pim.fit {anoint} | R Documentation |
Fit proportional interactions model
Description
Fits a single proportional interactions model for generalized linear or Cox regression model.
Usage
pim.fit(formula, trt, data, family="binomial", na.action=na.omit, ...)
Arguments
formula |
formula for covariate model as given in |
trt |
character name of treatment assignment indicator |
data |
data.frame containing the variables of |
family |
character specifying family of |
na.action |
function, na.action to perform for handling observations with missing variables among variables in formula. Default is |
... |
additional arguments passed to |
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
- 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
Author(s)
Stephanie Kovalchik <s.a.kovalchik@gmail.com>
References
Follmann DA, Proschan MA. A multivariate test of interaction for use in clinical trials. Biometrics 1999; 55(4):1151-1155
See Also
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.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")