bcmmrm: Model Median Inference for Longitudinal Data in Randomized...

In kzkzmr/bcmixed: Mixed Effect Model with the Box-Cox Transformation

Model Median Inference for Longitudinal Data in Randomized Clinical Trials.

Description

bcmmrm provides inference results for the model median differences between treatment groups proposed by Maruo et al. (2017), which focuses on continuous and positive longitudinally observed outcomes and a situation where the efficacy of some treatments is compared based on a randomized, parallel group clinical trial. If time and id are not specified, inference results reduce to the results for the context of linear regression model provided by Maruo et al. (2015).

Usage

bcmmrm(
  outcome,
  group,
  data,
  time = NULL,
  id = NULL,
  covv = NULL,
  cfactor = NULL,
  structure = "UN",
  conf.level = 0.95,
  lmdint = c(-3, 3),
  glabel = NULL,
  tlabel = NULL
)

Arguments

outcome	a name of positive outcome (dependent) variable included in data.
group	a name of treatment group variable included in data.
data	a data frame that may include outcome, group, time, id, and specified covariate variables.
time	a name of time variable for repeated measurements included in data. The default is NULL.
id	a name of subject id variable for repeated measurements included in data. The default is NULL.
covv	a character vector with names of covariate variables included in data. The default is NULL.
cfactor	an integer vector including nominal variable indicators for covariate variables. Nominal variable: 1, continuous variable: 0. The default is NULL.
structure	specify the covariance structure from c("UN", "CS", "AR(1)"). The default is "UN".
conf.level	a numeric value of the confidence level for the confidence intervals. The default is 0.95.
lmdint	a vector containing the end-points of the interval to be searched for a transformation parameter. The default is c(-3, 3).
glabel	a vector of length number of treatment groups containing the labels of group variable. The default is NULL and the levels of group variable in data are used.
tlabel	a vector of length number of repeated measures containing the labels of time variable. The default is NULL and the levels of time variable in data are used.

Value

an object of class "bcmmrm" representing the results of model median inference based on the Box-Cox transformed MMRM model. Generic functions such as print, plot, and summary have methods to show the results of the fit. See bcmmrmObject for the components of the fit.

Note

If baseline observation for the outcome variable is available, Box-Cox transformed baseline should be included as a covariate for accuracy of estimation.
Although this function can be applied to non-randomized trial data, performances of the above approach have not evaluated in context of non-randomized trials.

References

• Maruo, K., Isogawa, N., Gosho, M. (2015). Inference of median difference based on the Box-Cox model in randomized clinical trials. Statistics in Medicine, 34, 1634-1644, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.6408")}.
  

• Maruo, K., Yamaguchi, Y., Noma, H., Gosho, M. (2017). Interpretable inference on the mixed effect model with the Box-Cox transformation. Statistics in Medicine, 36, 2420-2434, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.7279")}.

See Also

bcmarg, bcmmrmObject

Examples

data(aidscd4)

# covariate: Box-Cox transformed baseline (continuous) and sex (nominal),
# covariance structure: AR(1) structure
# *Note: The UN structure is preferred although the AR(1)
#        structure is used in this example to reduce calculation time

# Box-Cox transformation for the baseline
aidscd4$cd4.bl.tr <- bct.v(aidscd4$cd4.bl)$transformed

# Median inference for each group and week
bcmmrm(outcome = cd4, group = treatment, data = aidscd4, time = weekc,
       id = id, covv = c("cd4.bl.tr", "sex"), cfactor = c(0, 1),
       structure = "AR(1)", glabel = c("Zid/Did", "Zid+Zal", "Zid+Did",
       "Zid+Did+Nev"))

kzkzmr/bcmixed documentation built on Oct. 18, 2023, 10:32 p.m.