pim.subsets: Perform all subsets proportional interactions modeling

In anoint: Analysis of Interactions

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

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.

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

.

Author(s)

Stephanie Kovalchik <[e-mail]>

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")

anoint documentation built on Sept. 11, 2024, 8:49 p.m.