stmodelGLM-class: Class '"stmodelGLM"'

In stepp: Subpopulation Treatment Effect Pattern Plot (STEPP)

Class "stmodelGLM"

Description

This is the stepp model for data arising from the following Generalized Linear Models: 1. gaussian with identity link, 2. binomial with logit link, and 3. Poisson with log link.

One can specify additional covariates in the model using model.matrix in R.

Value

The new method returns the stmodelGLM object.

The estimate method returns a list with the following fields:

model	the stepp model - "GLMGe" - Gaussian model, "GLMBe" - binomial model, and "GLMPe" - Poisson model
sObs1	a vector of effect estimates of all subpopulations based on the first treatment
sSE1	a vector of standard errors of effect estimates of all subpopulations based on the first treatment
oObs1	effect estimate of the entire population based on the first treatment
oSE1	the standard error of the effect estimate of the entire population based on the first treatment
sObs2	a vector of effect estimates of all subpopulations based on the first treatment
sSE2	a vector of standard errors of effect estimates of all subpopulations based on the first treatment
oObs2	effect estimate of the entire population based on the first treatment
oSE2	the standard error of the effect estimate of the entire population based on the first treatment
sglmw	Wald's statistics for the effect estimate differences between the two treatments
RD	a vector of effect estimates difference of all subpopulations between the two treatments
RDSE	a vector of the standard error effect estimates difference of all subpopulations between the two treatments
ORD	overall difference of effect estimates of the entire population between the two treatments
ORDSE	the standard error of the overall difference of the effect estimates of the entire population between the two treatments
logR	a vector of log ratio of effect estimates of all subpopulations between the two treatments
logRSE	a vector of standard error of log ratio of effect estimates of all subpopulations between the two treatments
ologR	log ratio of effect estimates of the entire population between the two treatments
ologRSE	the standard error of the log ratio of effect estimates of the entire population between the two treatments
sglmlogrw	Wald's statistics for the log ratio of effect estimates between the two treatments

The test method returns a list with the following fields:

model	the stepp model - "GLMt"
sigma	the covariance matrix for subpopulations based on effect differences
hasigma	the homogeneous association covariance matrix for subpopulations based on effect differences
HRsigma	the covariance matrix for the subpopulations based on hazard ratio
haHRsigma	the homogeneous association covariance matrix for subpopulations based on hazard ratio
pvalue	the supremum pvalue based on effect difference
chi2pvalue	the chisquare pvalue based on effect difference
hapvalue	the homogeneous association pvalue based on effect difference
HRpvalue	the supremum pvalue based on hazard ratio
haHRpvalue	the homogeneous association pvalue based on hazard ratio

Objects from the Class

Objects can be created by calls of the form new("stmodelGLM", ...) or by
the construction function stmodel.GLM.

Slots

coltrt:

Object of class "numeric"
the treatment variable

colY:

Object of class "numeric"
a vector containing the outcome

trts:

Object of class "numeric"
a vector containing the codes for the 2 treatment groups, first and second treatment groups, respectively

MM:

Object of class "ANY"
a model matrix for extra adjustment covariates; default is NULL
currently, stepp can only support covariates with numeric values, no logical values or factors allowed
these values need to be converted to numeric with some encoding schemes

glm:

Object of class "character"
the glm to be used for analysis: "gaussian", "binomial", "poisson"

link:

Object of class "character"
the link function; reserved for future use

Extends

Class "stmodel", directly.

Methods

estimate

signature(.Object = "stmodelGLM"):
estimate the effect in absolute and relative scale of the overall and each subpopulation

print

signature(.Object = "stmodelGLM"):
print the estimate, covariance matrices and statistics

test

signature(.Object = "stmodelGLM"):
perform the permutation tests or GEE and obtain various statistics

Author(s)

Wai-Ki Yip

See Also

stwin, stsubpop, stmodelKM, stmodelCI, steppes, stmodel, stepp.win, stepp.subpop, stepp.KM, stepp.CI, stepp.GLM, stepp.test, estimate, generate

Examples

showClass("stmodelGLM")

stepp documentation built on June 18, 2022, 5:06 p.m.