c212 (version 0.98)

c212.DFDR: Implementation of the Double False Discovery Rate for controlling the False Discovery Rate.

Description

The Double False Discovery Rate is designed to take advantage of possible groupings which may exist within sets of hypotheses. It applies the BH-procedure twice. Once at the group level, to identify sets of hypotheses which may contain significant hypotheses. It then groups these hypotheses together to form a single family and applies the BH-procedure again to declare hypotheses significant.

Usage

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

Arguments

trial.data

File or data frame containing the p-values for the hypotheses being tested. The data must contain the following columns: B: the index or name of the groupings; p: 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 Double False Discovery Rate process.

References

Mehrotra, D. V. and Adewale, A. J. (2012). Flagging clinical adverse experiences: reducing false discoveries without materially compromising power for detecting true signals. Stat Med, 31(18):1918-30.

Examples

# NOT RUN {
trial.data <- data.frame(B = c(1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4),
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.DFDR(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
# }