| Title: | Matching on Poset-Based Average Rank for Multiple Treatments (MARMoT) |
| Version: | 0.0.4 |
| Description: | It contains the function to apply MARMoT balancing technique discussed in: Silan, Boccuzzo, Arpino (2021) <doi:10.1002/sim.9192>, Silan, Belloni, Boccuzzo, (2023) <doi:10.1007/s10260-023-00695-0>; furthermore it contains a function for computing the Deloof's approximation of the average rank (and also a parallelized version) and a function to compute the Absolute Standardized Bias. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| Imports: | parallel, parsec, stats |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| Depends: | R (≥ 3.5.0) |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2024-02-05 10:01:08 UTC; alberto |
| Author: | Alberto Calore [aut, cre], Pietro Belloni [aut], Margherita Silan [aut] |
| Maintainer: | Alberto Calore <calorealberto57@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-02-06 17:00:08 UTC |
Absolute standardized bias
Description
Compute the absolute standardized bias of given confounders and return some useful statistics.
Usage
ASB(data, confounders, treatment, verbose = TRUE)
Arguments
data |
A dataframe or equivalent. |
confounders |
A vector with the column names of the confounders to balance by. |
treatment |
A string with the column name of the treatment variable. |
verbose |
Set to FALSE to suppress any console output. Default is TRUE |
Value
A list of objects, containing the ASB matrix and some summary statistics.
Examples
ASB(data = MARMoT_data, confounders = c("race", "age"), treatment = "hospital")
Average rank with Deloof's approximation
Description
Compute the average with using Deloof's approximation.
Usage
deloof(comparable_data)
Arguments
comparable_data |
A dataframe or equivalent, which columns are ordered factors or numeric variables. |
Value
A vector containing the average rank of all observations (it is recommended to normalized it before use).
References
Caperna, G., 2019. Approximation of AverageRank by means of a formula. https://doi.org/10.5281/zenodo.2565699
Examples
AR = deloof(deloof_data)
Data to showcase deloof and mcdeloof functions
Description
A very simple example dataset
Usage
deloof_data
Format
deloof_data
A data frame with 8,450 rows and 2 columns:
- comorbidities, age
our partially ordered variables
Source
Created in-house to serve as an example
MARMoT balancing method
Description
Matching on poset-based average rank for multiple treatments (MARMoT).
Usage
MARMoT(
data,
confounders,
treatment,
reference = "median",
n.cores = 1,
caliper = 0.25,
verbose = TRUE
)
Arguments
data |
A dataframe or equivalent. |
confounders |
A vector containing the column names of the confounders to balance by. |
treatment |
A string indicating the column name of the treatment variable. |
reference |
The statistic used to determine the reference frequencies in the balancing process. Default is median. |
n.cores |
Number of cores to be used (Linux and Mac systems only!); if a number grater than 1 is specified the function will use a parallelized version of the deloof approximation. Default set to 1. |
caliper |
Fraction of the standard deviation used to determine the closest neighbour. Default is 0.25. |
verbose |
Set to FALSE to suppress any console output. Default is TRUE |
Details
There are many scenarios where classic propensity score techniques are not applicable (e.g. there are many treatments). In a multiple-treatment framework, MARMoT is a method to balance the distribution of covariates among several treatment groups. MARMoT introduces a method for achieving balance among treatment groups by utilizing partially ordered set (poset) theory. This approach focuses on equalizing individual characteristics without relying on propensity score techniques or a dependent variable. Unlike propensity score methods, poset theory doesn't require assumptions about model specifications for treatment allocation. Each subject is represented by a profile of their characteristics, and an average rank approximation is associated with each profile. This value represents the significance of individual characteristics for treatment allocation and can be normalized for better interpretability.
Value
A list of objects, also containing the balanced dataset with the same structure of the input dataset.
References
Silan, M., Boccuzzo, G. and Arpino, B., 2021. 'Matching on poset‐based average rank for multiple treatments to compare many unbalanced groups'. Statistics in Medicine, 40(28), pp.6443-6458. https://doi.org/10.1002/sim.9192
Silan, M., Belloni, P. and Boccuzzo, G., 2023. 'Identification of neighborhood clusters on data balanced by a poset-based approach'. Statistical Methods & Applications, pp.1-22. https://doi.org/10.1007/s10260-023-00695-0
Examples
out = MARMoT(data = MARMoT_data, confounders = c("race", "age"),
treatment = "hospital", n.cores = 1)
out
Data to showcase MARMoT and ASB functions
Description
An example dataset where our treatment is the hospital structure.
Usage
MARMoT_data
Format
MARMoT_data
A data frame with 8450 rows and 3 columns:
- hospital
our treatment
- race, age
our confounders
Source
Created in-house to serve as an example
Average rank with Deloof's approximation (parallel computing)
Description
Compute the average with using Deloof's approximation, using parallel computing; it can be used only on Linux and Mac systems!
Usage
mcdeloof(comparable_data, n.cores)
Arguments
comparable_data |
A dataframe or equivalent, which columns are ordered factors or numeric variables. |
n.cores |
Number of cores to be used. |
Value
A vector containing the average rank of all the observations (it is recommended to normalized it before use).
References
Caperna, G., 2019. Approximation of AverageRank by means of a formula. https://doi.org/10.5281/zenodo.2565699
Examples
AR = mcdeloof(deloof_data, n.cores = 1)