WRestimates (version 0.1.0)

wr.power: Power of a Win Ratio

Description

Calculate the power of a win ratio. $$Power = 1 - \Phi(Z[\alpha] - ln(WR[true])(\sqrt{N}/\sigma))$$

Usage

wr.power(N, alpha = 0.025, WR.true = 1, sigma.sqr, k, p.tie)

Value

wr.power returns an object of class "list" containing the following components:

power

Power of the win ratio.

N

Sample size.

alpha

Level of significance.

WR.true

True or assumed win ratio.

sigma.sqr

Population variance of the natural log (\(ln\)) of the win ratio.

k

The proportion of subjects allocated to one group.

p.tie

The proportion of ties.

Arguments

N

Sample size.

alpha

Level of significance (Type I error rate); Default: \(\alpha\) = 0.025.

WR.true

True or assumed win ratio; Default: WR.true = 1 where H₀ is assumed true.

sigma.sqr

Population variance of the natural log (\(ln\)) of the win ratio.

k

The proportion of subjects allocated to one group i.e. the proportion of patients allocated to treatment.

p.tie

The proportion of ties.

Author

Autumn O'Donnell

References

Yu, R. X. and Ganju, J. (2022). Sample size formula for a win ratio endpoint. Statistics in medicine, 41(6), 950-963. doi: 10.1002/sim.9297.

See Also

wr.sigma.sqr

Examples

## N = 100 patients, 1:1 allocation, one-sided alpha = 2.5%, small
## proportion of ties p.tie = 0.1, and 50% more wins on treatment
## than control.

### Calculate the Power
wr.power(N = 100, WR.true = 1.5, k = 0.5, p.tie = 0.1)