person(given = "Nicolas R.", family = "Lauve", role = "aut"),
person(given = "Stuart J.", family = "Nelson", role = "aut"),
person(given = "S. Stanley", family = "Young", role = "aut"),
person(given = "Robert L.", family = "Obenchain", role = "aut"),
person(given = "Melania", family = "Pintilie", role = "ctb"),
person(given = "Martin", family = "Kutz", role = "ctb"),
person(given = "Christophe G.", family = "Lambert", role = c("aut","cre"), email = "cglambert@unm.edu"))

Suggests: colorspace, RColorBrewer, TeachingDemos, data.table,
dendextend, ggplot2, gplots, gridExtra, rpart, rpart.plot,

The package implements a family of methods for non-parametric bias correction
when comparing treatments in observational studies, including survival
analysis settings, where competing risks and/or censoring may be present.
The approach extends to bias-corrected personalized predictions of treatment
outcome differences, and analysis of heterogeneity of treatment effect-sizes
Suggests: colorspace, RColorBrewer, TeachingDemos, data.table, ggplot2,

Package: LocalControlStrategy
Title: Local Control Strategy for Robust Analysis of Cross-Sectional
Data
Version: 1.3.1
Date: 2018-10-01
Author: Bob Obenchain
Maintainer: Bob Obenchain <wizbob@att.net>
Depends: cluster, lattice
Description: Especially when cross-sectional data are observational, effects of treatment
selection bias and confounding are revealed by using the unsupervised, nonparametric
preprocessing methods central to Local Control (LC) Strategy. The LC objective is to
estimate the "effect-size distribution" that best quantifies a potentially causal
relationship between a numeric y-Outcome variable and a t-Treatment variable. This
t-variable may be either binary {1 = "new" vs 0 = "control"} or a numeric measure of
Exposure level. LC Strategy starts by CLUSTERING experimental units (patients) on
baseline x-Covariates, forming mutually exclusive and exhaustive BLOCKS of relatively
well-matched units. The implicit statistical model for LC is thus simple one-way ANOVA.
The Within-Block measures of effect-size are Local Rank Correlations (LRCs) when Exposure
is numeric with more than two levels. Otherwise, Treatment choice is Nested within
BLOCKS, and effect-sizes are Local Treatment Differences (LTDs) between within-cluster
y-Outcome Means ["new" minus "control"]. An Instrumental Variable (IV) method is also
provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute
information for effect-size inferences ...assuming that x-Covariates influence only
Treatment choice or Exposure level and otherwise have no direct effects on y-Outcome.
License: GPL-3
URL: https://www.R-project.org, http://localcontrolstatistics.org
NeedsCompilation: no
Packaged: 2018-09-29 21:17:55 UTC; wiz
Repository: CRAN
Date/Publication: 2018-10-01 04:20:03 UTC