pwr.logRank: Sample size calculations for the log-rank test
In bgreenwell/bmisc: Miscellaneous R Functions

Description Usage Arguments Details Value Note Examples

Calculates the required sample size for a log-rank test using Freedman's method.

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pwr.logRank(S.trt, S.ctrl, sig.level = 0.05, power = 0.8,
  alternative = c("two.sided", "less", "greater"),
  method = c("Freedman"))

`S.trt`	Numeric specifying the hazard rate for the treatment group.
`S.ctrl`	Numeric specifying the hazard rate for the control group.
`sig.level`	Significance level (Type I error probability).
`power`	Power of test (1 minus Type II error probability).
`alternative`	Character string specifying the form of the alternative hypothesis. Must be one of `"two.sided"` (default), `"greater"` or `"less"`.
`method`	Character string specifying the method to use in calculating the required sample size. (Currently ignored.)

PASS Sample Size Software, Chapter 700: The power calculations used here assume an underlying exponential distribution. However, we are rarely in a position to assume exponential survival times in an actual clinical trial. How do we justify the exponential survival time assumption? First, the logrank test and the test derived using the exponential distribution have nearly the same power when the data are in fact exponentially distributed. Second, under the proportional hazards model (which is assumed by the logrank test), the survival distribution can be transformed to be exponential and the logrank test remains the same under monotonic transformations.

An estimate of the required sample size.

Returns Inf when S.trt == S.ctrl.