set_censor_weight_model function

Set censoring weight model

Description

set_censor_weight_model(
  object,
  censor_event,
  numerator,
  denominator,
  pool_models = NULL,
  model_fitter
)

## S4 method for signature 'trial_sequence'
set_censor_weight_model(
  object,
  censor_event,
  numerator,
  denominator,
  pool_models = c("none", "both", "numerator"),
  model_fitter = stats_glm_logit()
)

## S4 method for signature 'trial_sequence_PP'
set_censor_weight_model(
  object,
  censor_event,
  numerator,
  denominator,
  pool_models = "none",
  model_fitter = stats_glm_logit()
)

## S4 method for signature 'trial_sequence_ITT'
set_censor_weight_model(
  object,
  censor_event,
  numerator,
  denominator,
  pool_models = "numerator",
  model_fitter = stats_glm_logit()
)

## S4 method for signature 'trial_sequence_AT'
set_censor_weight_model(
  object,
  censor_event,
  numerator,
  denominator,
  pool_models = "none",
  model_fitter = stats_glm_logit()
)

Arguments

• object: trial_sequence.
• censor_event: string. Name of column containing censoring indicator.
• numerator: A RHS formula to specify the logistic models for estimating the numerator terms of the inverse probability of censoring weights.
• denominator: A RHS formula to specify the logistic models for estimating the denominator terms of the inverse probability of censoring weights.
• pool_models: Fit pooled or separate censoring models for those treated and those untreated at the immediately previous visit. Pooling can be specified for the models for the numerator and denominator terms of the inverse probability of censoring weights. One of "none", "numerator", or "both" (default is "none" except when estimand = "ITT" then default is "numerator").
• model_fitter: An object of class te_model_fitter which determines the method used for fitting the weight models. For logistic regression use stats_glm_logit().

Returns

object is returned with @censor_weights set

Examples

trial_sequence("ITT") |>
  set_data(data = data_censored) |>
  set_censor_weight_model(
    censor_event = "censored",
    numerator = ~ age_s + x1 + x3,
    denominator = ~ x3 + x4,
    pool_models = "both",
    model_fitter = stats_glm_logit(save_path = tempdir())
  )