library("causatr")
target_trial(
eligibility = "Adults aged 25-74 who smoke >= 5 cig/day",
treatment_strategy = "Quit smoking vs. continue smoking",
follow_up = "Baseline (1971) to end of follow-up (1982)",
outcome = "Weight change in kg at end of follow-up",
causal_contrast = "ATE: E[Y(quit)] - E[Y(continue)]"
)Specify a target trial protocol
Description
Creates a structured description of the target trial that your causal analysis emulates. This is a documentation aid — the object is not passed to causat() or contrast(). It helps you reason through the components of the target trial (Hernán & Robins 2025, Ch. 22) before writing the analysis code.
Usage
target_trial(
eligibility = NULL,
treatment_strategy = NULL,
assignment = NULL,
follow_up = NULL,
outcome = NULL,
causal_contrast = NULL,
model = NULL,
censoring = NULL
)
Arguments
eligibility
|
Character. Who is eligible for the trial at time zero? |
treatment_strategy
|
Character. What treatment strategies are compared? (e.g. "initiate treatment A vs. remain untreated") |
assignment
|
Character. How are individuals assigned to strategies? (e.g. "random assignment at baseline") |
follow_up
|
Character. When does follow-up start and end? |
outcome
|
Character. What outcome is measured, and when? |
causal_contrast
|
Character. What causal contrast is estimated? (e.g. "intention-to-treat effect", "per-protocol effect") |
model
|
Character. What statistical model links the data to the causal parameter? (e.g. "parametric g-formula with logistic outcome model") |
censoring
|
Character. How is loss to follow-up or non-adherence handled? (e.g. "IPCW for dropout", "grace period of 2 visits") |
Value
A causatr_target_trial object (S3 list with a print method).
See Also
causat(), contrast()