trial_sequence.md
In TrialEmulation: Causal Analysis of Observational Time-to-Event Data

show works for trial_sequence_PP with nothing set

Code
  show(trial_sequence("PP"))
Output
  Trial Sequence Object 
  Estimand: Per-protocol

  Data: 
   - No data has been set. Use set_data()

  IPW for informative censoring: 
   - No weight model specified

  IPW for treatment switch censoring: 
   - No weight model specified

  Outcome model: 
   - Outcome model not specified. Use set_outcome_model()

show works for trial_sequence_ITT with nothing set

Code
  show(trial_sequence("ITT"))
Output
  Trial Sequence Object 
  Estimand: Intention-to-treat

  Data: 
   - No data has been set. Use set_data()

  IPW for informative censoring: 
   - No weight model specified

  Outcome model: 
   - Outcome model not specified. Use set_outcome_model()

show works for trial_sequence_AT with nothing set

Code
  show(trial_sequence("AT"))
Output
  Trial Sequence Object 
  Estimand: As treated

  Data: 
   - No data has been set. Use set_data()

  IPW for informative censoring: 
   - No weight model specified

  IPW for treatment switch censoring: 
   - No weight model specified

  Outcome model: 
   - Outcome model not specified. Use set_outcome_model()

show works for trial_sequence_PP with data and outcome_model set

Code
  show(result)
Output
  Trial Sequence Object 
  Estimand: Per-protocol

  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: 
   - No weight model specified

  Sequence of Trials Data: 
  - Use set_expansion_options() and expand_trials() to construct the sequence of trials dataset.

  Outcome model: 
  - Formula: outcome ~ assigned_treatment + followup_time + I(followup_time^2) + trial_period + I(trial_period^2) 
  - Treatment variable: assigned_treatment 
  - Adjustment variables:  
  - Model fitter type: te_stats_glm_logit

  Use fit_msm() to fit the outcome model