This is the main, top-level wrapper for {outstandR}.
Methods taken from
RemiroAzocar2022outstandR.
Arguments
- AC.IPD
Individual-level patient data. Suppose between studies A and C.
- BC.ALD
Aggregate-level data. Suppose between studies B and C.
- strategy
Computation strategy function. These can be
strategy_maic(),strategy_stc(),strategy_gcomp_ml()andstrategy_gcomp_stan()- CI
Confidence interval; between 0,1
- scale
Relative treatment effect scale. If
NULL, the scale is automatically determined from the model.- ...
Additional arguments
Value
List of length 3 of statistics as a outstandR class object.
Containing statistics between each pair of treatments.
These are the mean, variances and confidence intervals,
for contrasts and absolute values.
Examples
data(AC_IPD) # AC patient-level data
data(BC_ALD) # BC aggregate-level data
lin_form <- as.formula("y ~ X3 + X4 + trt*X1 + trt*X2")
# matching-adjusted indirect comparison
outstandR_maic <- outstandR(AC_IPD, BC_ALD, strategy = strategy_maic(formula = lin_form))
# simulated treatment comparison
outstandR_stc <- outstandR(AC_IPD, BC_ALD, strategy = strategy_stc(lin_form))
# G-computation with maximum likelihood
# outstandR_gcomp_ml <- outstandR(AC_IPD, BC_ALD, strategy = strategy_gcomp_ml(lin_form))
# G-computation with Bayesian inference
outstandR_gcomp_stan <- outstandR(AC_IPD, BC_ALD, strategy = strategy_gcomp_stan(lin_form))
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.003759 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.59 seconds.
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#> Chain 1: Elapsed Time: 0.072 seconds (Warm-up)
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#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 9e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
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#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 9e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
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#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 9e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
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# Multiple imputation marginalization
outstandR_gcomp_stan <- outstandR(AC_IPD, BC_ALD, strategy = strategy_mim(lin_form))
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
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#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
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