anoint (version 1.4)

anoint.fit: Fits and global tests of analysis of interaction models

Description

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.

Arguments

object

object of anoint class

level

significance level for global interaction tests

interval

interval of possible values for responsiveness parameter of PIM

Objects from the Class

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

Slots

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

Methods

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.

Details

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.

See Also

anoint,anoint-class,obo,uim,pim

Examples

# 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")