reveal.data

An output object resulting from a call to ltdagg() or lrcagg().

Quoted NAME for the Cluster-Number variable.

clus.var

Quoted NAME for the LTD/LRC effect-size variable.

effe.var

reveal.data() forms a data.frame by sorting and appending the LTD or LRC treatment
 effect-size measures from ltdagg() or lrcagg() -- as well as a Cluster membership-number
 variable -- to a copy of the data.frame specified in LCsetup(). In the fourth and final REVEAL
 Phase of Local Control Strategy, a stretch-goal is to predict variation in LTD/LRC effect-size
 distributions using the known (baseline) X-covariate characteristics of experimental units. For
 example, the data.frame output by reveal.data() is suitable for input to party::ctree() as well
 as to a number of other "less Visual" prediction methods available in R.

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.

reveal.data: Create a data.frame for use in Prediction of a LTD/LRC effect-size Distribution

Description

Usage

Arguments

Value

References

See Also