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Set censoring weight model

Source: R/trial_sequence.R
set_censor_weight_model.Rd

[Experimental]

Usage

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

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

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

# S4 method for class '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().

Value

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())
  )
#> Trial Sequence Object 
#> Estimand: Intention-to-treat 
#>  
#> Observational Data: 
#>  - N: 725 observations from 89 patients 
#>         id period treatment    x1           x2    x3        x4   age      age_s
#>      <int>  <int>     <num> <num>        <num> <int>     <num> <num>      <num>
#>   1:     1      0         1     1  1.146148362     0 0.7342030    36 0.08333333
#>   2:     1      1         1     1  0.002200337     0 0.7342030    37 0.16666667
#>  ---                                                                           
#> 724:    99      6         1     1 -0.033762356     1 0.5752681    71 3.00000000
#> 725:    99      7         0     0 -1.340496520     1 0.5752681    72 3.08333333
#>      outcome censored eligible time_on_regime
#>        <num>    <int>    <num>          <num>
#>   1:       0        0        1              0
#>   2:       0        0        0              1
#>  ---                                         
#> 724:       0        0        0              1
#> 725:       1        0        0              2
#>  
#> IPW for informative censoring: 
#>  - Numerator formula: 1 - censored ~ age_s + x1 + x3 
#>  - Denominator formula: 1 - censored ~ x3 + x4 
#>  - Numerator and denominotor models are pooled across treatment arms. 
#>  - Model fitter type: te_stats_glm_logit 
#>  - Weight models not fitted. Use calculate_weights() 
#>  
#> Sequence of Trials Data: 
#> - Use set_expansion_options() and expand_trials() to construct the sequence of trials dataset. 
#>  
#> Outcome model: 
#>  - Outcome model not specified. Use set_outcome_model()