Power of Interaction Test for Exponential Survival

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Description

Uses the method of Peterson and George to compute the power of an interaction test in a 2 x 2 setup in which all 4 distributions are exponential. This will be the same as the power of the Cox model test if assumptions hold. The test is 2-tailed. The duration of accrual is specified (constant accrual is assumed), as is the minimum follow-up time. The maximum follow-up time is then accrual + tmin. Treatment allocation is assumed to be 1:1.

Usage

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ciapower(tref, n1, n2, m1c, m2c, r1, r2, accrual, tmin, 
         alpha=0.05, pr=TRUE)

Arguments

tref

time at which mortalities estimated

n1

total sample size, stratum 1

n2

total sample size, stratum 2

m1c

tref-year mortality, stratum 1 control

m2c

tref-year mortality, stratum 2 control

r1

% reduction in m1c by intervention, stratum 1

r2

% reduction in m2c by intervention, stratum 2

accrual

duration of accrual period

tmin

minimum follow-up time

alpha

type I error probability

pr

set to FALSE to suppress printing of details

Value

power

Side Effects

prints

AUTHOR

Frank Harrell

Department of Biostatistics

Vanderbilt University

f.harrell@vanderbilt.edu

References

Peterson B, George SL: Controlled Clinical Trials 14:511–522; 1993.

See Also

cpower, spower

Examples

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# Find the power of a race x treatment test.  25% of patients will
# be non-white and the total sample size is 14000.  
# Accrual is for 1.5 years and minimum follow-up is 5y.
# Reduction in 5-year mortality is 15% for whites, 0% or -5% for
# non-whites.  5-year mortality for control subjects if assumed to
# be 0.18 for whites, 0.23 for non-whites.
n <- 14000
for(nonwhite.reduction in c(0,-5)) {
  cat("\n\n\n% Reduction in 5-year mortality for non-whites:",
      nonwhite.reduction, "\n\n")
  pow <- ciapower(5,  .75*n, .25*n,  .18, .23,  15, nonwhite.reduction,  
                  1.5, 5)
  cat("\n\nPower:",format(pow),"\n")
}

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