a1Table: Function to produce tables of alpha1
In adaptTest: Adaptive two-stage tests

Description Usage Arguments Details Value Author(s) References See Also Examples

This function produces tables of alpha1 for a grid of different choices of alpha and alpha0.

1	a1Table(typ, a = NA, a0 = NA, Pocock = FALSE, round = FALSE)

`typ`	type of test: `"b"` for Bauer and Koehne (1994), `"l"` for Lehmacher and Wassmer (1999), `"v"` for Vandemeulebroecke (2006) and `"h"` for the horizontal conditional error function
`a`	vector of different choices of alpha, the overall test level
`a0`	vector of different choices of alpha0, the futility stopping bound
`Pocock`	logical determining whether the "Pocock-type" should be calculated or the full level should be applied after the second stage (see details; default: full level after second stage).
`round`	rounding specification, logical or integer (see details; default: no rounding)

This function produces tables of alpha1 on a grid spanned by the vectors a and a0 (i.e., alpha and alpha0). This is done either for the "Pocock-type" (i.e., under the condition alpha1 = alpha2: Pocock = TRUE) or using the full level after the second stage (alpha = alpha2: Pocock = FALSE (the default)). The function a1Table can be a convenient shortcut for a repeated use of tsT; see this latter function for further details.

The result is rounded to round digits after the comma (round = TRUE rounds to 1 digit; round = FALSE and round = 0 prevent rounding).

a1Table returns a matrix of alpha1 values, with the corresponding alpha and alpha0 values being displayed as dimnames.

Marc Vandemeulebroecke

Bauer, P., Koehne, K. (1994). Evaluation of experiments with adaptive interim analyses. Biometrics 50, 1029-1041.

Lehmacher, W., Wassmer, G. (1999). Adaptive sample size calculations in group sequential trials. Biometrics 55, 1286-1290.

Vandemeulebroecke, M. (2006). An investigation of two-stage tests. Statistica Sinica 16, 933-951.

adaptTest package description, tsT

## Produce basic reference tables for the test by Vandemeulebroecke (2006)
alpha  <- c(0.1, 0.05, 0.025, 0.01)
alpha0 <- 1:10/10
a1Table(typ="v", a=alpha, a0=alpha0, Pocock=FALSE)
a1Table(typ="v", a=alpha, a0=alpha0, Pocock=TRUE)