Estimate the emulated trial effect

emul_estimate(
  clones_weighted,
  method = c("Cox", "logistic", "KM"),
  cluster = "id",
  weights = NULL,
  predictors = NULL,
  outcome = "outcome",
  time_start = "Tstart",
  time_stop = "Tstop",
  arm = "arms"
)

Arguments

clones_weighted: A named list of weighted clone data frames, or a single data frame containing an arm column.
method: Analysis method: "Cox", "logistic", or "KM".
cluster: Column name used for robust clustering in Cox models.
weights: Optional weight column name. Unquoted column names are also accepted when they exist in the data.
predictors: Optional adjustment predictors. For KM, predictors define additional strata rather than covariate adjustment.
outcome: Column name for the outcome indicator.
time_start: Column name for interval start time.
time_stop: Column name for interval stop time.
arm: Column name for treatment arm after binding clone lists.

Value

A fitted model object: "coxph" for Cox, "glm" for logistic, or "survfit" for KM.

Examples

data(lungcancer)
arms <- c("Control", "Surgery")
clones <- clone_arms(lungcancer, arms)
policies <- create_policy_A(
  arms, "surgery", "timetosurgery", 182.62, "death", "fup_obs",
  clone_outcome = "outcome", clone_followup = "fup"
)
clones_policy <- apply_logics(clones, policies)
censoring_logics <- create_censoring_logics_A(
  arms, "surgery", "timetosurgery", 182.62, "fup_obs",
  clone_censoring = "censoring",
  clone_uncensored_followup = "fup_uncensored"
)
clones_censored <- apply_logics(clones_policy, censoring_logics)
clones_final <- create_final_data(
  clones_censored, "fup", "outcome", "censoring", "id"
)
clones_estimated <- estimate_censoring(
  clones_final,
  predictors = c("age", "sex"),
  method = "pooled_logit"
)
clones_weighted <- weight_cases(clones_estimated)
fit <- emul_estimate(
  clones_weighted,
  method = "Cox",
  weights = "weight_Cox",
  predictors = c("age", "sex")
)