c212BH: Implementation of Benjamini-Hochberg procedure for False...

In c212: Methods for Detecting Safety Signals in Clinical Trials Using Body-Systems (System Organ Classes)

Description

Implementaion of Benjamini-Hochberg procedure for False Discovery Rate control. The hypotheses' data can be contained in a file or data frame.

Usage

c212.BH(trial.data, alpha = 0.05)

Arguments

trial.data	File or data frame containing the p-values for the hypotheses being tested. The data must include a column called p which contains the p-values of the hypotheses.
alpha	The level for FDR control. E.g. 0.05.

Value

The subset of hypotheses in file or trial.data deemed significant by the Benjamini-Hochberg procedure.

Note

No check is made for duplicate rows in the input file or data frame.

Author(s)

R. Carragher<[e-mail]>

References

Benjamini, Yoav and Hochberg, Yosef, (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1):289-300.

Examples

trial.data <- data.frame(B = c(1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4),
j = c(1, 1, 2, 3, 4, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5),
AE = c("AE1", "AE2", "AE3", "AE4", "AE5", "AE6", "AE7", "AE8", "AE9", "AE10", "AE11",
"AE12", "AE13", "AE14", "AE15", "AE16", "AE17"),
p = c(0.135005, 0.010000, 0.001000, 0.005000, 0.153501, 0.020000, 0.0013, 0.0023,
0.011, 0.023000, 0.016, 0.0109, 0.559111, 0.751986, 0.308339, 0.837154, 0.325882))


c212.BH(trial.data, 0.05)


## Not run: 
   B j   AE      p
1  2 2  AE3 0.0010
2  3 2  AE7 0.0013
3  3 3  AE8 0.0023
4  2 3  AE4 0.0050
5  2 1  AE2 0.0100
6  3 7 AE12 0.0109
7  3 4  AE9 0.0110
8  3 6 AE11 0.0160
9  3 1  AE6 0.0200
10 3 5 AE10 0.0230

## End(Not run)

c212 documentation built on Sept. 8, 2020, 5:07 p.m.