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 a common target population so the treatment coefficient is the anchored, population-adjusted contrast in that population. 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
)

Arguments

network: A cpaic_network() object that includes IPD.
target: Named numeric vector (or list / one-row data frame) of target-population 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().

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 (not a marginal standardization); for collapsible measures the two coincide. It is implemented natively here because mlumr::stc() targets the unanchored two-trial case; the link and standard-error machinery is adapted from 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. Every aggregate-only edge keeps its published, study-population 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, and prefer cmlnmr(), which carries the component by effect-modifier interactions through the whole network and so adjusts every edge to the same target population coherently.

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")
relative_effects(fit)
#> Relative effects (OR, back-transformed)
#>  treatment comparator estimate    se lower  upper     z     p
#>          A    Placebo    1.649 0.256 0.998  2.725 1.951 0.051
#>        A+B    Placebo    2.460 0.363 1.209  5.005 2.483 0.013
#>      A+B+C    Placebo    4.014 0.435 1.711  9.416 3.194 0.001
#>      A+B+D    Placebo    4.669 0.430 2.009 10.850 3.582 0.000
#>          B    Placebo    1.492 0.256 0.903  2.466 1.560 0.119
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.