pim: Fit proportional interaction model
In anoint: Analysis of Interactions

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

Fits a proportional interactions model from parallel-group clinical trial.

1	pim(object,exact=TRUE,interval=c(-3,3),n.boot=NULL,...)

`object`	object of class `anoint`
`exact`	logical indicator whether `exact` or approximate fit is performed
`interval`	interval passed to `optimize` if `exact` is `TRUE`
`n.boot`	number of bootstrap resamples for variance calculations
`...`	other arguments passed to `optimize` if `exact` is `TRUE`

When exact is FALSE the method of Follmann and Proschan (1999) is used to estimate the PIM coefficients and perform a likelihood-ratio test on the responsiveness parameter theta.

If exact method is specified, then optimize is used to maximize the profile-likelihood alternating between fixing theta and fixing all other PIM parameters. The arguments interval and additional arguments to ... control the optimization with respect to theta.

When n.boot is NULL no bootstrap resamples are taken. In this case, when using the exact method the variance-covariance for the main effects is based on the model likelihood treating the responsiveness parameter as fixed. To include uncertainty measures for the responsiveness parameter, bootstrap resampling can be used. For the approximate method, only the bootstrap resampling variance is provided for the vcov and confint methods, which is invoked by specifying a positive integer number of samples or n.boot.

Returns instance of pim class.

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-class

set.seed(1115)

pim.interaction <- data.anoint(
                             alpha = c(log(.2/.8),log(.2*.75/(1-.2*.75))),
                             beta = log(c(1.25,1.5)),
                             gamma = rep(1.2,2),
                             mean = c(0,0),
                             vcov = diag(2),
                             type="binomial", n = 500
                             )


object <- anoint(y~(V1+V2)*trt,data=pim.interaction,family="binomial")

object

fit <- pim(object)

fit

summary(fit)

# EXAMPLE WITH BOOTSTRAP 
fit <- pim(object, n=50)
summary(fit)
confint(fit)