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Set key variables

Source: R/utilities.R
set_vars.Rd

This function is used to define the names of key variables within the data.frame's that are provided as input arguments to draws() and ancova().

Usage 

set_vars(
  subjid = "subjid",
  visit = "visit",
  outcome = "outcome",
  group = "group",
  covariates = character(0),
  strata = group,
  strategy = "strategy",
  group_contrasts = NULL
)

Arguments

subjid

The name of the "Subject ID" variable. A length 1 character vector.

visit

The name of the "Visit" variable. A length 1 character vector.

outcome

The name of the "Outcome" variable. A length 1 character vector.

group

The name of the "Group" variable. A length 1 character vector.

covariates

The name of any covariates to be used in the context of modelling. See details.

strata

The name of the any stratification variable to be used in the context of bootstrap sampling. See details.

strategy

The name of the "strategy" variable. A length 1 character vector.

group_contrasts

Optional specification of the treatment-group contrasts to be estimated by ancova(). Either NULL (the default) or a fully named list whose elements are one of:

  • a length-2 character vector c(minuend, subtrahend) giving a pairwise contrast minuend - subtrahend between two levels of group; or

  • a numeric weight vector over the group levels (summing to zero), either named by the levels of group (unlisted levels default to 0) or of the same length as the number of levels (in factor order).

Each element must be named; the name is used as the output parameter name (and must not start with lsm_, which is reserved for the least-squares means). See details.

Value

A vars object; a named list of class ivars recording the names of the key variables (subjid, visit, outcome, group, covariates, strata and strategy) used throughout rbmi by functions such as draws(), ancova() and analyse().

Details

In both draws() and ancova() the covariates argument can be specified to indicate which variables should be included in the imputation and analysis models respectively. If you wish to include interaction terms these need to be manually specified i.e. covariates = c("group*visit", "age*sex"). Please note that the use of the I() function to inhibit the interpretation/conversion of objects is not supported.

The group_contrasts argument is only used by ancova(). If NULL (default) a treatment effect is estimated for every non-reference group versus the reference group (the first factor level of group). Alternatively a bespoke set of contrasts can be requested. Pairwise contrasts are given as length-2 character vectors, e.g. group_contrasts = list(c("A", "Placebo"), c("B", "Placebo")) requests the contrasts A - Placebo and B - Placebo. More general linear contrasts are given as named numeric weight vectors over the group levels, e.g. group_contrasts = list(pooled_vs_pbo = c(Placebo = -1, A = 0.5, B = 0.5)) contrasts the average of A and B against Placebo. List names are carried through to the contrast_label column of the pool() output; weight-vector contrasts must be named. See ancova() for the resulting parameter naming scheme.

Currently strata is only used by draws() in combination with method_condmean(type = "bootstrap") and method_approxbayes() in order to allow for the specification of stratified bootstrap sampling. By default strata is set equal to the value of group as it is assumed most users will want to preserve the group size between samples. See draws() for more details.

Likewise, currently the strategy argument is only used by draws() to specify the name of the strategy variable within the data_ice data.frame. See draws() for more details.

See also

draws()

ancova()

Examples 

if (FALSE) { # \dontrun{

# Using CDISC variable names as an example
set_vars(
    subjid = "usubjid",
    visit = "avisit",
    outcome = "aval",
    group = "arm",
    covariates = c("bwt", "bht", "arm * avisit"),
    strategy = "strat"
)

} # }