anoint (version 1.4)

pim.subsets: Perform all subsets proportional interactions modeling

Description

Computes all possible proportional interactions model among p covariates.

Usage

pim.subsets(formula,trt,data,family="binomial",na.action=na.omit,fwer=0.05,...)

Arguments

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

Value

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

.

Details

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.

References

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

Examples

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")