power.stratify: Power Calculation for Survival Analysis with Binary Predictor...

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

Description

Power calculation for survival analysis with binary predictor and exponential survival function.

Usage

power.stratify(
    n, 
    timeUnit, 
    gVec, 
    PVec, 
    HR, 
    lambda0Vec, 
    power.ini = 0.8, 
    power.low = 0.001, 
    power.upp = 0.999, 
    alpha = 0.05, 
    verbose = TRUE)

Arguments

n	integer. Sample size.
timeUnit	numeric. Total study length.
gVec	numerc. m by 1 vector. The s-th element is the proportion of the total sample size for the s-th stratum, where m is the number of strata.
PVec	numeric. m by 1 vector. The s-th element is the proportion of subjects in treatment group 1 for the s-th stratum, where m is the number of strata.
HR	numeric. Hazard ratio (Ratio of the hazard for treatment group 1 to the hazard for treatment group 0, i.e. reference group).
lambda0Vec	numeric. m by 1 vector. The s-th element is the hazard for treatment group 0 (i.e., reference group) in the s-th stratum.
power.ini	numeric. Initial power estimate.
power.low	numeric. Lower bound for power.
power.upp	numeric. Upper bound for power.
alpha	numeric. Type I error rate.
verbose	Logical. Indicating if intermediate results will be output or not.

Details

We assume (1) there is only one predictor and no covariates in the survival model (exponential survival function); (2) there are m strata; (3) the predictor x is a binary variable indicating treatment group 1 (x=1) or treatment group 0 (x=0); (3) the treatment effect is constant over time (proportional hazards); (4) the hazard ratio is the same in all strata, and (5) the data will be analyzed by the stratified log rank test.

The sample size formula is Formula (1) on page 801 of Palta M and Amini SB (1985):

n=(Z_{α}+Z_{β})^2/μ^2,

where α is the Type I error rate, β is the Type II error rate (power=1-β), Z_{α} is the 100(1-α)-th percentile of standard normal distribution, and

μ=\log(δ)√{ ∑_{s=1}^{m} g_s P_s (1 - P_s) V_s },

and

V_s=P_s≤ft[1-\frac{1}{λ_{1s}} ≤ft{ \exp≤ft[-λ_{1s}(T-1)\right] -\exp(-λ_{1s}T) \right} \right] +(1-P_s)≤ft[ 1-\frac{1}{λ_{0s}} ≤ft{ \exp≤ft[-λ_{0s}(T-1)\right] -\exp(-λ_{0s}T \right} \right].

In the above formulas, m is the number of strata, T is the total study length, δ is the hazard ratio, g_s is the proportion of the total sample size in stratum s, P_s is the proportion of stratum s, which is in treatment group 1, and λ_{is} is the hazard for the i-th treatment group in stratum s.

Value

A list of 2 elments.

power	Estimated power
res.optim	Object returned by funciton optim. We used numerical optimization method to calculate power based on sample size calculation formula.

References

Palta M and Amini SB. (1985). Consideration of covariates and stratification in sample size determination for survival time studies. Journal of Chronic Diseases. 38(9):801-809.

See Also

ssize.stratify

Examples

# example on page 803 of Palta M and Amini SB. (1985). 
res.power <- power.stratify(
  n = 146, 
  timeUnit = 1.25, 
  gVec = c(0.5, 0.5),
  PVec = c(0.5, 0.5), 
  HR = 1 / 1.91, 
  lambda0Vec = c(2.303, 1.139),
  power.ini = 0.8, 
  power.low = 0.001, 
  power.upp = 0.999,
  alpha = 0.05, 
  verbose = TRUE
  )

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