plot.ltdagg

An object output by ltdagg() for K = Number of Clusters in baseline X-covariate space.

R environment output by a previous call to LCsetup().

envir

Choice of "all", "seq", "hist", "boxp", or "ecdf".

show

Parameter setting for hist(); May be an integer value ...like 25 or 50.

breaks

Other arguments passed on to plot().

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Display a Histogram, Box-Whisker Diagram and/or empirical Cumulative Distribution
 Function depicting the Observed Local Treatment Difference (LTD) Distribution across K Clusters.

methods

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.

plot.ltdagg: Display Visualizations of an Observed LTD Distribution in Local Control Strategy

Description

Usage

Arguments

Value

See Also