Title: Unsupervised and Supervised methods of Propensity Score
Description: Unsupervised methods: Define Local Treatment Differences
(LTDs) and Local Average Outcomes (LAOs) within Clusters of
well-matched patients and display their distributions across
Clusters.  This form of Nonparametric Preprocessing of
observational data is also called Local Control because it uses
post-hoc blocking to implement nested ANOVA (treatment within
cluster.)  Supervised methods: Estimate and Validate Propensity
Scores and use them to either sub-group or smooth observed
patient outcomes over the common support of alternative

Package: USPS
Title: Unsupervised and Supervised methods of Propensity Score
Adjustment for Bias
Version: 1.2-2
Date: 2012-6-18
Author: Bob Obenchain <wizbob@att.net>
Maintainer: Bob Obenchain <wizbob@att.net>
Depends: R (>= 1.8.0), cluster, lattice, gss
Description: Unsupervised PS Methods define Local Treatment Differences
(LTDs) within numerous Clusters of patients well-matched on
their pre-treatment X-characteristics and display the resulting
distribution of local effect-size estimates across Clusters.  I
now prefer to call this form of Nonparametric Preprocessing of
observational outcomes Local Control; it uses patient blocking
/ matching concepts so as to rely only on a simple model
(Nested ANOVA, treatment within cluster) that becomes more and
more relastic as Clusters become small and numerous. In sharp
contrast, the Supervised PS Methods provided here attempt to
estimate unknow true Propensities with parametric models that
can be quite wrong and unrealistic.  PS estimates always need
to be Validated; there is usually no guarantee that such
estimatres actually block patients with similar
X-characteristics together, like true propensities do.
License: GPL (>= 2)
URL: http://www.r-project.org, http://members.iquest.net/~softrx/
BuildVignettes: no
Packaged: 2012-06-19 03:35:20 UTC; wiz
Repository: CRAN
Date/Publication: 2012-06-19 05:42:57

Title: Create US cancer datasets from SEER, IARC, and US Census data
Description: This package contains functions to read cancer data from SEER (http://seer.cancer.gov/) and IARC (http://www.iarc.fr) to create datasets at the county level based on US census information.