pim.subsets: Perform all subsets proportional interactions modeling
In anoint: Analysis of Interactions

Description Usage Arguments Details Value Author(s) References Examples

Computes all possible proportional interactions model among p covariates.

1	pim.subsets(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`

Under the proportional interaction model the coef of the main covariate effects in the control arm are multiplied by the interaction effect to get the covariate effects for the treatment group.

Returns a list with

subset: indicator of the covariates included in the fitted model
interaction: value of the interaction effect of the proportional interaction model, see details
LRT: value of likelihood ratio test of proportional 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 subsets and columns as covariates with logical entries indicating which covariates (columns) were include in which subset model (row)
covariates: vector of covariate names as in formula
reject: indicator of rejected hypotheses using a multiple testing correction such that familywise error is controlled at level fwer

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

Follmann DA, Proschan MA. A multivariate test of interaction for use in clinical trials. Biometrics 1999; 55(4):1151-1155

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)

pim.subsets(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"
                             )

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