FSFWER.arbidept.p.adjust: Adjusted P-values for Fixed Sequence FWER Controlling...
In allenzhuaz/FixSeqMTP: Fixed Sequence Multiple Testing Procedures

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

Given a set of pre-ordered p-values and accuracy for the result, returns adjusted p-values using one of three generalized fixed sequence FWER controlling procedures. The function also provides an option to make decisions given a pre-specified significant level α.

1 2	FSFWER.arbidept.p.adjust(p, alpha=0.05, beta=0.5, tol = 1e-6, method = c("reject","accept","both"), make.decision = TRUE)

`p`	numeric vector of p-values (possibly with `NA`s). Any other R is coerced by `as.numeric`. Same as in `p.adjust`.
`alpha`	significant level used to compare with adjusted p-values to make decisions, the default value is 0.05.
`beta`	pre-specified constant satisfying 0 ≤ β <1, only for `method="accept"`. The default value is `0.5`.
`tol`	desired accuracy. The default value is `1e-6` .
`method`	adjustment method. See details.
`make.decision`	logical; if `TRUE` (default), then the output include the decision rules compared adjusted p-values with significant level alpha

The adjustment methods for Fixed Sequence multiple testing include Procedure A1 only using numbers of rejections ("reject"), Procedure A2 only using numbers of acceptances ("accept") and Procedure A3 using both numbers of rejections and numbers of acceptances ("both"). The three methods strongly control FWER under arbitrary dependence. The constant beta needs to be specified for the Procedure A2 ("accept"), while one can ignore this argument when using other methods.

A numeric vector of the adjusted p-values (of the same length as p) if make.decision = FALSE, or a data frame including original p-values, adjusted p-values and decision rules if make.decision = TRUE.

Yalin Zhu

Qiu, Z., Guo, W., & Lynch, G. (2015). On generalized fixed sequence procedures for controlling the FWER. Statistics in medicine, 34(30), 3968-3983.

FSFDR.arbidept.p.adjust and FSFDR.arbidept.p.adjust for fixed sequence FDR controlling procedures.

  ## Clinical trial example in Qiu et al. (2015)
Pval <- c(0.0008, 0.0135, 0.0197, 0.7237, 0.0003, 0.2779, 0.0054, 0.8473)
FSFWER.arbidept.p.adjust(p=Pval, alpha=0.05, method = "reject")
FSFWER.arbidept.p.adjust(p=Pval, alpha=0.05, beta=0.1, method = "accept")
FSFWER.arbidept.p.adjust(p=Pval, alpha=0.05, beta=0.5, method = "accept")
FSFWER.arbidept.p.adjust(p=Pval, alpha=0.05, beta=0.9, method = "accept")
FSFWER.arbidept.p.adjust(p=Pval, alpha=0.05, method = "both")