This is the main, top-level wrapper for {outstandR}.
Methods taken from
(Remiro‐Azócar et al. 2022)
.
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
- ...
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 contrasts, variances and confidence intervals,
respectively.
References
Remiro‐Azócar A, Heath A, Baio G (2022). “Parametric G‐computation for compatible indirect treatment comparisons with limited individual patient data.” Res. Synth. Methods, 1–31. ISSN 1759-2879, doi:10.1002/jrsm.1565 , 2108.12208.
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 'bernoulli' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 3.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.39 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1: Iteration: 1 / 4000 [ 0%] (Warmup)
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.494 seconds (Warm-up)
#> Chain 1: 0.516 seconds (Sampling)
#> Chain 1: 1.01 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1.9e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
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#> Chain 2:
#> Chain 2: Elapsed Time: 0.483 seconds (Warm-up)
#> Chain 2: 0.552 seconds (Sampling)
#> Chain 2: 1.035 seconds (Total)
#> Chain 2:
# Multiple imputation marginalization
outstandR_gcomp_stan <- outstandR(AC_IPD, BC_ALD, strategy = strategy_mim(lin_form))
#>
#> SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1: Iteration: 1 / 4000 [ 0%] (Warmup)
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.273 seconds (Warm-up)
#> Chain 1: 0.752 seconds (Sampling)
#> Chain 1: 1.025 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 2.1e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
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#> Chain 2: Elapsed Time: 0.251 seconds (Warm-up)
#> Chain 2: 0.799 seconds (Sampling)
#> Chain 2: 1.05 seconds (Total)
#> Chain 2: