numDEpi {powerSurvEpi} | R Documentation |
Calculate Number of Deaths Required for Cox Proportional Hazards Regression with Two Covariates for Epidemiological Studies
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. Some parameters will be estimated based on a pilot data set.
Usage
numDEpi(X1,
X2,
power,
theta,
alpha = 0.05)
Arguments
X1 |
numeric. a |
X2 |
numeric. a |
power |
numeric. the postulated power. |
theta |
numeric. postulated hazard ratio |
alpha |
numeric. 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:
where the covariate is of our interest. The covariate
should be
a binary variable taking two possible values: zero and one, while the
covariate
can be binary or continuous.
Suppose we want to check if the hazard of is equal to
the hazard of
or not. Equivalently, we want to check if
the hazard ratio of
to
is equal to
or is equal to
.
Given the type I error rate
for a two-sided test, the total
number of deaths required to achieve a power of
is
where is the
-th percentile of the standard normal distribution,
and
,
,
,
and
.
and
will be estimated from a pilot data set.
Value
D |
the number of deaths required to achieve the desired power with given type I error rate. |
p |
proportion of subjects taking |
rho2 |
square of the correlation between |
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 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
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
Examples
# generate a toy pilot data set
X1 <- c(rep(1, 39), rep(0, 61))
set.seed(123456)
X2 <- sample(c(0, 1), 100, replace = TRUE)
res <- numDEpi(X1 = X1,
X2 = X2,
power = 0.8,
theta = 2,
alpha = 0.05)
print(res)
# proportion of subjects died of the disease of interest.
psi <- 0.505
# total number of subjects required to achieve the desired power
ceiling(res$D / psi)