powerEpi.default: Power Calculation for Cox Proportional Hazards Regression...

In powerSurvEpi: Power and Sample Size Calculation for Survival Analysis of Epidemiological Studies

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	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.
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 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(β_1 x_1+β_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(β_1)=θ. Given the type I error rate α for a two-sided test, the power required to detect a hazard ratio as small as \exp(β_1)=θ is

power=Φ≤ft(-z_{1-α/2}+√{n[\log(θ)]^2 p (1-p) ψ (1-ρ^2)}\right),

where z_{a} is the 100 a-th percentile of the standard normal distribution, ψ is the proportion of subjects died of the disease of interest, and

ρ=corr(X_1, X_2)=(p_1-p_0)\times √{\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).

Value

The power of the test.

Note

(1) The formula can be used to calculate power for a randomized trial study by setting rho2=0.

(2) When rho2=0, the formula derived by Latouche et al. (2004) looks the same as that derived by Schoenfeld (1983). Latouche et al. (2004) pointed out that in this situation, the interpretations are different hence the two formulae are actually different. In Latouched et al. (2004), the hazard ratio θ measures the difference of effect of a covariate at two different levels on the subdistribution hazard for a particular failure, while in Schoenfeld (1983), the hazard ratio θ measures the difference of effect on the cause-specific hazard.

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)

powerSurvEpi documentation built on March 1, 2021, 9:06 a.m.