maic

A generalised workflow for generation of subject weights to be used in Matching-Adjusted Indirect Comparison (MAIC) per Signorovitch et al. (2012) doi:10.1016/j.jval.2012.05.004, Signorovitch et al (2010) doi:10.2165/11538370-000000000-00000. In MAIC, unbiased comparison between outcomes of two trials is facilitated by weighting the subject-level outcomes of one trial with weights derived such that the weighted aggregate measures of the prognostic or effect modifying variables are equal to those of the sample in the comparator trial. The functions and classes included in this package wrap and abstract the process demonstrated in the UK National Institute for Health and Care Excellence Decision Support Unit (NICE DSU)'s example (Phillippo et al, (2016) see URL), providing a repeatable and easily specifiable workflow for producing multiple comparison variable sets against a variety of target studies, with preprocessing for a number of aggregate target forms (e.g. mean, median, domain limits).

Last 30 days

This package has been downloaded 234 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 6 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 3,686 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Jul 21, 2024 with 152 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.