MCPerm (version 1.1.4)

I2.MCPerm: Calculate p.value for Heterogeneity statistics I2 in meta analysis

Description

Calculate p.value for Heterogeneity statistics I2 in meta analysis.

Usage

I2.MCPerm(case_11, case_12, case_22, control_11, control_12, control_22,  model = "allele", method = "MH", repeatNum = 1000)

Arguments

case_11

a non-negative integer, the frequency of genotype "allele1/allele1" in case samples.

case_12

a non-negative integer, the frequency of genotype "allele1/allele2" in case samples.

case_22

a non-negative integer, the frequency of genotype "allele2/allele2" in case samples.

control_11

a non-negative integer, the frequency of genotype "allele1/allele1" in control samples.

control_12

a non-negative integer, the frequency of genotype "allele1/allele2" in control samples.

control_22

a non-negative integer, the frequency of genotype "allele2/allele2" in control samples.

model

a character string indicating the type of model("allele"(default),"dominant" or "recessive") supplied to the data. The risk allele(see details) is marked as allele1. The allele model indicates allele1 versus allele2, the dominant model indicates + versus , the recessive model indicates versus + .

method

a character string indicating the method('Inverse','MH'(default) or 'Peto') to calculate Q value. See details.

repeatNum

an integer(default 1000) specifying the number of replicates used in the Monte Carlo permutation.

Value

risk_allele

the symbol of risk allele.See details.

I2

the I2 statistics for observed meta data.

corrected_I2p

the p value for I2, the percentage of more than I2 value.

Details

Allele 1 and allele 2 to each study have OR values. The risk allele is the allele which the number of studies which OR>1 more than half of the number of all studies.

I2 is calculated by formula I2=max(Q-d.f./Q, 0), considering I2=1-24 moderate heterogeneity; I2=50-74

MCPerm details see chisq.MCPerm.

References

Julian P.T.Higgins, Simon G.Thompson(Statistics in Medicine,2002): Quantifying heterogeneity in a meta-analysis.

Julian P.T.Higgins, Simon G.Thompson, Jonathan J Deeks(BMJ,2003):Measuring inconsistency in meta-analyses.

See Also

meta.MCPerm, meta.TradPerm, Q.TradPerm, I2.TradPerm, chisq.MCPerm, chisq.TradPerm, VS.Hist, VS.KS, VS.Allele.Hist, VS.Genotype.Hist, PermMeta.LnOR.Hist, PermMeta.LnOR.CDC, PermMeta.Hist

Examples

## import data
# data(MetaGenotypeCount)
## delete the first line which is the names for columns.
# temp=MetaGenotypeCount[-1,,drop=FALSE]
# result=I2.MCPerm(case_11=as.numeric(temp[,14]),case_12=as.numeric(temp[,16]),
	 # case_22=as.numeric(temp[,18]),control_11=as.numeric(temp[,15]),
	 # control_12=as.numeric(temp[,17]),control_22=as.numeric(temp[,19]),
	 # model="allele",method="MH",repeatNum=100000)
# result