epi.surv {SimHap} | R Documentation |
epi.surv
is used to fit Cox proportional hazards models to epidemiological survival data.
epi.surv(formula, pheno, sub = NULL)
formula |
a symbolic description of the model to be fit. The response must be a survival object as returned by the Surv function. |
pheno |
a dataframe containing phenotype data. |
sub |
an expression representing a subset of the data on which to perform the models. |
formula
should be in the form of response ~ predictor(s)
. A formula has an implied intercept term. See documentation for the formula
function for more details of allowed formulae.
epi.surv
returns an object of class epiSurv
containing the following items
results |
a table containing the hazard ratios, confidence intervals and p-values of the parameter estimates. |
formula |
formula passed to epi.surv . |
Wald |
The Wald test for overall significance of the fitted model including SNP parameters. |
logLik |
the log-likelihood for the linear model fit using formula . |
fit.coxph |
an object of class coxph fit using formula1 . See coxph.object for details. |
rsquared |
r-squared values for the fitted model. |
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(survPheno.dat) mymodel <- epi.surv(formula=Surv(time, status)~age, pheno=survPheno.dat) summary(mymodel) # example with subsetting variable mymodel <- epi.surv(formula=Surv(time, status)~age, pheno=survPheno.dat, sub=expression(sex==1)) summary(mymodel)