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

Description Usage Arguments Details Value Author(s) References See Also

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

1
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:

Author(s)

Bob Obenchain <[email protected]>

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.

See Also

UPSaccum, UPSnnltd and UPSgraph.


LocalControl documentation built on May 2, 2019, 7:29 a.m.