# Subsampling Potential Patient Populations

While the vpop function returns a VirtualPopulation ensemble of parameter values which correspond to relatively good fits to the data, in many cases this return is referred to as a "potential patient population", i.e. a set of potentially good parameters which may or may not reflect the statistical effects of the population. For example, say that for the data series that is being fit to, 50% of the population known to be fast matabolizers and 50% being slow matabolizers (characterized by some parameter or measurement in the model). The virtual population technique will return N potential patients but there is no guarentee that macro statistical quantities are held. The purpose of the subsample algorithm is to downsample from N to M to find a subpopulation which is more statistically represented by the resulting parameter set.

Missing docstring.

Missing docstring for subsample. Check Documenter's build log for details.

## Subsampling Algorithms

JuliaSimModelOptimizer.ARMType
ARM(; data::DataFrame, save_names, bw=fill(0.5, length(save_names)), N_neighbors::Int=5)

The Allen-Reiger-Musante (ARM) subsampling technique.

References

Allen RJ, Rieger TR, Musante CJ. Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models. CPT Pharmacometrics Syst Pharmacol. 2016 Mar;5(3):140-6. doi: 10.1002/psp4.12063. Epub 2016 Mar 17. PMID: 27069777; PMCID: PMC4809626.

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JuliaSimModelOptimizer.MAPELType
MAPEL(; binning, reference_weights)

The Mechanistic Axes Population Ensemble Linkage (MAPEL) algorithm for prevalence reweighing of a potential patient population to subsample to a virtual population. Uses a binning function with reference weights to choose a subsample of the potential patient population which bins with the same frequency as the reference.

Keyword Arguments

• binning: A function binning(sol) which returns an integer representing the bin the patient applies to.
• reference_weights

References

Schmidt BJ, Casey FP, Paterson T, Chan JR. Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis. BMC Bioinformatics. 2013 Jul 10;14:221. doi: 10.1186/1471-2105-14-221. PMID: 23841912; PMCID: PMC3717130.

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