Description Usage Arguments Value Side Effects AUTHOR References See Also Examples

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.

1 2 |

`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 |

`r2` |
% reduction in |

`accrual` |
duration of accrual period |

`tmin` |
minimum follow-up time |

`alpha` |
type I error probability |

`pr` |
set to |

power

prints

Frank Harrell

Department of Biostatistics

Vanderbilt University

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
# 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")
}
``` |

Hmisc documentation built on May 20, 2017, 5:52 a.m.

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