Power Calculation Testing Interaction Effect for Cox Proportional Hazards Regression

Share:

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

1
powerEpiInt.default1(n, theta, psi, p00, p01, p10, p11, alpha = 0.05)

Arguments

n

total number of subjects.

theta

postulated hazard ratio.

psi

proportion of subjects died of the disease of interest.

p00

proportion of subjects taking values X_1=0 and X_2=0, i.e., p_{00}=Pr(X_1=0,\mbox{and}, X_2=0).

p01

proportion of subjects taking values X_1=0 and X_2=1, i.e., p_{01}=Pr(X_1=0,\mbox{and}, X_2=1).

p10

proportion of subjects taking values X_1=1 and X_2=0, i.e., p_{10}=Pr(X_1=1,\mbox{and}, X_2=0).

p11

proportion of subjects taking values X_1=1 and X_2=1, i.e., p_{11}=Pr(X_1=1,\mbox{and}, X_2=1).

alpha

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 epidemoilogical studies:

h(t|x_1, x_2)=h_0(t)\exp(β_1 x_1+β_2 x_2 + γ (x_1 x_2)),

where both covariates X_1 and X_2 are binary variables.

Suppose we want to check if the hazard ratio of the interaction effect X_1 X_2=1 to X_1 X_2=0 is equal to 1 or is equal to \exp(γ)=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(γ)=θ is:

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

where

δ=\frac{1}{p_{00}}+\frac{1}{p_{01}}+\frac{1}{p_{10}} +\frac{1}{p_{11}},

ψ is the proportion of subjects died of the disease of interest, and p_{00}=Pr(X_1=0,\mbox{and}, X_2=0), p_{01}=Pr(X_1=0,\mbox{and}, X_2=1), p_{10}=Pr(X_1=1,\mbox{and}, X_2=0), p_{11}=Pr(X_1=1,\mbox{and}, X_2=1).

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.default0, powerEpiInt2

Examples

1
2
3
4
5
6
7
  # Example at the end of Section 4 of Schmoor et al. (2000).
  # p00, p01, p10, and p11 are calculated based on Table III on page 448
  # of Schmoor et al. (2000).
  powerEpiInt.default1(n = 184, theta = 3, psi = 139 / 184,
    p00 = 50 / 184, p01 = 21 / 184, p10 = 78 / 184, p11 = 35 / 184,
    alpha = 0.05)
  

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.