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