R: Nearest Neighbor Distribution of LTDs in Unsupervised...

UPSnnltd {LocalControl}

R Documentation

Nearest Neighbor Distribution of LTDs in Unsupervised Propensiy Scoring

Description

For a given number of patient clusters in baseline X-covariate space, UPSnnltd() characterizes the distribution of Nearest Neighbor "Local Treatemnt Differences" (LTDs) on a specified Y-outcome variable.

Usage

UPSnnltd(envir, numclust)

Arguments

`envir`	name of the working local control classic environment.
`numclust`	Number of clusters in baseline X-covariate space.

Details

Multiple calls to UPSnnltd(n) for varying numbers of clusters, n, are typically made after first invoking UPShclus() to hierarchically cluster patients in X-space and then invoking UPSaccum() to specify a Y outcome variable and a two-level treatment factor t. UPSnnltd(n) then determines the LTD Distribution corresponding to n clusters and, optionally, displays this distribution in a "Snowball" plot.

Value

An output list object of class UPSnnltd:

hiclusName of clustering object created by UPShclus().
dframeName of data.frame containing X, t & Y variables.
trtmName of treatment factor variable.
yvarName of outcome Y variable.
numclustNumber of clusters requested.
actclustNumber of clusters actually produced.
scedasScedasticity assumption: "homo" or "hete"
PStdifCharacter string describing the treatment difference.
nnhbindfVector containing cluster number for each patient.
rawmeanUnadjusted outcome mean by treatment group.
rawvarsUnadjusted outcome variance by treatment group.
rawfreqNumber of patients by treatment group.
ratdifUnadjusted mean outcome difference between treatments.
ratsdeStandard error of unadjusted mean treatment difference.
binmeanUnadjusted mean outcome by cluster and treatment.
binvarsUnadjusted variance by cluster and treatment.
binfreqNumber of patients by bin and treatment.
awbdifAcross cluster average difference with cluster size weights.
awbsdeStandard error of awbdif.
wwbdifAcross cluster average difference, inverse variance weights.
wwbsdeStandard error of wwbdif.
faclevMaximum number of different numerical values an outcome variable can assume without automatically being converted into a "factor" variable; faclev=1 causes a binary indicator to be treated as a continuous variable determining an average or proportion.
youtype"contin"uous => only next eight outputs; "factor" => only last three outputs.
aovdiffANOVA summary for treatment main effect only.
form2Formula for outcome differences due to bins and to treatment nested within bins.
bindiffANOVA summary for treatment nested within cluster.
sig2Estimate of error mean square in nested model.
pbindifUnadjusted treatment difference by cluster.
pbinsdeStandard error of the unadjusted difference by cluster.
pbinsizCluster radii measure: square root of total number of patients.
symsizSymbol size of largest possible Snowball in a UPSnnltd() plot with 1 cluster.
factabMarginal table of counts by Y-factor level and treatment.
cumchiCumulative Chi-Square statistic for interaction in the three-way, nested table.
cumdfDegrees of-Freedom for the Cumulative Chi-Squared.

Author(s)

Bob Obenchain <wizbob@att.net>

References

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.

Rosenbaum PR, Rubin RB. (1983) The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika 70: 41–55.

Rubin DB. (1980) Bias reduction using Mahalanobis metric matching. Biometrics 36: 293-298.