mim.fit: Fits and global tests of analysis of interaction models
In anoint: Analysis of Interactions

Description Arguments Details Objects from the Class Slots Methods Author(s) See Also Examples

Fits one-by-one (OBO), unrestricted (UIM), and proportional interaction (PIM) regression models to investigate multiple treatment response factors in a parallel-group clinical trial.

`object`	object of `anoint` class
`level`	significance level for global interaction tests
`interval`	interval of possible values for responsiveness parameter of PIM

The global tests for the presence of treatment response factors (treatment-covariate interaction) are one-stage or two-stage likelihood ratio tests.

The fitted multiple interaction models include: one-by-one univariate interaction models (OBO), a full unrestricted model with all pairwise treatment-covariate interactions (UIM), and a proportional interactions model (PIM) fit with an exact or asymptotic approximate estimate for the likelihood ratio test and responsiveness parameter, theta.

Objects can be created by calls of the form anoint.fit(object, level = .05, interval=c(.5,3))

K: number of prognostic factors
responsiveness: list with exact and approximate estimates of PIM responsiveness parameter
tests: list of global interaction test results
pvalues: list of pvalues on which test rejections are based
fits: list of fitted models for each anoint method

Components of tests are the results of the global tests of interaction:

obo.reject: Result of unadjusted one-by-one global test of interaction. Null is no effect modification for K subgroups, the alternative is at least one K is an effect modifier.
obo.adjust: Same as obo.reject but with Bonferroni-correction for K comparisons
uim.reject: Result of UIM global test of interaction. Null is no effect modification for K subgroups, the alternative is at least one K is an effect modifier.
pim.exact.reject: Result of PIM exact global test of interaction. Null is no proportional effect modification (theta responsiveness parameter = 1) against the alternative that the treatment responsiveness parameter theta is not equal to 1.
pim.approx.reject: Same as pim.exact.reject but using approximate method.
pim.obo: Two-stage global test. First stage tests PIM using an exact method at level/2 significance. If not rejected, the second stage is a test of adjusted OBO with a second-stage global level/2 significance.
pim.uim: Same as pim.obo but with UIM at the second stage.

Components of pvalues on which the global tests are based:

obo.p: p-value for the maximum LRT of the one-by-one testing
uim.p: p-value for the global LRT of any interaction base on UIM
pim.exact.p: p-value for the test of proportional interaction using the PIM exact method
pim.approx.p: p-value for the test of proportional interaction using the PIM approximate method

Components of fits are the models underlying the global interaction tests:

obo: Univariate interaction regression models of each subgroup.
uim: Full regression model with all pairwise treatment-covariate interactionns
pim.exact: Proportional interactions model with exact fit
pim.approx: Proportional interactions model with asymptotic approximate estimation

show: signature(object = "anoint.fit"): Display table of results of global test of interaction.
print: signature(x = "anoint.fit",...): Display table of results of global test of interaction.
summary: signature(object = "anoint.fit",...): Display results of global test of interaction and p-values. Returns list with tests and pvalues.
fits: signature(object = "anoint.fit",type=c("obo","uim","pim.exact","pim.approx"): Extracts the specified fitted object from a anoint.fit.

S. Kovalchik s.a.kovalchik@gmail.com

anoint,anoint-class,obo,uim,pim

# 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
                             )

object <- anoint(Surv(y, event)~(V1+V2)*trt,data=null.interaction,family="coxph")

fit <- anoint.fit(object)

summary(fit)

fits(fit,type="obo")