c212LSL: Implementaion of the least-slope estimator estimator (LSL)...

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

Implementaion of the least-slope estimator estimator (LSL) for the proportion of true null hypotheses.

Description

The least-slope estimator estimator (LSL) is one of a number of estimators of the proportion of true null hypotheses. This implementation assumes a grouped structure for the data.

Usage

c212.LSL(trial.data)

Arguments

trial.data

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.

Value

An estimate of the proportion of true null hypotheses.

Note

The implementation is that described in Hu, J. X. and Zhao, H. and Zhou, H. H. (2010).

Author(s)

R. Carragher<[e-mail]>

References

Hu, J. X. and Zhao, H. and Zhou, H. H. (2010). False Discovery Rate Control With Groups. J Am Stat Assoc, 105(491):1215-1227.

Benjamini Y, Hochberg Y. (2000). On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics. Journal of Educational and Behavioral Statistics, 25(1):60–83.

Examples

data(c212.FDR.data)
lsl <- c212.LSL(c212.FDR.data)
print(lsl)
## Not run: 
          B       pi0
1 Bdy-sys_5 1.0000000
2 Bdy-sys_6 1.0000000
3 Bdy-sys_7 1.0000000
4 Bdy-sys_8 1.0000000
5 Bdy-sys_2 1.0000000
6 Bdy-sys_3 0.2857143
7 Bdy-sys_4 1.0000000
8 Bdy-sys_1 1.0000000

## End(Not run)

c212 documentation built on April 3, 2025, 10:29 p.m.