Component simulated treatment comparison (cSTC)

Anchored STC generalized to a (possibly disconnected) component network. For each IPD study an outcome regression is fitted with treatment main effects, prognostic main effects, and treatment-by-effect-modifier interactions. The effect modifiers are centered at common target means, so the treatment coefficient is the anchored average conditional link-scale contrast at those means. These adjusted contrasts replace the corresponding unadjusted aggregate contrasts and cnma_bridge() combines them through the additive component model.

Usage

cstc(
  network,
  target,
  effect_modifiers = NULL,
  prognostics = NULL,
  common = FALSE,
  random = TRUE,
  reference = NULL,
  allow_experimental_bridge = FALSE,
  allow_ipd_only_studies = FALSE
)

Arguments

network: A cpaic_network() object that includes IPD.
target: Named numeric vector (or list / one-row data frame) of target means for the effect modifiers.
effect_modifiers: Covariates that interact with treatment (centered at target). Defaults to all IPD covariates.
prognostics: Covariates included as main effects only. Defaults to the effect modifiers (so each enters as main effect + interaction).
common, random: Passed to cnma_bridge().
reference: Optional anchor (comparator) arm to use in every IPD study in which it appears, instead of inferring it from the aggregate row order.
allow_experimental_bridge: Logical. The default FALSE stops when aggregate-only edges would be combined with target-adjusted IPD edges. Set TRUE only for explicitly exploratory sensitivity work; the fit records the retained edges and the reason the bridge is approximate.
allow_ipd_only_studies: Logical. The default FALSE requires every IPD study to match exactly one aggregate two-arm edge. Set TRUE to append an IPD-derived edge that has no aggregate row. Such additions are recorded in the returned fit.

Value

An object of class cpaic_stc (and cpaic_fit).

Details

Unlike cmaic() (reweighting) this is the regression-adjustment route. The reported treatment coefficient is the conditional effect at the target effect-modifier means. Equivalently, under the fitted linear interaction model this is the average conditional link-scale effect at the supplied target means, not a marginal standardization. It is implemented natively here because the stc() function in the mlumr package targets the unanchored two-trial case; the link and standard-error machinery is adapted from that package. (Written without the double-colon form on purpose: mlumr is not a dependency of cpaic, and the documentation site resolves a qualified package-and-function reference by loading that package.)

What the two-stage bridge does and does not adjust

Only the edges carrying individual patient data are population-adjusted to the target means. Every aggregate-only edge keeps its published study-specific contrast, and the additive bridge then combines all edges as if they estimated the same component effects. Under effect modification they do not: an aggregate edge estimates its contrast in its own trial population, while the adjusted IPD edge estimates it at the target. The two agree only when the aggregate populations resemble the target, or when the components on those edges are not effect-modified. Treat a cross-network contrast that leans on aggregate-only edges as adjusted for the IPD part alone. Prefer cmlnmr() for a joint model whose average conditional link-scale outputs are explicitly evaluated at common target effect-modifier means.

Examples

net <- cpaic_network(cpaic_bin_agd, ipd = cpaic_bin_ipd, sm = "OR",
                     family = "binomial", ipd_covariates = "x1",
                     inactive = "Placebo")
fit <- cstc(net, target = c(x1 = 0), effect_modifiers = "x1",
             allow_experimental_bridge = TRUE)
#> Warning: cstc() cannot form a decision-grade component bridge:
#>   - retained aggregate-only edge(s) remain in their own study populations: S1: A vs Placebo; S2: B vs Placebo; S5: A+B+C vs A+B+D
#> Use cmlnmr() for a joint model, restrict the analysis to a design in which every edge is adjusted and the estimand is additive, or set `allow_experimental_bridge = TRUE` only for explicitly exploratory sensitivity work.
relative_effects(fit)
#> Relative effects (OR, natural scale)
#>  treatment comparator estimate estimate_link se_link lower  upper   scale     z
#>          A    Placebo    1.649         0.500   0.256 0.998  2.725 natural 1.951
#>        A+B    Placebo    2.460         0.900   0.363 1.209  5.005 natural 2.483
#>      A+B+C    Placebo    4.014         1.390   0.435 1.711  9.416 natural 3.194
#>      A+B+D    Placebo    4.669         1.541   0.430 2.009 10.850 natural 3.582
#>          B    Placebo    1.492         0.400   0.256 0.903  2.466 natural 1.560
#>      p
#>  0.051
#>  0.013
#>  0.001
#>  0.000
#>  0.119
#>   `se_link` is on the log-ratio scale; the interval is back-transformed.
additivity_test(fit)
#> Additive component model: fit statistics
#>   Total lack of fit (Q.additive): Q = 2.669, df = 1, p = 0.102
#>   Additivity restrictions (Q.diff): not available; no standard NMA
#>     is estimable on a disconnected network.
#>   Note: neither statistic tests whether component effects are constant
#>   ACROSS sub-networks, which is the assumption that bridges the gap.
#>   That assumption is untestable from the data and must be justified
#>   clinically.