Model-Based Standardisation for Indirect Treatment Comparison with Limited Subject-Level Data
Aggregate-level data mean and variance statistics
Bayesian G-computation using Stan
G-computation Maximum Likelihood Bootstrap
Calculate individual-level patient data statistics
Calculate MAIC
Multiple imputation marginalization (MIM)
Calculate simulated treatment comparison statistics
Calculate Average Treatment Effect
Calculate Trial Mean Binary Data
Calculate Trial Mean Continuous Data
Calculate Trial Mean Count Data
Calculate Trial Mean Wrapper
Calculate trial variance binary
Calculate trial variance continuous
Calculate trial variance count
Calculate trial variance
Check formula
Continuity Correction
Compute covariance matrix
Estimate Variance Sandwich Estimator
G-computation maximum likelihood mean outcomes by arm
Bootstrap for G-computation via Maximum Likelihood
Retrieve list of allowed variance methods
Get study comparator treatment names
Get covariate names
Get effect modifiers
Get reference treatment
Compute Robust Covariance Matrix (HC0-style)
Get treatment effect scale corresponding to a link function
Get treatment name
Determine and validate variance method for a strategy
Guess treatment name
Factory function for creating calc_IPD_stats methods
Estimate MAIC weights
MAIC bootstrap sample
Marginal treatment effect from reported event counts
Marginal effect variance using the delta method
Numerical Gradient
outstandR class
outstandR: Model-Based Standardisation for Indirect Treatment Comparis...
Calculate the difference between treatments using all evidence
Default Plot Method for outstandR Objects
Prepare Aggregate Level Data
Prepare Individual Patient Data
Prepare Covariate Distributions
Print a Summary of a outstandR Object
Objective function to minimize for standard method of moments MAIC
Convert aggregate data from wide to long format
Convert aggregate data from long to wide format
Calculate and arrange result statistics
Simulate Aggregate-Level Data Pseudo-Population
Strategy class and subclasses
New strategy objects
Summary method for outstandR
Input data validator
Variance estimate by pooling
Wald-type interval estimates
For the problem of indirect treatment comparison with limited subject-level data, this package provides tools for model-based standardisation with several different computation approaches. See Remiro‐Azócar A, Heath A, Baio G (2022) "Parametric G‐computation for compatible indirect treatment comparisons with limited individual patient data", Res. Synth. Methods, 1–31. ISSN 1759-2879, <doi:10.1002/jrsm.1565>.
Useful links