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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". Time-to-event comparator data go to set_agd_surv() instead, which takes reconstructed pseudo-IPD rather than a scalar outcome summary.

outcome_n

Column name for sample size. Required for binomial. For normal, required when there is more than one aggregate row, because the comparator-population estimand is the size-weighted mixture of those rows and they cannot be combined without knowing how large each is; optional for a single row, where the weighting is irrelevant.

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. As for set_ipd(), the internal column names cannot be used as column names in data.

Details

Rows must partition the aggregate sample. Each row contributes its own likelihood factor, as if the rows were disjoint sets of patients. One arm, or one set of mutually exclusive subgroup cells, is right; several overlapping subgroup tables of the same participants count every patient once per table and overstate the precision. Nothing in the data reveals the overlap, so it is not checked. See vignette("subgroup-identification", "mlumr") for how many rows the relaxed model needs.

Scales. For family = "normal", outcome_mean and outcome_se are on the arithmetic scale under both links; a geometric mean or a log-scale summary is a different quantity and cannot be converted by the delta method. For family = "poisson", outcome_r is the total count and outcome_E the total person-time, and the covariate distribution the rate is averaged over has to describe the covariates weighted by exposure; person-level moments stand in for that only when exposure carries no information about the rate within the row. For family = "binomial", outcome_r and outcome_n are counts of events and trials.

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")
)
} # }