R: Hierarchical Clustering of Patients on X-covariates for...

UPShclus {LocalControl}

R Documentation

Hierarchical Clustering of Patients on X-covariates for Unsupervised Propensiy Scoring

Description

Derive a full, hierarchical clustering tree (dendrogram) for all patients (regardless of treatment received) using Mahalonobis between-patient distances computed from specified baseline X-covariate characteristics.

Usage

UPShclus(envir, dframe, xvars, method, metric)

Arguments

`envir`	name of the working local control classic environment.
`dframe`	Name of data.frame containing baseline X covariates.
`xvars`	List of names of X variable(s).
`method`	Hierarchical Clustering Method: "diana", "agnes" or "hclus".
`metric`	A valid distance metric for clustering.

Details

The first step in an Unsupervised Propensity Scoring alalysis is always to hierarchically cluster patients in baseline X-covariate space. UPShclus uses a Mahalabobis metric and clustering methods from the R "cluster" library for this key initial step.

Value

An output list object of class UPShclus:

dframeName of data.frame containing baseline X covariates.
xvarsList of names of X variable(s).
methodHierarchical Clustering Method: "diana", "agnes" or "hclus".
upshclHierarchical clustering object created by choice between three possible methods.

Author(s)

Bob Obenchain <wizbob@att.net>

References

Kaufman L, Rousseeuw PJ. (1990) Finding Groups in Data. An Introduction to Cluster Analysis. New York: John Wiley and Sons.

Kereiakes DJ, Obenchain RL, Barber BL, et al. (2000) Abciximab provides cost effective survival advantage in high volume interventional practice. Am Heart J 140: 603-610.

Obenchain RL. (2004) Unsupervised Propensity Scoring: NN and IV Plots. Proceedings of the American Statistical Association (on CD) 8 pages.

Obenchain RL. (2011) USPSinR.pdf USPS R-package vignette, 40 pages.

Rubin DB. (1980) Bias reduction using Mahalanobis metric matching. Biometrics 36: 293-298.