Set switching weight model — set_switch_weight

Usage

set_switch_weight_model(object, numerator, denominator, model_fitter, ...)

# S4 method for class 'trial_sequence'
set_switch_weight_model(
  object,
  numerator,
  denominator,
  model_fitter,
  eligible_wts_0 = NULL,
  eligible_wts_1 = NULL
)

# S4 method for class 'trial_sequence_ITT'
set_switch_weight_model(object, numerator, denominator, model_fitter)

Arguments

object: A trial_sequence object.
numerator: Right hand side formula for the numerator model
denominator: Right hand side formula for the denominator model
model_fitter: A te_model_fitter object, such as stats_glm_logit
...: Other arguments used by methods.
eligible_wts_0: Name of column containing indicator (0/1) for observation to be excluded/included in weight model.
eligible_wts_1: Exclude some observations when fitting the models for the inverse probability of treatment weights. For example, if it is assumed that an individual will stay on treatment for at least 2 visits, the first 2 visits after treatment initiation by definition have a probability of staying on the treatment of 1.0 and should thus be excluded from the weight models for those who are on treatment at the immediately previous visit. Users can define a variable that indicates that these 2 observations are ineligible for the weight model for those who are on treatment at the immediately previous visit and add the variable name in the argument eligible_wts_1. Similar definitions are applied to eligible_wts_0 for excluding observations when fitting the models for the inverse probability of treatment weights for those who are not on treatment at the immediately previous visit.

Value

object is returned with @switch_weights set

Examples

trial_sequence("PP") |>
  set_data(data = data_censored) |>
  set_switch_weight_model(
    numerator = ~ age_s + x1 + x3,
    denominator = ~ x3 + x4,
    model_fitter = stats_glm_logit(tempdir())
  )
#> Trial Sequence Object 
#> Estimand: Per-protocol 
#>  
#> Observational Data: 
#>  - N: 321 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
#>  ---                                                                           
#> 320:    99      1         1     0 -1.106480738     1 0.5752681    66 2.58333333
#> 321:    99      2         0     0  1.650478074     1 0.5752681    67 2.66666667
#>      outcome censored eligible time_on_regime
#>        <num>    <int>    <num>          <num>
#>   1:       0        0        1              0
#>   2:       0        0        0              1
#>  ---                                         
#> 320:       0        0        0              1
#> 321:       0        0        0              2
#>  
#> IPW for informative censoring: 
#>  - No weight model specified 
#>  
#> IPW for treatment switch censoring: 
#>  - Numerator formula: treatment ~ age_s + x1 + x3 
#>  - Denominator formula: treatment ~ x3 + x4 
#>  - 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()