Computes mean and variance statistics for individual-level patient data using various approaches, including Matching-Adjusted Indirect Comparison (MAIC), Simulated Treatment Comparison (STC), and G-computation via Maximum Likelihood Estimation (MLE) or Bayesian inference.
Usage
calc_IPD_stats(strategy, analysis_params, ...)
# Default S3 method
calc_IPD_stats(...)
# S3 method for class 'stc'
calc_IPD_stats(strategy, analysis_params, var_method = NULL, ...)
# S3 method for class 'maic'
calc_IPD_stats(strategy, analysis_params, var_method = NULL, ...)
# S3 method for class 'gcomp_ml'
calc_IPD_stats(strategy, analysis_params, var_method = NULL, ...)
# S3 method for class 'gcomp_bayes'
calc_IPD_stats(strategy, analysis_params, var_method = NULL, ...)
# S3 method for class 'mim'
calc_IPD_stats(strategy, analysis_params, var_method = NULL, ...)Arguments
- strategy
A list corresponding to different modelling approaches
- analysis_params
A list containing:
ipd: Individual-level patient data (data frame)ald: Aggregate-level trial data (data frame)ref_trt: Treatment label for the reference arm (common; e.g., "C")ipd_comp: Treatment label for the comparator arm in the IPD (e.g., "A")scale: Scaling parameter ("log_odds", "risk_difference", "log_relative_risk")
- ...
Additional arguments
- var_method
A string specifying the variance estimation method, either "sample" (default) or "sandwich".
Value
A list containing:
contrasts: A list with elementsmeanandvar.absolute: A list with elementsmeanandvar.
Simulated treatment comparison statistics
IPD for reference "C" and comparator "A" trial arms are used to fit a regression model describing the observed outcomes \(y\) in terms of the relevant baseline characteristics \(x\) and the treatment variable \(z\).
Matching-adjusted indirect comparison statistics
Marginal IPD comparator treatment "A" vs reference treatment "C" treatment effect estimates using bootstrapping sampling.
G-computation maximum likelihood statistics
Compute a non-parametric bootstrap with default \(R=1000\) resamples.
G-computation Bayesian statistics
Using Stan, compute marginal relative effects for IPD comparator "A" vs reference "C" treatment arms for each MCMC sample by transforming from probability to linear predictor scale.
Multiple imputation marginalisation
Using Stan, compute marginal relative treatment effect for IPD comparator "A" vs reference "C" arms for each MCMC sample by transforming from probability to linear predictor scale. Approximate by using imputation and combining estimates using Rubin's rules.
Examples
strategy <- strategy_maic(formula = as.formula(y~trt:X1), family = binomial())
ipd <- data.frame(trt = sample(c("A", "C"), size = 100, replace = TRUE),
X1 = rnorm(100, 1, 1),
y = sample(c(1,0), size = 100, prob = c(0.7,0.3), replace = TRUE))
ald <- data.frame(trt = c(NA, "B", "C", "B", "C"),
variable = c("X1", "y", "y", NA, NA),
statistic = c("mean", "sum", "sum", "N", "N"),
value = c(0.5, 10, 12, 20, 25))
calc_IPD_stats(strategy,
analysis_params = list(ipd = ipd, ald = ald, scale = "log_odds"))
#> $contrasts
#> $contrasts$mean
#> [1] 0.04072276
#>
#> $contrasts$var
#> [1] 0.2451418
#>
#>
#> $absolute
#> $absolute$mean
#> A C
#> 0.7025638 0.6951590
#>
#> $absolute$var
#> A C
#> 0.005650160 0.005183217
#>
#>
#> $method_name
#> [1] "MAIC"
#>
#> $model
#> $model$weights
#> weights1 weights2 weights3 weights4 weights5 weights6 weights7
#> 0.8644187 0.7471808 1.0941498 0.5137571 1.6671230 1.0104253 0.7662525
#> weights8 weights9 weights10 weights11 weights12 weights13 weights14
#> 1.0924254 0.7196236 0.8194893 1.1114083 0.7223890 1.2919443 0.8837867
#> weights15 weights16 weights17 weights18 weights19 weights20 weights21
#> 0.8534521 1.0946767 0.3083721 0.7779886 0.7720410 0.5818904 1.2813310
#> weights22 weights23 weights24 weights25 weights26 weights27 weights28
#> 0.4048974 0.7521981 1.2995935 1.0658084 0.8159727 1.5211228 0.5130418
#> weights29 weights30 weights31 weights32 weights33 weights34 weights35
#> 0.8678377 0.9656656 0.7451850 1.2546478 1.4252054 2.1407440 0.3410411
#> weights36 weights37 weights38 weights39 weights40 weights41 weights42
#> 0.3434997 1.0843389 1.7071231 0.3736615 1.0078974 1.2780704 0.8389136
#> weights43 weights44 weights45 weights46 weights47 weights48 weights49
#> 0.9167890 0.6745588 1.0534299 0.4039551 0.3274506 0.7164308 1.8885636
#> weights50 weights51 weights52 weights53 weights54 weights55 weights56
#> 1.2057152 1.0639350 1.5633846 0.9396529 3.0833536 0.9397011 0.4908616
#> weights57 weights58 weights59 weights60 weights61 weights62 weights63
#> 1.1619506 0.5912057 0.7738476 0.3515805 0.8976565 0.5447694 0.4509756
#> weights64 weights65 weights66 weights67 weights68 weights69 weights70
#> 0.7542419 1.0597763 0.9247797 1.8938653 0.6103745 0.8746193 0.5307043
#> weights71 weights72 weights73 weights74 weights75 weights76 weights77
#> 0.4544261 0.6079698 0.8191396 1.3460228 0.7095652 0.5928967 0.4365664
#> weights78 weights79 weights80 weights81 weights82 weights83 weights84
#> 1.4783556 0.5532154 0.2758676 0.5065433 0.3944689 1.0978242 0.3406655
#> weights85 weights86 weights87 weights88 weights89 weights90 weights91
#> 0.9603423 0.8679057 0.6058239 1.2072252 0.4784969 0.5336189 0.7415894
#> weights92 weights93 weights94 weights95 weights96 weights97 weights98
#> 0.7947574 1.2495765 0.7779616 0.7617820 0.4187021 0.3652338 1.6012872
#> weights99 weights100
#> 0.2849569 1.3295498
#>
#> $model$ESS
#> ESS
#> 79.4969
#>
#>