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In rbmi: Reference Based Multiple Imputation

print - Pool Method

Code
  print(.test_print$bayes$pool)
Output

  Pool Object
  -----------
  Number of Results Combined: 50
  Method: rubin
  Confidence Level: 0.95
  Alternative: two.sided

  Results:

    ========================================================
        parameter      est     se     lci     uci     pval  
    --------------------------------------------------------
       trt_visit_1    7.253   0.781  5.665   8.842   <0.001 
     lsm_ref_visit_1  7.254   0.566  6.102   8.406   <0.001 
     lsm_alt_visit_1  14.507  0.479  13.533  15.481  <0.001 
       trt_visit_3    7.984   0.258  7.448    8.52   <0.001 
     lsm_ref_visit_3  7.005   0.205  6.575   7.436   <0.001 
     lsm_alt_visit_3  14.989  0.167  14.641  15.338  <0.001 
    --------------------------------------------------------

Code
  print(.test_print$approxbayes$pool)
Output

  Pool Object
  -----------
  Number of Results Combined: 5
  Method: rubin
  Confidence Level: 0.9
  Alternative: less

  Results:

    ===================================================
        parameter      est     se     lci    uci  pval 
    ---------------------------------------------------
       trt_visit_1    7.253   0.781  6.232   Inf   1   
     lsm_ref_visit_1  7.254   0.566  6.513   Inf   1   
     lsm_alt_visit_1  14.507  0.479  13.881  Inf   1   
       trt_visit_2    7.406   0.388  6.898   Inf   1   
     lsm_ref_visit_2  7.011   0.282  6.643   Inf   1   
     lsm_alt_visit_2  14.417  0.238  14.106  Inf   1   
       trt_visit_3    5.359   1.092  3.929   Inf   1   
     lsm_ref_visit_3  6.723   0.835  5.624   Inf   1   
     lsm_alt_visit_3  12.082  0.658  11.222  Inf   1   
    ---------------------------------------------------

Code
  print(.test_print$condmean_boot$pool$percentile)
Output

  Pool Object
  -----------
  Number of Results Combined: 1 + 5
  Method: bootstrap (percentile)
  Confidence Level: 0.95
  Alternative: greater

  Results:

    =====================================================
        parameter      est     se   lci    uci     pval  
    -----------------------------------------------------
       trt_visit_1    6.643   <NA>  -Inf  7.383   <0.001 
     lsm_ref_visit_1  7.605   <NA>  -Inf  8.126   <0.001 
     lsm_alt_visit_1  14.248  <NA>  -Inf  15.088  <0.001 
       trt_visit_2    6.906   <NA>  -Inf  7.944   <0.001 
     lsm_ref_visit_2  7.299   <NA>  -Inf  7.666   <0.001 
     lsm_alt_visit_2  14.205  <NA>  -Inf  14.977  <0.001 
       trt_visit_3    4.118   <NA>  -Inf  4.257   <0.001 
     lsm_ref_visit_3  7.514   <NA>  -Inf  8.083   <0.001 
     lsm_alt_visit_3  11.632  <NA>  -Inf  11.837  <0.001 
    -----------------------------------------------------

Code
  print(.test_print$condmean_boot$pool$normal)
Output

  Pool Object
  -----------
  Number of Results Combined: 1 + 5
  Method: bootstrap (normal)
  Confidence Level: 0.95
  Alternative: greater

  Results:

    ======================================================
        parameter      est     se    lci    uci     pval  
    ------------------------------------------------------
       trt_visit_1    6.643   0.561  -Inf  7.565   <0.001 
     lsm_ref_visit_1  7.605   1.057  -Inf  9.343   <0.001 
     lsm_alt_visit_1  14.248  1.163  -Inf  16.161  <0.001 
       trt_visit_2    6.906   0.852  -Inf  8.308   <0.001 
     lsm_ref_visit_2  7.299   1.114  -Inf   9.13   <0.001 
     lsm_alt_visit_2  14.205  0.984  -Inf  15.823  <0.001 
       trt_visit_3    4.118   0.663  -Inf  5.208   <0.001 
     lsm_ref_visit_3  7.514   1.003  -Inf  9.165   <0.001 
     lsm_alt_visit_3  11.632  1.339  -Inf  13.834  <0.001 
    ------------------------------------------------------

Code
  print(.test_print$condmean_jack$pool)
Output

  Pool Object
  -----------
  Number of Results Combined: 1 + 35
  Method: jackknife
  Confidence Level: 0.9
  Alternative: two.sided

  Results:

    ========================================================
        parameter      est     se     lci     uci     pval  
    --------------------------------------------------------
       trt_visit_1    7.296   0.784  6.006   8.587   <0.001 
     lsm_ref_visit_1  7.051   0.766  5.792   8.311   <0.001 
     lsm_alt_visit_1  14.348  0.74   13.131  15.564  <0.001 
       trt_visit_2    7.363   0.373  6.749   7.977   <0.001 
     lsm_ref_visit_2  7.085   0.555  6.173   7.997   <0.001 
     lsm_alt_visit_2  14.448  0.599  13.463  15.433  <0.001 
       trt_visit_3    4.593   1.063  2.844   6.342   <0.001 
     lsm_ref_visit_3  6.469   0.815  5.129   7.809   <0.001 
     lsm_alt_visit_3  11.062  0.929  9.534   12.59   <0.001 
    --------------------------------------------------------

Code
  print(.test_print$bmlmi$pool)
Output

  Pool Object
  -----------
  Number of Results Combined: 24
  Method: bmlmi
  Confidence Level: 0.9
  Alternative: two.sided

  Results:

    ========================================================
        parameter      est     se     lci     uci     pval  
    --------------------------------------------------------
       trt_visit_1    7.039    0.5   6.032   8.047   <0.001 
     lsm_ref_visit_1  6.993   1.38   4.212   9.773   0.004  
     lsm_alt_visit_1  14.032  1.178  11.658  16.406  <0.001 
       trt_visit_2    7.494   0.403  6.681   8.306   <0.001 
     lsm_ref_visit_2  6.694   1.278  4.119    9.27   0.003  
     lsm_alt_visit_2  14.188  1.013  12.146  16.23   <0.001 
       trt_visit_3    4.737   1.142   2.43   7.044   0.009  
     lsm_ref_visit_3   6.53   1.097  4.318   8.742   0.002  
     lsm_alt_visit_3  11.267  1.753  7.734    14.8   0.001  
    --------------------------------------------------------

print - approx bayes

Code
  print(drawobj_ab)
Output

  Draws Object
  ------------
  Number of Samples: 3
  Number of Failed Samples: 0
  Model Formula: outcome ~ 1 + group + visit + age + sex + visit * group
  Imputation Type: random
  Method:
      name: Approximate Bayes
      covariance: ar1
      threshold: 0.5
      same_cov: TRUE
      REML: TRUE
      n_samples: 3

Code
  print(impute_ab)
Output

  Imputation Object
  -----------------
  Number of Imputed Datasets: 3
  Fraction of Missing Data (Original Dataset):
      visit_1:   0%
      visit_2:   0%
      visit_3:  42%
  References:
      TRT     -> Placebo
      Placebo -> Placebo

Code
  print(analysis_ab)
Output

  Analysis Object
  ---------------
  Number of Results: 3
  Analysis Function: ancova
  Delta Applied: FALSE
  Analysis Estimates:
      trt_visit_1
      lsm_ref_visit_1
      lsm_alt_visit_1
      trt_visit_2
      lsm_ref_visit_2
      lsm_alt_visit_2
      trt_visit_3
      lsm_ref_visit_3
      lsm_alt_visit_3

print - bayesian

Code
  print(drawobj_b)
Output

  Draws Object
  ------------
  Number of Samples: 50
  Number of Failed Samples: 0
  Model Formula: outcome ~ 1 + group + visit + age + sex + visit * group
  Imputation Type: random
  Method:
      name: Bayes
      burn_in: 200
      burn_between: 1
      same_cov: TRUE
      n_samples: 50
      seed: 859

Code
  print(impute_b)
Output

  Imputation Object
  -----------------
  Number of Imputed Datasets: 50
  Fraction of Missing Data (Original Dataset):
      visit_1:   0%
      visit_2:   0%
      visit_3:  42%
  References:
      TRT     -> TRT
      Placebo -> Placebo

Code
  print(analysis_b)
Output

  Analysis Object
  ---------------
  Number of Results: 50
  Analysis Function: rbmi::ancova
  Delta Applied: TRUE
  Analysis Estimates:
      trt_visit_1
      lsm_ref_visit_1
      lsm_alt_visit_1
      trt_visit_3
      lsm_ref_visit_3
      lsm_alt_visit_3

print - condmean bootstrap

Code
  print(drawobj_cmb)
Output

  Draws Object
  ------------
  Number of Samples: 1 + 0
  Number of Failed Samples: 0
  Model Formula: outcome ~ 1 + group + visit + age + sex + visit * group
  Imputation Type: condmean
  Method:
      name: Conditional Mean
      covariance: ar1
      threshold: 0.2
      same_cov: TRUE
      REML: TRUE
      type: bootstrap
      n_samples: 0

Code
  print(impute_cmb)
Output

  Imputation Object
  -----------------
  Number of Imputed Datasets: 1 + 0
  Fraction of Missing Data (Original Dataset):
      visit_1:   0%
      visit_2:   0%
      visit_3:  42%
  References:
      TRT     -> TRT
      Placebo -> Placebo

Code
  print(analysis_cmb)
Output

  Analysis Object
  ---------------
  Number of Results: 1 + 0
  Analysis Function: ancova
  Delta Applied: FALSE
  Analysis Estimates:
      trt_visit_1
      lsm_ref_visit_1
      lsm_alt_visit_1
      trt_visit_2
      lsm_ref_visit_2
      lsm_alt_visit_2
      trt_visit_3
      lsm_ref_visit_3
      lsm_alt_visit_3

print - condmean jackknife

Code
  print(drawobj_cmj)
Output

  Draws Object
  ------------
  Number of Samples: 1 + 35
  Number of Failed Samples: 0
  Model Formula: outcome ~ 1 + group + visit + age + sex + visit * group
  Imputation Type: condmean
  Method:
      name: Conditional Mean
      covariance: us
      threshold: 0.5
      same_cov: FALSE
      REML: TRUE
      type: jackknife

Code
  print(impute_cmj)
Output

  Imputation Object
  -----------------
  Number of Imputed Datasets: 1 + 35
  Fraction of Missing Data (Original Dataset):
      visit_1:   0%
      visit_2:   0%
      visit_3:  46%
  References:
      TRT     -> Placebo
      Placebo -> Placebo

Code
  print(analysis_cmj)
Output

  Analysis Object
  ---------------
  Number of Results: 1 + 35
  Analysis Function: ancova
  Delta Applied: FALSE
  Analysis Estimates:
      trt_visit_1
      lsm_ref_visit_1
      lsm_alt_visit_1
      trt_visit_2
      lsm_ref_visit_2
      lsm_alt_visit_2
      trt_visit_3
      lsm_ref_visit_3
      lsm_alt_visit_3

print - bmlmi

Code
  print(drawobj_bml)
Output

  Draws Object
  ------------
  Number of Samples: 6
  Number of Failed Samples: 0
  Model Formula: outcome ~ 1 + group + visit + age + sex + visit * group
  Imputation Type: random
  Method:
      covariance: cs
      threshold: 0.05
      same_cov: TRUE
      REML: TRUE
      B: 6
      D: 4

Code
  print(impute_bml)
Output

  Imputation Object
  -----------------
  Number of Imputed Datasets: 24
  Fraction of Missing Data (Original Dataset):
      visit_1:   0%
      visit_2:   0%
      visit_3:  42%
  References:
      TRT     -> Placebo
      Placebo -> Placebo

Code
  print(analysis_bml)
Output

  Analysis Object
  ---------------
  Number of Results: 24
  Analysis Function: compare_prop_lastvisit
  Delta Applied: FALSE
  Analysis Estimates:
      trt