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Set up aggregate survival data (reconstructed pseudo-IPD)

Source: R/survival.R
set_agd_surv.Rd

Prepare comparator aggregate survival data for an unanchored indirect comparison. The comparator arm is supplied as reconstructed pseudo-IPD (event/censoring times digitized from a published Kaplan-Meier curve, e.g. via the Guyot algorithm) together with summary covariate moments (means/SDs). The Stan model integrates the comparator likelihood over the covariate distribution implied by those moments.

Usage

set_agd_surv(
  data,
  treatment,
  Surv = NULL,
  time = NULL,
  status = NULL,
  entry_time = NULL,
  cov_means,
  cov_sds = NULL,
  cov_types = NULL,
  study = NULL,
  arm = NULL
)

Arguments

data

Data frame of reconstructed pseudo-IPD (one row per pseudo-individual).

treatment

Column name for the (single) comparator treatment.

Surv

Optional survival::Surv() object describing the outcome. Use this for left/interval censoring or delayed entry.

time, status, entry_time

Character column names as an alternative to Surv (right-censoring with status 0/1, plus optional delayed entry).

cov_means

Character vector of covariate mean/proportion column names (constant within each arm). Suffixes _mean/_prop are stripped to match the IPD covariate names.

cov_sds

Character vector of covariate SD column names (NA for binary covariates). NULL treats all covariates as binary.

cov_types

Character vector of "continuous"/"binary" per covariate. If NULL, inferred from the presence of an SD column.

study

Optional study identifier column.

arm

Optional arm identifier column. Only a single comparator arm is supported; if supplied, it must have one unique value. Multi-arm reconstructed survival comparators are rejected until a weighting estimand is implemented. Defaults to a single arm.

Value

An object of class mlumr_agd_surv (also inheriting mlumr_agd). The internal column names cannot be used as column names in data.

Details

Under delayed entry the comparator likelihood conditions each integration point on survival to its entry time and then averages, so cov_means, cov_sds and the distributions add_integration() builds must describe the population observed at entry (those in the risk set), not a baseline population before selection. With varying entry times that population can differ by entry time while the model has one distribution per arm; pooled summaries are right only under a common entry time or when the covariate distribution among those observed at entry is the same at every entry time. Neither condition is checkable from the summaries supplied. Delayed entry in the individual arm is unaffected.

Reconstruction uncertainty is not propagated

The pseudo-individual records enter the likelihood as observed data, so credible intervals are conditional on this one reconstruction and narrower than the evidence supports. Treat the reconstruction as an analysis choice: digitize the curve more than once or perturb the points within their reading error, refit, and report the spread across refits beside the within-fit interval.

See also

set_agd() for non-survival aggregate data; multinma::set_agd_surv() is the ML-NMR equivalent.

Examples

if (FALSE) { # \dontrun{
agd <- set_agd_surv(
  data = comparator_km,
  treatment = "trt",
  time = "time", status = "status",
  cov_means = c("age_mean", "male_prop"),
  cov_sds = c("age_sd", NA),
  cov_types = c("continuous", "binary")
)
} # }