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Calculate the difference between treatments using all evidence

Source: R/outstandR.R
outstandR.Rd

This is the main, top-level wrapper for {outstandR}. Methods taken from (Remiro‐Azócar et al. 2022) .

Usage

outstandR(
  ipd_trial,
  ald_trial,
  strategy,
  ref_trt = NA,
  CI = 0.95,
  scale = NULL,
  var_method = NULL,
  seed = NULL,
  verbose = TRUE,
  ...
)

Arguments

ipd_trial

Individual-level patient data. For example, suppose between studies A and C. In a long format and must contain a treatment column and outcome column consistent with the formula object. The labels in the treatment are used internally so there must be a common treatment with the aggregate-level data trial.

ald_trial

Aggregate-level data. For example, suppose between studies B and C. The column names are

  • variable: Covariate name. In the case of treatment arm sample size this is NA,

  • statistic: Summary statistic name from "mean", standard deviation "sd", probability "prop", or "sum",

  • value: Numerical value of summary statistic,

  • trt: Treatment label. Because we assume a common covariate distribution between treatment arms this is NA.

strategy

Computation strategy function. These can be strategy_maic(), strategy_stc(), strategy_gcomp_ml() and strategy_gcomp_bayes().

ref_trt

Reference / common / anchoring treatment name.

CI

Confidence interval level; between 0,1 with default 0.95.

scale

Relative treatment effect scale. If NULL, the scale is automatically determined from the model. Choose from "log-odds", "log_relative_risk", "risk_difference", "delta_z", "mean_difference", "rate_difference" depending on the data type.

var_method

Variance estimation method.

seed

Random seed.

verbose

Logical. If TRUE, prints progress messages and warnings.

...

Additional arguments. Currently, can pass named arguments to rstanarm::stan_glm() via strategy_gcomp_bayes().

Value

List of length 11 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.

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.

See also

strategy_maic() strategy_stc() strategy_gcomp_ml() strategy_gcomp_bayes()

Examples

data(AC_IPD_binY_contX)  # A vs C individual patient-level data
data(BC_ALD_binY_contX)  # B vs C aggregate-level data

# linear formula
lin_form <- as.formula("y ~ PF_cont_1 + PF_cont_2 + trt*EM_cont_1 + trt*EM_cont_2")
                                
# sampling values of additional arguments picked for speed
# select appropriate to specific analysis

# matching-adjusted indirect comparison
outstandR_maic <- outstandR(
  AC_IPD_binY_contX, BC_ALD_binY_contX,
  strategy = strategy_maic(formula = lin_form, n_boot = 100))
#> Note: Using legacy 'formula' argument.
#> --> Analysis Model: y ~ PF_cont_1 + PF_cont_2 + trt * EM_cont_1 + trt * EM_cont_2
#> --> Inferred Balance Model: ~ PF_cont_1 + PF_cont_2 + EM_cont_1 + EM_cont_2
#>     (Balancing on means of these covariates by default)
#> Warning: Covariates detected in the MAIC outcome model. To ensure the estimation of compatible marginal treatment effects, MAIC requires an unadjusted outcome model. The outcome model is being automatically overridden to 'y ~ trt'.
#> 
#> ── Starting outstandR Analysis ─────────────────────────────────────────────────
#> ℹ Strategy: maic
#> 
#> ── MAIC Execution ──
#> 
#> ℹ Calculating weights using method of moments...
#> ℹ Starting Bootstrap with 100 replicates.
#> Treatment is guessed as: trt

# simulated treatment comparison
outstandR_stc <- outstandR(
  AC_IPD_binY_contX, BC_ALD_binY_contX,
  strategy = strategy_stc(lin_form))
#> Warning: `strategy_stc()` was deprecated in outstandR 1.X.X.
#> ℹ Please use `strategy_gcomp_ml()` instead.
#> 
#> ── Starting outstandR Analysis ─────────────────────────────────────────────────
#> ℹ Strategy: stc

# \donttest{
# G-computation with maximum likelihood
outstandR_gcomp_ml <- outstandR(
  AC_IPD_binY_contX, BC_ALD_binY_contX,
  strategy = strategy_gcomp_ml(lin_form, n_boot = 100, N =100))
#> 
#> ── Starting outstandR Analysis ─────────────────────────────────────────────────
#> ℹ Strategy: gcomp_ml
#> 
#> ── G-Computation (ML) Execution ──
#> 
#> ℹ Fitting initial model...
#> ℹ Starting Bootstrap: 100 replicates.
#> ℹ Simulating pseudo-pop (N=100) per replicate.

# G-computation with Bayesian inference
outstandR_gcomp_bayes <- outstandR(
  AC_IPD_binY_contX, BC_ALD_binY_contX,
  strategy = strategy_gcomp_bayes(lin_form),
  chains = 1, iter = 1000, warmup = 20)
#> 
#> ── Starting outstandR Analysis ─────────────────────────────────────────────────
#> ℹ Strategy: gcomp_bayes
#> 
#> ── G-Computation (Bayesian) Execution ──
#> 
#> ℹ Compiling/Sampling Stan model...
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 4.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.43 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1:          three stages of adaptation as currently configured.
#> Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1:          the given number of warmup iterations:
#> Chain 1:            init_buffer = 3
#> Chain 1:            adapt_window = 15
#> Chain 1:            term_buffer = 2
#> Chain 1: 
#> Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
#> Chain 1: Iteration:  21 / 1000 [  2%]  (Sampling)
#> Chain 1: Iteration: 520 / 1000 [ 52%]  (Sampling)
#> Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.002 seconds (Warm-up)
#> Chain 1:                0.103 seconds (Sampling)
#> Chain 1:                0.105 seconds (Total)
#> Chain 1: 

# Multiple imputation marginalization
outstandR_mim <- outstandR(
  AC_IPD_binY_contX, BC_ALD_binY_contX,
  strategy = strategy_mim(lin_form,
                          N = 100), # size of pseudo-population
  chains = 1, iter = 1000, warmup = 20)
#> 
#> ── Starting outstandR Analysis ─────────────────────────────────────────────────
#> ℹ Strategy: mim
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1:          three stages of adaptation as currently configured.
#> Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1:          the given number of warmup iterations:
#> Chain 1:            init_buffer = 3
#> Chain 1:            adapt_window = 15
#> Chain 1:            term_buffer = 2
#> Chain 1: 
#> Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
#> Chain 1: Iteration:  21 / 1000 [  2%]  (Sampling)
#> Chain 1: Iteration: 120 / 1000 [ 12%]  (Sampling)
#> Chain 1: Iteration: 220 / 1000 [ 22%]  (Sampling)
#> Chain 1: Iteration: 320 / 1000 [ 32%]  (Sampling)
#> Chain 1: Iteration: 420 / 1000 [ 42%]  (Sampling)
#> Chain 1: Iteration: 520 / 1000 [ 52%]  (Sampling)
#> Chain 1: Iteration: 620 / 1000 [ 62%]  (Sampling)
#> Chain 1: Iteration: 720 / 1000 [ 72%]  (Sampling)
#> Chain 1: Iteration: 820 / 1000 [ 82%]  (Sampling)
#> Chain 1: Iteration: 920 / 1000 [ 92%]  (Sampling)
#> Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0 seconds (Warm-up)
#> Chain 1:                0.106 seconds (Sampling)
#> Chain 1:                0.106 seconds (Total)
#> Chain 1: 
#> Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
# }