pim.fit: Fit proportional interactions model
In anoint: Analysis of Interactions

Description Usage Arguments Details Value Author(s) References See Also Examples

Fits a single proportional interactions model for generalized linear or Cox regression model.

1	pim.fit(formula, trt, data, family="binomial", na.action=na.omit, ...)

`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`
`...`	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

interaction: value of the interaction effect of the proportional interaction model, see details
LRT: value of likelihood ratio test of proportional interaction
lower: lower endpoint of 95 percent confidence interval for interaction parameter
upper: upper endpoint of 95 percent confidence interval for interaction parameter
pvalue: pvalue for 1-df chi-squared test
model0: model object for control group
model1: model object for treatment group

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

pim.subsets

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