powerCT.default: Power Calculation in the Analysis of Survival Data for...

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

Description

Power calculation for the Comparison of Survival Curves Between Two Groups under the Cox Proportional-Hazards Model for clinical trials.

Usage

powerCT.default(nE, 
		nC, 
		pE, 
		pC, 
		RR, 
		alpha = 0.05)

Arguments

nE	integer. number of participants in the experimental group.
nC	integer. number of participants in the control group.
pE	numeric. probability of failure in group E (experimental group) over the maximum time period of the study (t years).
pC	numeric. probability of failure in group C (control group) over the maximum time period of the study (t years).
RR	numeric. postulated hazard ratio.
alpha	numeric. type I error rate.

Details

This is an implementation of the power calculation method described in Section 14.12 (page 807) of Rosner (2006). The method was proposed by Freedman (1982).

Suppose we want to compare the survival curves between an experimental group (E) and a control group (C) in a clinical trial with a maximum follow-up of t years. The Cox proportional hazards regression model is assumed to have the form:

h(t|X_1)=h_0(t)\exp(β_1 X_1).

Let n_E be the number of participants in the E group and n_C be the number of participants in the C group. We wish to test the hypothesis H0: RR=1 versus H1: RR not equal to 1, where RR=\exp(β_1)=underlying hazard ratio for the E group versus the C group. Let RR be the postulated hazard ratio, α be the significance level. Assume that the test is a two-sided test. If the ratio of participants in group E compared to group C = n_E/n_C=k, then the power of the test is

power=Φ(√{k*m}*|RR-1|/(k*RR+1)-z_{1-α/2}),

where

m=n_E p_E+n_C p_C,

and z_{1-α/2} is the 100 (1-α/2)-th percentile of the standard normal distribution N(0, 1), Φ is the cumulative distribution function (CDF) of N(0, 1).

Value

The power of the test.

Note

The power formula assumes that the central-limit theorem is valid and hence is appropriate for large samples.

References

Freedman, L.S. (1982). Tables of the number of patients required in clinical trials using the log-rank test. Statistics in Medicine. 1: 121-129

Rosner B. (2006). Fundamentals of Biostatistics. (6-th edition). Thomson Brooks/Cole.

See Also

powerCT.default0, powerCT

Examples

  # Example 14.42 in Rosner B. Fundamentals of Biostatistics. 
  # (6-th edition). (2006) page 809
  powerCT.default(nE = 200, 
		  nC = 200, 
		  pE = 0.3707, 
		  pC = 0.4890, 
                  RR = 0.7, 
		  alpha = 0.05)

Example output

[1] 0.6383389

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