tasmd_rand: Target Absolute Standardized Mean Differences (TASMD)
In FSM: Finite Selection Model

Description Usage Arguments Value Author(s) References Examples

View source: R/design_assessment.R

Computes the mean and standard deviation of Target Absolute Standardized Mean Differences (TASMD) of multiple covariates (or transformations thereof) in a treatment group relative to a target population or a target individual for a set of assignments under one or two designs.

tasmd_rand(
  data_frame,
  index_col = FALSE,
  alloc1,
  alloc2,
  treat_lab = 1,
  legend = c("CRD", "FSM"),
  mean_tar = NULL,
  sd_tar = NULL,
  denom = "target",
  roundoff = 3
)

`data_frame`	Data frame containing a column of unit indices (optional) and covariates (or transformations thereof).
`index_col`	if `TRUE`, `data_frame` contains a column of unit indices.
`alloc1`	A matrix or vector of treatment assignments. If `alloc1` is a matrix, then each row should correspond to an assignment vector.
`alloc2`	A (optional) matrix or vector of treatment assignment. If `alloc2` is a matrix, then each row should correspond to an assignment vector.
`treat_lab`	Label of the treatment group in which the TASMD is computed.
`legend`	Legend of the two designs under consideration.
`mean_tar`	A (optional) vector of target profile of the covariates under consideration, e.g., mean of the covariates in the target population. Applicable only when `imbalance = 'TASMD'`. If `mean_tar = NULL`, the full-sample average of the covariates is considered as the target profile.
`sd_tar`	A optional vector of the standard deviation of the covariates in the target population. Applicable only when `imbalance = 'TASMD'`.
`denom`	Specifies the denominator for the computation of TASMD. If `denom = 'target'`, the standard deviations of the covariates in the target population are used. If `denom = 'group'`, the standard deviations of the covariates in the treatment group given by `treat_lab` are used. Applicable only when `imbalance = 'TASMD'`.
`roundoff`	A number indicating the number of decimal places to be used for rounding off the TASMDs.

A list containing the following items (if alloc1 and alloc2 are matrices)

tasmd_table: A matrix containing the means (standard deviations in parenthesis) of the TASMDs for the designs under consideration. If alloc1 or alloc2 is a vector, the TASMD of the corresponding assignment is returned.

tasmd_mean: A matrix containing the means of the TASMDs for the designs under consideration.

tasmd_sd: A matrix containing the standard deviations of the TASMDs for the designs under consideration.

If alloc1 and alloc2 are vectors, tasmd_rand produces a data frame of the corresponding TASMDs.

Ambarish Chattopadhyay, Carl N. Morris and Jose R. Zubizarreta.

Chattopadhyay, A., Morris, C. N., and Zubizarreta, J. R. (2020), “Randomized and Balanced Allocation of Units into Treatment Groups Using the Finite Selection Model for R".

# Consider the Lalonde dataset.
# Get the full sample size.
N = nrow(Lalonde)
# Get the treatment group sizes.
n1 = floor(N/2)
n2 = N-n1
# Generate an SOM.
som_obs = som(n_treat = 2, treat_sizes = c(n1,n2),include_discard = FALSE,
method = 'SCOMARS', marginal_treat = rep((n2/N),N), control = FALSE)
# Generate a treatment assignment given som_obs.
f = fsm(data_frame = Lalonde, SOM = som_obs, s_function = 'Dopt', eps = 0.0001, 
ties = 'random', intercept = TRUE, standardize = TRUE, units_print = FALSE)
# Get assignment vector under the FSM.
Z_fsm_obs = f$data_frame_allocated$Treat
# Draw a random CRD.
Z_crd_obs = crd(data_frame = Lalonde, n_treat = 2, treat_sizes = c(n1, n2), 
control = FALSE)$Treat
# Calculate the TASMD.
TASMD = tasmd_rand(data_frame = Lalonde, index_col = TRUE, alloc1 = Z_crd_obs, 
alloc2 = Z_fsm_obs, treat_lab = 1, mean_tar = NULL, sd_tar = NULL, 
denom = 'target', legend = c('CRD','FSM'), roundoff = 3)