anoint.subgroups: Perform one-by-one subgroup analyses
In anoint: Analysis of Interactions

Description Usage Arguments Value Author(s) Examples

Computes all interaction effects one variable at a time.

1	anoint.subgroups(formula,trt,data,family="binomial",na.action=na.omit,fwer=0.05,...)

`formula`	formula for covariate model as given in `glm` or `coxph`, i.e. `y~x1+x2`
`trt`	character name of treatment assignment indicator
`data`	data.frame containing the variables of `formula` and `trt`
`family`	character specifying family of `glm` or character "coxph" if `coxph` model is fit
`na.action`	function, na.action to perform for handling observations with missing variables among variables in formula. Default is `na.omit`
`fwer`	numeric value for the desired familywise error rate, should be between 0 and 1.
`...`	additional arguments passed to `glm` or `coxph`

Returns a list with

subset: indicator of the covariates included in the fitted model
interaction: value of the of treatment-covariate interaction effect (using model with treatment-covariate product term)
LRT: value of likelihood ratio test of treatment-covariate interaction
lower: lower endpoints of 95 percent confidence interval for interaction parameter
upper: upper endpoints of 95 percent confidence interval for interaction parameter
pvalue: pvalue for 1-df chi-squared test
include.exclude.matrix: matrix of same rows as covariates and columns as covariates with logical entries indicating which covariates (columns) were include in the fitted model (row)
covariates: vector of covariate names as in formula
reject: indicator of rejected hypotheses using a Bonferroni multiple testing correction such that familywise error is controlled at level fwer

Stephanie Kovalchik <s.a.kovalchik@gmail.com>

set.seed(11903)

# NO INTERACTION CONDITION, LOGISTIC MODEL

null.interaction <- data.anoint(
                             alpha = c(log(.5),log(.5*.75)),
                             beta = log(c(1.5,2)),
                             gamma = rep(1,2),
                             mean = c(0,0),
                             vcov = diag(2),
                             type="survival", n = 500
                             )

head(null.interaction)

anoint.subgroups(Surv(y, event)~V1+V2,trt="trt",data=null.interaction,family="coxph")


# PROPORTIONAL INTERACTION WITH THREE COVARIATES AND BINARY OUTCOME

pim.interaction <- data.anoint(
			     n = 5000,
                             alpha = c(log(.2/.8),log(.2*.75/(1-.2*.75))),
                             beta = rep(log(.8),3),
                             gamma = rep(1.5,3),
                             mean = c(0,0,0),
                             vcov = diag(3),
                             type="binomial"
                             )

anoint.subgroups(y~V1+V2+V3,trt="trt",data=pim.interaction,family="binomial")