numDEpi.default: Calculate Number of Deaths Required for Cox Proportional...

Description Usage Arguments Details Value Note References See Also Examples

View source: R/powerEpi.R

Description

Calculate number of deaths required 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

1
numDEpi.default(power, theta, p, rho2, alpha = 0.05)

Arguments

power

the postulated power.

theta

postulated hazard ratio

p

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

rho2

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

alpha

type I error rate.

Details

This is an implementation of the calculation of the number of required deaths 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 total number of deaths required to achieve a power of 1-β is

D=\frac{≤ft(z_{1-α/2}+z_{1-β}\right)^2}{ [\log(θ)]^2 p (1-p) (1-ρ^2), }

where

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 number of deaths required to achieve the desired power with given type I error rate.

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 cause-specific hazard for a particular failure, while in Schoenfeld (1983), the hazard ratio θ measures the difference of effect on subdistribution 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

numDEpi

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
  # Example at the end of Section 5.2 of Latouche et al. (2004)
  # for a cohort study.
  D <- numDEpi.default(power = 0.8, theta = 2, p = 0.39, 
    rho2 = 0.132^2, alpha = 0.05)

  # proportion of subjects died of the disease of interest.
  psi <- 0.505

  # total number of subjects required to achieve the desired power
  ceiling(D / psi)

powerSurvEpi documentation built on May 29, 2017, 10:46 a.m.

Search within the powerSurvEpi package
Search all R packages, documentation and source code