sampleSize {clippda} | R Documentation

## A function for sample size calculations

### Description

This generic function `sampleSize` calculates the protein variance and the sample size required to estimate the clinically important differences (`DIFF`). The input data are the consensus parameters of peaks with medium biological variation.

### Usage

    sampleSize(Data,intraclasscorr,signifcut, ...)

### Arguments

`Data` | An object of `aclinicalProteomicsData` class.
`intraclasscorr` | An object of `numeric` class. It is a known value of the intraclass correlation, or an estimate from a pilot data.
`signifcut` | An object of `numeric` class. It is significance threshold (usually, taken to be 0.05 in the analysis of the protein profiling studies).
`...` | Some methods for this generic function may take additional, optional arguments. At present none do.

### Details

The sample sizes are computed for various combinations of the power with values `beta=c(0.90,0.80,0.70)` and the significance values, `alpha = c(0.001, 0.01,0.05)`. Note that here we use `beta` for power rather than the conventional `1-beta`.

### Value

`protein_variance` | consensus protein variance
`replicate_correlation` | consensus intraclass correlation
`sample_size` | the sample size required

### Author(s)

Stephen Nyangoma

### References

Nyangoma SO, Ferreira JA, Collins SI, Altman DG, Johnson PJ, and Billingham LJ: Sample size calculations for planning clinical proteomic profiling studies using mass spectrometry. Bioinformatics (Submitted)

Smyth GK, et al.: Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 2005, 21, 2067 - 75

Smyth GK: Linear models and emperical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004, 3, 1, Article 3

### Examples

    ########################################################################
    ## SAMPLE SIZE
    #######################################################################
    #The function sampleSize calculates the biological variance, differences.
    #These are the consensus values of peaks with median biological variation
    # It also gives sample sizes for beta=c(0.90,0.80,0.70) and alpha = c(0.001, 0.01,0.05)

    ####################################################################
    ####################################################################
    ####################################################################

    intraclasscorr  <-  0.60 #cut-off for intraclass correlation

    signifcut <- 0.05      #significance cut-off

    data(liverdata)

    data(liver_pheno)

    OBJECT=new("aclinicalProteomicsData")

    OBJECT@rawSELDIdata=as.matrix(liverdata)
    OBJECT@covariates=c("tumor" ,    "sex")
    OBJECT@phenotypicData=as.matrix(liver_pheno)
    OBJECT@variableClass=c('numeric','factor','factor')
    OBJECT@no.peaks=53

    sampleSize(OBJECT,intraclasscorr,signifcut)

    ####################################################################
    ####################################################################
    ####################################################################

[Package clippda version 1.40.0 Index]