sglr-package: An R package for power and boundary calculations in...

Description Details Author(s) References Examples

Description

This package is an implementation of the methodology of Shih, Lai, Heyse, and Chen (see reference below) for computing Generalized Likelihood Ratio test boundaries in pre-licensure vaccine studies

Details

Package: sglr
Type: Package
Version: 0.05
Date: 2010-04-20
License: GPL (version 2 or later)
LazyLoad: yes

The package provides several functions. The function glrSearch computes boundaries for testing a given p_0 versus p_1 (specified as a two-dimensional vector) given a significance level α and a type II error β. The function computeBoundary computes the boundary in terms of a more understandable and usable quantity, such as the number of adverse events in a pre-licensure vaccine study for example. It takes as input a set of given boundaries for the GLR statistic. The third function is plotBoundary which also takes the same arguments as computeBoundary and produces a plot. The last two functions can make use of statistics computed previously for the problem, which can be specified as an argument; otherwise, the statistics are computed from scratch.

Author(s)

Balasubramanian Narasimhan with input from Tze Lai and Mei-Chiung Shih. Maintainer: Balasubramanian Narasimhan <naras@stat.stanford.edu>

References

Mei-Chiung Shih, Tze Leung Lai, Joseph F. Heyse, and Jie Chen. Sequential Generalized Likelihood Ratio Tests for Vaccine Safety Evaluation (Statistics in Medicine, Volume 29, issue 26, p.2698-2708, 2010.)

Please also consult the website http://med.stanford.edu/biostatistics/ClinicalTrialMethodology/ for further developments.

Examples

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library(sglr)
result <- glrSearch(p=c(.5, .75), alpha=0.05, beta=0.10)
## print(result)  ## large amounts of output possible!
result[1:3]

Example output

Loading required package: ggplot2
Loading required package: shiny
$b1
             beta     beta     beta     beta 
2.995732 2.802585 2.302585 2.802585 2.802585 

$b0
[1] 2.302585 2.995732 2.995732 2.995732 3.495732

$estimate
              alpha       beta
         0.06558493 0.12533833
estimate 0.06971572 0.06474775
estimate 0.06558493 0.12533833
estimate 0.06971572 0.06474775
estimate 0.04094917 0.06791892

sglr documentation built on May 1, 2019, 7:14 p.m.