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Naive unadjusted indirect comparison

Source: R/naive.R
naive.Rd

Compute an unadjusted (naive) indirect treatment comparison by comparing crude outcomes from the IPD and AgD without any covariate adjustment. The index outcome remains marginal over the index-study population and the comparator outcome remains marginal over the comparator population. The contrast therefore has no single standardized target population. It returns the link-scale contrast plus the two observed marginal outcomes and available natural-scale contrasts.

Usage

naive(data, link = NULL, conf_level = 0.95)

Arguments

data

An mlumr_data object from combine_data()

Link function. For binomial: "logit" (default), "probit", or "cloglog". For normal/poisson: ignored (identity/log always used). For survival: ignored (an unadjusted Cox proportional-hazards log hazard ratio is returned). The naive Cox benchmark accepts only right-censored / event data (optionally with delayed entry); left- or interval-censored data (which the Bayesian mlumr() model supports) are rejected. If NULL, uses the canonical default.

conf_level

Confidence level for the interval (default 0.95)

Value

An object of class mlumr_naive

Details

The two arms are observed directly, so their intervals are exact: the Clopper-Pearson interval for a binomial proportion and the Garwood interval for a Poisson rate, pooled across aggregate rows. The arm standard errors and the contrasts use the boundary pseudo-count (r + 0.5) / (n + 1) when an arm has zero or all events, or 0.5 events for a zero Poisson count; the reported crude proportions and rates are unchanged. The link-scale contrast, the log risk ratio and the risk difference get Wald intervals, whose coverage is approximate.

Scale note: $estimate (and the binomial $log_rr) is on the link / log scale, where the null is 0. To compare against the natural-scale risk ratio or rate ratio from marginal_effects() (where the null is 1), exponentiate it (e.g. exp(result$estimate)).

Normal-family weighting

Across multiple AgD rows the normal-family comparator mean here is population weighted using outcome_n, matching the Bayesian ML-UMR comparator-population estimand. outcome_n is required when there is more than one row; a single row has weight one. The comparator-mean variance combines independent, mutually exclusive strata as sum(w^2 * se^2) using normalized population weights. The same weighting applies to stc().

Examples

if (FALSE) { # \dontrun{
result <- naive(dat)
print(result)
} # }