Description Usage Arguments Details Value Author(s) References Examples

Computes all possible proportional interactions model among `p`

covariates.

1 |

`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 |

`fwer` |
numeric value for the desired familywise error rate, should be between 0 and 1. |

`...` |
additional arguments passed to |

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.

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`

.

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ```
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")
``` |

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