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 |

`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.`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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
# 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")
``` |

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