Add numerical integration points — add

Generate quasi-Monte Carlo integration points using Sobol sequences and a Gaussian copula to account for correlations between covariates in the AgD.

Usage

add_integration(
  data,
  n_int = 64,
  cor = NULL,
  cor_adjust = NULL,
  verbose = TRUE,
  ...
)

Arguments

data: An mlumr_data object from combine_data()
n_int: Number of integration points (default 64; use powers of 2). More points can improve quasi-Monte Carlo integration of the AgD likelihood and comparator-population estimands. Increase it when check_integration() shows numerical sensitivity. Wider posterior intervals alone indicate neither an inadequate grid nor a need for more points. Larger values cost more sampling time.
cor: Correlation matrix for covariates, on the covariate scale. If NULL (the default) it is estimated from the IPD; see the correlation-transport note in Details.
cor_adjust: Adjustment method: "spearman", "pearson", or "none"
verbose: Logical; if FALSE, suppresses progress messages.
...: Distribution specifications for each covariate using distr()

Value

An mlumr_data object with integration points added

Details

The correlation structure is assumed to transport. Aggregate data report marginal summaries only, so with cor = NULL the within-row correlation is estimated from the IPD and applied to every comparator row, as in ML-NMR (Phillippo et al. 2020). The assumption is untestable from the data; supply cor from an external source to vary it, and use check_integration() to confirm the realized moments and correlations.

cor_adjust maps the covariate-scale correlation onto the Gaussian copula: the Spearman map is exact for continuous margins, the Pearson map holds for Gaussian margins, and pairs involving a binary margin use prevalence-independent heuristics. A nonbinary discrete margin (a count or an ordered category) is treated as continuous and its realized association need not match the target; add_integration() warns when it sees one. "none" passes a latent Gaussian-copula matrix through unchanged.

Examples

if (FALSE) { # \dontrun{
dat <- add_integration(
  dat,
  n_int = 64,
  x1 = distr(qnorm, mean = x1_mean, sd = x1_sd),
  x2 = distr(qbern, prob = x2_mean)
)
} # }