maic_unanchored.md
In maicplus: Matching Adjusted Indirect Comparison

test binary case

Code
  testout_RR$descriptive$summary
Output
    trt_ind treatment            type        n   events events_pct
  1       B         B Before matching 400.0000 280.0000   70.00000
  2       A         A Before matching 500.0000 390.0000   78.00000
  3       B         B  After matching 400.0000 280.0000   70.00000
  4       A         A  After matching 199.4265 142.8968   71.65386

Code
  testout_RR$inferential$summary
Output
           case       RR       LCL      UCL        pval
  1          AB 1.114286 1.0293724 1.206204 0.007455267
  2 adjusted_AB 1.023627 0.9123647 1.148457 0.690809607

Code
  testout_RR$inferential$fit
Output
  $model_before

  Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
      data = dat)

  Coefficients:
  (Intercept)         ARMA  
      -0.3567       0.1082

  Degrees of Freedom: 899 Total (i.e. Null);  898 Residual
  Null Deviance:        1023 
  Residual Deviance: 1016   AIC: 1020

  $model_after

  Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
      data = dat, weights = weights)

  Coefficients:
  (Intercept)         ARMA  
     -0.35667      0.02335

  Degrees of Freedom: 899 Total (i.e. Null);  898 Residual
  Null Deviance:        726.7 
  Residual Deviance: 726.5  AIC: 712.5

  $res_AB
  $res_AB$est
  [1] 1.023627

  $res_AB$se
  [1] 0.06025155

  $res_AB$ci_l
  [1] 0.9123647

  $res_AB$ci_u
  [1] 1.148457

  $res_AB$pval
  [1] 0.6908096


  $res_AB_unadj
  $res_AB_unadj$est
  [1] 1.114286

  $res_AB_unadj$se
  [1] 0.04511891

  $res_AB_unadj$ci_l
  [1] 1.029372

  $res_AB_unadj$ci_u
  [1] 1.206204

  $res_AB_unadj$pval
  [1] 0.007455267


  $boot_res
  NULL

  $boot_res_AB
  NULL

Code
  testout_RD$descriptive$summary
Output
    trt_ind treatment            type        n   events events_pct
  1       B         B Before matching 400.0000 280.0000   70.00000
  2       A         A Before matching 500.0000 390.0000   78.00000
  3       B         B  After matching 400.0000 280.0000   70.00000
  4       A         A  After matching 199.4265 142.8968   71.65386

Code
  testout_RD$inferential$summary
Output
           case       RD       LCL       UCL        pval
  1          AB 8.000000  2.224920 13.775080 0.006626293
  2 adjusted_AB 1.653865 -6.532173  9.839902 0.692119096

Code
  testout_RD$inferential$fit
Output
  $model_before

  Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
      data = dat)

  Coefficients:
  (Intercept)         ARMA  
         0.70         0.08

  Degrees of Freedom: 899 Total (i.e. Null);  898 Residual
  Null Deviance:        1023 
  Residual Deviance: 1016   AIC: 1020

  $model_after

  Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
      data = dat, weights = weights)

  Coefficients:
  (Intercept)         ARMA  
      0.70000      0.01654

  Degrees of Freedom: 899 Total (i.e. Null);  898 Residual
  Null Deviance:        726.7 
  Residual Deviance: 726.5  AIC: 712.5

  $res_AB
  $res_AB$est
  [1] 1.653865

  $res_AB$se
  [1] 4.176627

  $res_AB$ci_l
  [1] -6.532173

  $res_AB$ci_u
  [1] 9.839902

  $res_AB$pval
  [1] 0.6921191


  $res_AB_unadj
  $res_AB_unadj$est
  [1] 8

  $res_AB_unadj$se
  [1] 2.946523

  $res_AB_unadj$ci_l
  [1] 2.22492

  $res_AB_unadj$ci_u
  [1] 13.77508

  $res_AB_unadj$pval
  [1] 0.006626293


  $boot_res
  NULL

  $boot_res_AB
  NULL

Code
  testout_OR$descriptive$summary
Output
    trt_ind treatment            type        n   events events_pct
  1       B         B Before matching 400.0000 280.0000   70.00000
  2       A         A Before matching 500.0000 390.0000   78.00000
  3       B         B  After matching 400.0000 280.0000   70.00000
  4       A         A  After matching 199.4265 142.8968   71.65386

Code
  testout_OR$inferential$summary
Output
           case       OR       LCL      UCL        pval
  1          AB 1.519481 1.1247154 2.052805 0.006417064
  2 adjusted_AB 1.083350 0.7268601 1.614683 0.694183560

Code
  testout_OR$inferential$fit
Output
  $model_before

  Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
      data = dat)

  Coefficients:
  (Intercept)         ARMA  
       0.8473       0.4184

  Degrees of Freedom: 899 Total (i.e. Null);  898 Residual
  Null Deviance:        1023 
  Residual Deviance: 1016   AIC: 1020

  $model_after

  Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
      data = dat, weights = weights)

  Coefficients:
  (Intercept)         ARMA  
      0.84730      0.08006

  Degrees of Freedom: 899 Total (i.e. Null);  898 Residual
  Null Deviance:        726.7 
  Residual Deviance: 726.5  AIC: 712.5

  $res_AB
  $res_AB$est
  [1] 1.08335

  $res_AB$se
  [1] 0.2275624

  $res_AB$ci_l
  [1] 0.7268601

  $res_AB$ci_u
  [1] 1.614683

  $res_AB$pval
  [1] 0.6941836


  $res_AB_unadj
  $res_AB_unadj$est
  [1] 1.519481

  $res_AB_unadj$se
  [1] 0.2373883

  $res_AB_unadj$ci_l
  [1] 1.124715

  $res_AB_unadj$ci_u
  [1] 2.052805

  $res_AB_unadj$pval
  [1] 0.006417064


  $boot_res
  NULL

  $boot_res_AB
  NULL

Code
  print(testout_boot_RR$descriptive$summary, digits = 5)
Output
    trt_ind treatment            type      n events events_pct
  1       B         B Before matching 400.00  280.0     70.000
  2       A         A Before matching 500.00  390.0     78.000
  3       B         B  After matching 400.00  280.0     70.000
  4       A         A  After matching 199.43  142.9     71.654

Code
  print(testout_boot_RR$inferential$summary, digits = 5)
Output
           case     RR     LCL    UCL      pval
  1          AB 1.1143 1.02937 1.2062 0.0074553
  2 adjusted_AB 1.0236 0.91236 1.1485 0.6908096

Code
  print(testout_boot_RR$inferential$fit, digits = 5)
Output
  $model_before

  Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
      data = dat)

  Coefficients:
  (Intercept)         ARMA  
     -0.35667      0.10821

  Degrees of Freedom: 899 Total (i.e. Null);  898 Residual
  Null Deviance:        1023 
  Residual Deviance: 1015.6     AIC: 1019.6

  $model_after

  Call:  glm(formula = RESPONSE ~ ARM, family = binomial(link = glm_link), 
      data = dat, weights = weights)

  Coefficients:
  (Intercept)         ARMA  
    -0.356675     0.023352

  Degrees of Freedom: 899 Total (i.e. Null);  898 Residual
  Null Deviance:        726.66 
  Residual Deviance: 726.48     AIC: 712.47

  $res_AB
  $res_AB$est
  [1] 1.0236

  $res_AB$se
  [1] 0.060252

  $res_AB$ci_l
  [1] 0.91236

  $res_AB$ci_u
  [1] 1.1485

  $res_AB$pval
  [1] 0.69081


  $res_AB_unadj
  $res_AB_unadj$est
  [1] 1.1143

  $res_AB_unadj$se
  [1] 0.045119

  $res_AB_unadj$ci_l
  [1] 1.0294

  $res_AB_unadj$ci_u
  [1] 1.2062

  $res_AB_unadj$pval
  [1] 0.0074553


  $boot_res

  STRATIFIED BOOTSTRAP


  Call:
  boot(data = boot_ipd, statistic = stat_fun, R = R, strata = weights_object$boot_strata, 
      w_obj = weights_object, pseudo_ipd = pseudo_ipd, normalize = normalize_weights)


  Bootstrap Statistics :
       original      bias    std. error
  t1* 0.0233518  0.01366708  0.05380528
  t2* 0.0030551 -0.00004315  0.00038235

  $boot_res_AB
  $boot_res_AB$est
  [1] 1.0236

  $boot_res_AB$se
  [1] NA

  $boot_res_AB$ci_l
  [1] 0.90867

  $boot_res_AB$ci_u
  [1] 1.122

  $boot_res_AB$pval
  [1] NA

test time to event case

Code
  testout$descriptive$summary
Output
    trt_ind treatment            type records    n.max  n.start    events
  1       B         B Before matching     300 300.0000 300.0000 178.00000
  2       A         A Before matching     500 500.0000 500.0000 190.00000
  3       B         B  After matching     300 300.0000 300.0000 178.00000
  4       A         A  After matching     500 199.4265 199.4265  65.68878
        rmean se(rmean)    median  0.95LCL   0.95UCL
  1  4.303551 0.3367260  2.746131 2.261125  3.320857
  2  8.709690 0.3551477  7.587627 6.278691 10.288538
  3  4.303551 0.3367260  2.746131 2.261125  3.320857
  4 10.166029 0.5499915 11.900015 7.815275 14.873786

Code
  testout$inferential$summary
Output
           case        HR       LCL       UCL         pval
  1          AB 0.3748981 0.3039010 0.4624815 5.245204e-20
  2 adjusted_AB 0.2834780 0.2074664 0.3873387 2.473442e-15

Code
  testout$inferential$fit
Output
  $km_before
  Call: survfit(formula = Surv(TIME, EVENT) ~ ARM, data = dat, conf.type = km_conf_type)

          n events median 0.95LCL 0.95UCL
  ARM=B 300    178   83.6    68.8     101
  ARM=A 500    190  230.9   191.1     313

  $km_after
  Call: survfit(formula = Surv(TIME, EVENT) ~ ARM, data = dat, weights = dat$weights, 
      conf.type = km_conf_type)

        records   n events median 0.95LCL 0.95UCL
  ARM=B     300 300  178.0   83.6    68.8     101
  ARM=A     500 199   65.7  362.2   237.9     453

  $model_before
  Call:
  coxph(formula = Surv(TIME, EVENT) ~ ARM, data = dat)

          coef exp(coef) se(coef)      z      p
  ARMA -0.9811    0.3749   0.1071 -9.159 <2e-16

  Likelihood ratio test=80.62  on 1 df, p=< 2.2e-16
  n= 800, number of events= 368

  $model_after
  Call:
  coxph(formula = Surv(TIME, EVENT) ~ ARM, data = dat, weights = weights, 
      robust = TRUE)

          coef exp(coef) se(coef) robust se      z        p
  ARMA -1.2606    0.2835   0.1504    0.1593 -7.915 2.47e-15

  Likelihood ratio test=80.4  on 1 df, p=< 2.2e-16
  n= 800, number of events= 368

  $res_AB
  $res_AB$est
  [1] 0.283478

  $res_AB$se
  [1] 0.04601759

  $res_AB$ci_l
  [1] 0.2074664

  $res_AB$ci_u
  [1] 0.3873387

  $res_AB$pval
  [1] 2.473442e-15


  $res_AB_unadj
  $res_AB_unadj$est
  [1] 0.3748981

  $res_AB_unadj$se
  [1] 0.0405065

  $res_AB_unadj$ci_l
  [1] 0.303901

  $res_AB_unadj$ci_u
  [1] 0.4624815

  $res_AB_unadj$pval
  [1] 5.245204e-20


  $boot_res
  NULL

  $boot_res_AB
  NULL

Code
  print(testout2$descriptive$summary, digits = 5)
Output
    trt_ind treatment            type records  n.max n.start  events   rmean
  1       B         B Before matching     300 300.00  300.00 178.000  4.3036
  2       A         A Before matching     500 500.00  500.00 190.000  8.7097
  3       B         B  After matching     300 300.00  300.00 178.000  4.3036
  4       A         A  After matching     500 199.43  199.43  65.689 10.1660
    se(rmean)  median 0.95LCL 0.95UCL
  1   0.33673  2.7461  2.2611  3.3209
  2   0.35515  7.5876  6.2787 10.2885
  3   0.33673  2.7461  2.2611  3.3209
  4   0.54999 11.9000  7.8153 14.8738

Code
  print(testout2$inferential$summary, digits = 5)
Output
           case      HR     LCL     UCL       pval
  1          AB 0.37490 0.30390 0.46248 5.2452e-20
  2 adjusted_AB 0.28348 0.20747 0.38734 2.4734e-15

Code
  print(testout2$inferential$fit, digits = 5)
Output
  $km_before
  Call: survfit(formula = Surv(TIME, EVENT) ~ ARM, data = dat, conf.type = km_conf_type)

          n events  median 0.95LCL 0.95UCL
  ARM=B 300    178  83.585  68.823  101.08
  ARM=A 500    190 230.948 191.108  313.16

  $km_after
  Call: survfit(formula = Surv(TIME, EVENT) ~ ARM, data = dat, weights = dat$weights, 
      conf.type = km_conf_type)

        records      n  events  median 0.95LCL 0.95UCL
  ARM=B     300 300.00 178.000  83.585  68.823  101.08
  ARM=A     500 199.43  65.689 362.207 237.877  452.72

  $model_before
  Call:
  coxph(formula = Surv(TIME, EVENT) ~ ARM, data = dat)

           coef exp(coef) se(coef)       z         p
  ARMA -0.98110   0.37490  0.10712 -9.1589 < 2.2e-16

  Likelihood ratio test=80.62  on 1 df, p=< 2.22e-16
  n= 800, number of events= 368

  $model_after
  Call:
  coxph(formula = Surv(TIME, EVENT) ~ ARM, data = dat, weights = weights, 
      robust = TRUE)

           coef exp(coef) se(coef) robust se      z         p
  ARMA -1.26062   0.28348  0.15035   0.15927 -7.915 2.473e-15

  Likelihood ratio test=80.4  on 1 df, p=< 2.22e-16
  n= 800, number of events= 368

  $res_AB
  $res_AB$est
  [1] 0.28348

  $res_AB$se
  [1] 0.046018

  $res_AB$ci_l
  [1] 0.20747

  $res_AB$ci_u
  [1] 0.38734

  $res_AB$pval
  [1] 2.4734e-15


  $res_AB_unadj
  $res_AB_unadj$est
  [1] 0.3749

  $res_AB_unadj$se
  [1] 0.040506

  $res_AB_unadj$ci_l
  [1] 0.3039

  $res_AB_unadj$ci_u
  [1] 0.46248

  $res_AB_unadj$pval
  [1] 5.2452e-20


  $boot_res

  STRATIFIED BOOTSTRAP


  Call:
  boot(data = boot_ipd, statistic = stat_fun, R = R, strata = weights_object$boot_strata, 
      w_obj = weights_object, pseudo_ipd = pseudo_ipd, normalize = normalize_weights)


  Bootstrap Statistics :
       original     bias    std. error
  t1* -1.260621 0.00245135   0.1313882
  t2*  0.025367 0.00058194   0.0026704

  $boot_res_AB
  $boot_res_AB$est
  [1] 0.28348

  $boot_res_AB$se
  [1] NA

  $boot_res_AB$ci_l
  [1] 0.21858

  $boot_res_AB$ci_u
  [1] 0.36584

  $boot_res_AB$pval
  [1] NA