| snp.surv {SimHap} | R Documentation |
snp.surv is used to fit cox proportional hazards models to single SNP genotype and phenotype survival data.
snp.surv(formula1, formula2, geno, pheno, sub = NULL)
formula1 |
a symbolic description of the full model to be fit, including SNP parameters. The response must be a survival object as returned by the Surv function. |
formula2 |
a symbolic description of the nested model excluding SNP parameters, to be compared to formula1 in a likelihood ratio test. The response must be a survival object as returned by the Surv function. |
geno |
a dataframe containing genotype data. |
pheno |
a dataframe containing phenotype data. |
sub |
an expression representing a subset of the data on which to perform the models. |
formula1 should be in the form: response ~ predictor(s) + SNP(s) and formula2 should be in the form: response ~ predictor(s). A formula has an implied intercept term. See documentation for the formula function for more details of allowed formulae.
snp.surv returns an object of class snpSurv.
The summary function can be used to obtain and print a
summary of the results.
An object of class snpSurv is a list containing the
following components:
results |
a table containing the hazard ratios, confidence intervals and p-values of the parameter estimates. |
formula |
formula1 passed to snp.surv. |
Wald |
The Wald test for overall significance of the fitted model including SNP parameters. |
logLik |
the log-likelihood for the model fit using formula1. |
fit.coxph |
an object of class coxph fit using formula1. See coxph.object for details. |
rsquared |
r-squared values for models fit using formula1 and formula2. |
Pamela A. McCaskie
Andersen, P., Gill, R. (1982) Cox's regression model for counting processes, a large sample study, Annals of Statistics, 10:1100-1120.
McCaskie, P.A., Carter, K.W, Hazelton, M., Palmer, L.J. (2007) SimHap: A comprehensive modeling framework for epidemiological outcomes and a multiple imputation approach to haplotypic analysis of population-based data, [online] www.genepi.org.au/simhap.
Therneau, T., Grambsch, P., Fleming, T. Martingale based residuals for survival models, Biometrika, 77(1):147-160.
data(SNPsurv.dat)
# transforms SNPlong.dat to an object containing 3 columns
# per SNP - additive, dominant and recessive, where genotypes
# defined in 'baseline' serve as the baseline genotypes
survGeno.dat <- SNP2Geno(SNPsurv.dat, baseline=c("V2V2", "GG", "CC"))
data(survPheno.dat)
mymodel <- snp.surv(formula1=Surv(time, status)~age+SNP_1_add,
formula2=Surv(time, status)~age, geno=survGeno.dat,
pheno=survPheno.dat)
summary(mymodel)
# example with subsetting variable
mymodel <- snp.surv(formula1=Surv(time, status)~age+SNP_1_add,
formula2=Surv(time, status)~age, pheno=survPheno.dat,
geno=survGeno.dat, sub=expression(sex==1))