alpha$ for a two-sided test, the power required to detect a hazard ratio as small as $\exp(\beta_1)=\theta$ is $$power=\Phi\left(-z_{1-\alpha/2}+\sqrt{n[\log(\theta)]^2 p (1-p) \psi (1-\rho^2)}\right),$$ where $\psi$ is the proportion of subjects died of the disease of interest, and $$\rho=corr(X_1, X_2)=(p_1-p_0)\times \sqrt{\frac{q(1-q)}{p(1-p)}},$$ and $p=Pr(X_1=1)$, $q=Pr(X_2=1)$, $p_0=Pr(X_1=1|X_2=0)$, and $p_1=Pr(X_1=1 | X_2=1)$.

References

Schoenfeld DA. (1983). Sample-size formula for the proportional-hazards regression model. Biometrics. 39:499-503.

Latouche A., Porcher R. and Chevret S. (2004). Sample size formula for proportional hazards modelling of competing risks. Statistics in Medicine. 23:3263-3274.

See Also

powerEpi

Examples

# Example at the end of Section 5.2 of Latouche et al. (2004)
  # for a cohort study.
  powerEpi.default(n = 139, theta = 2, p = 0.39, psi = 0.505,
    rho2 = 0.132^2, alpha = 0.05)