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Calculate individual-level patient data statistics

Source: R/IPD_stats.R
IPD_stats.Rd

Computes mean and variance statistics for individual-level patient data using various approaches, including Matching-Adjusted Indirect Comparison (MAIC), Simulated Treatment Comparison (STC), and G-computation via Maximum Likelihood Estimation (MLE) or Bayesian inference.

Usage

IPD_stats(strategy, ipd, ald, scale, ...)

# Default S3 method
IPD_stats(...)

# S3 method for class 'mim'
IPD_stats(strategy, ipd, ald, scale, ...)

# S3 method for class 'stc'
IPD_stats(strategy, ipd, ald, scale, ...)

# S3 method for class 'maic'
IPD_stats(strategy, ipd, ald, scale, ...)

# S3 method for class 'gcomp_ml'
IPD_stats(strategy, ipd, ald, scale, ...)

# S3 method for class 'gcomp_stan'
IPD_stats(strategy, ipd, ald, scale, ...)

Arguments

strategy

A list corresponding to different approaches

ipd

Individual-level data

ald

Aggregate-level data

scale

A scaling parameter for the effect calculation.

...

Additional arguments

Value

A list containing:

mean

Estimated mean treatment effect.

var

Estimated variance of the treatment effect.

Multiple imputation marginalisation

Using Stan, compute marginal relative treatment effect for A vs C for each MCMC sample by transforming from probability to linear predictor scale. Approximate by using imputation and combining estimates using Rubin's rules, in contrast to IPD_stats.gcomp_stan().

Simulated treatment comparison statistics

IPD from the AC trial are used to fit a regression model describing the observed outcomes \(y\) in terms of the relevant baseline characteristics \(x\) and the treatment variable \(z\).

Matching-adjusted indirect comparison statistics

Marginal A vs C treatment effect estimates using bootstrapping sampling.

G-computation maximum likelihood statistics

Compute a non-parametric bootstrap with default \(R=1000\) resamples.

G-computation Bayesian statistics

Using Stan, compute marginal log-odds ratio for A vs C for each MCMC sample by transforming from probability to linear predictor scale.

Examples

if (FALSE) { # \dontrun{
strategy <- strategy_maic()
ipd <- data.frame(id = 1:100, treatment = sample(c("A", "C"), 100, replace = TRUE), outcome = rnorm(100))
ald <- data.frame(treatment = c("A", "C"), mean = c(0.2, 0.1), var = c(0.05, 0.03))
IPD_stats(strategy, ipd, ald, scale = "log")
} # }