wcls: Estimates the causal excursion effect for continuous outcome...

In MRTAnalysis: Assessing Proximal, Distal, and Mediated Causal Excursion Effects for Micro-Randomized Trials

Estimates the causal excursion effect for continuous outcome MRT

Description

Returns the estimated causal excursion effect (on additive scale) and the estimated standard error. Small sample correction using the "Hat" matrix in the variance estimate is implemented.

Usage

wcls(
  data,
  id,
  outcome,
  treatment,
  rand_prob,
  moderator_formula,
  control_formula,
  availability = NULL,
  numerator_prob = NULL,
  verbose = TRUE
)

Arguments

data	A data set in long format.
id	The subject id variable.
outcome	The outcome variable.
treatment	The binary treatment assignment variable.
rand_prob	The randomization probability variable.
moderator_formula	A formula for the moderator variables. This should start with ~ followed by the moderator variables. When set to ~ 1, a fully marginal excursion effect (no moderators) is estimated.
control_formula	A formula for the control variables. This should start with ~ followed by the control variables. When set to ~ 1, only an intercept is included as the control variable.
availability	The availability variable. Use the default value (NULL) if your MRT doesn't have availability considerations.
numerator_prob	Either a number between 0 and 1, or a variable name for a column in data. If you are not sure what this is, use the default value (NULL).
verbose	If default ('TRUE'), additional messages will be printed during data preprocessing.

Value

An object of type "wcls_fit"

Examples

wcls(
    data = data_mimicHeartSteps,
    id = "userid",
    outcome = "logstep_30min",
    treatment = "intervention",
    rand_prob = 0.6,
    moderator_formula = ~1,
    control_formula = ~logstep_pre30min,
    availability = "avail",
    numerator_prob = 0.6
)

MRTAnalysis documentation built on Sept. 9, 2025, 5:41 p.m.