anoint (version 1.4)

data.anoint: Generate a clinical trial data set under a specified multiple interaction model

Description

Returns a simulated equal-allocation, parallel-group clinical trial with possibly multiple interactions among prognostic factors.

Usage

data.anoint(alpha, beta, gamma, mean, vcov, n=100, event=.8,
type = c("binomial","survival"))

Arguments

alpha

vector specifying control and treatment group intercept

beta

vector specifying prognostic factor main effects

gamma

vector specifying modification of prognostic effects in the presence of treatment

mean

vector of covariate mean to be supplied to mvrnorm of the MASS package.

vcov

matrix of variance-covariance matrix of prognostic covariates to be supplied to mvrnorm

n

number of subjects in each treatment arm

event

proportion of observed events when type is "survival"

type

string indicating type of response variable

Value

Data frame with y, trt, and V1,...,VK prognostic factors. The data frame also has event with a time-to-event reponse, which is an indicator for an observed event.

Details

When type is "survival", the parameters specify the log-rate for an exponentially distributed random variable. Censored times are non-informatively right-censored. When type is "binomial", the parameters specify a log-odds model.

Examples

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

fit <- glm(y~(.)*trt,data=null.interaction,family="binomial")

summary(fit)


null.interaction <- data.anoint(
                             alpha = c(log(.5),log(.5*.75)),
                             beta = c(1.5,2),
                             gamma = rep(1,2),
                             mean = c(0,0),
                             vcov = diag(2),
                             type="survival",
                             event = .7
                             )

fit <- coxph(Surv(y, event)~(.)*trt,data=null.interaction)

summary(fit)