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Set up aggregate data (AgD)

Source: R/data_setup.R
set_agd.Rd

Prepare AgD from the comparator treatment for an unanchored indirect comparison.

Usage

set_agd(
  data,
  treatment,
  family = c("binomial", "normal", "poisson"),
  outcome_n = NULL,
  outcome_r = NULL,
  outcome_mean = NULL,
  outcome_se = NULL,
  outcome_E = NULL,
  cov_means,
  cov_sds = NULL,
  cov_types = NULL,
  study = NULL
)

Arguments

data

Data frame containing AgD summary statistics

treatment

Column name for treatment variable

family

Outcome family: "binomial", "normal", or "poisson"

outcome_n

Column name for sample size (required for binomial, optional for normal)

outcome_r

Column name for number of events (required for binomial and poisson)

outcome_mean

Column name for mean outcome (required for normal)

outcome_se

Column name for standard error of outcome (required for normal)

outcome_E

Column name for total exposure (required for poisson)

cov_means

Character vector of column names for covariate means/proportions

cov_sds

Character vector of column names for covariate SDs (NA for binary covariates)

cov_types

Character vector specifying "continuous" or "binary" for each covariate. If NULL, inferred from presence of SD.

study

Column name for study identifier (optional)

Value

An object of class mlumr_agd

Details

Scale assumptions for family = "normal". The AgD likelihood is y_agd ~ normal(E[exp(eta)], se_agd) under link = "log" and y_agd ~ normal(E[eta], se_agd) under link = "identity". In both cases outcome_mean and outcome_se must be on the arithmetic (original, untransformed) scale. If a publication reports only a log-scale mean / SD or a geometric mean, back-transform before calling set_agd() and propagate uncertainty via the delta method; passing log-scale or geometric summaries silently misspecifies the likelihood and biases the posterior.

Scale assumptions for family = "poisson". outcome_r is the total count in each AgD row and outcome_E is the total person-time (or other exposure). The Stan likelihood uses log(E_agd) as an offset, so rates are modeled on the log scale regardless of how outcome_r is tabulated.

Scale assumptions for family = "binomial". outcome_r / outcome_n are counts of events and trials. The log-odds (or probit / cloglog under alternative links) are formed from outcome_r / outcome_n, so no scale conversion is required.

Examples

if (FALSE) { # \dontrun{
# Binary outcome
agd <- set_agd(
  data = trial_b,
  treatment = "trt",
  outcome_n = "n_total",
  outcome_r = "n_events",
  cov_means = c("age_mean", "sex_prop"),
  cov_sds = c("age_sd", NA),
  cov_types = c("continuous", "binary")
)

# Continuous outcome
agd <- set_agd(
  data = trial_b,
  treatment = "trt",
  family = "normal",
  outcome_mean = "mean_score",
  outcome_se = "se_score",
  outcome_n = "n_total",
  cov_means = c("age_mean", "sex_prop")
)

# Count outcome
agd <- set_agd(
  data = trial_b,
  treatment = "trt",
  family = "poisson",
  outcome_r = "n_events",
  outcome_E = "person_years",
  cov_means = c("age_mean", "sex_prop")
)
} # }