UPSnnltd {USPS}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

  nnobj <- UPSnnltd(numclust)

Arguments

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:

hiclus Name of clustering object created by UPShclus().
dframe Name of data.frame containing X, t & Y variables.
trtm Name of treatment factor variable.
yvar Name of outcome Y variable.
numclust Number of clusters requested.
actclust Number of clusters actually produced.
scedas Scedasticity assumption: "homo" or "hete"
PStdif Character string describing the treatment difference.
nnhbindf Vector containing cluster number for each patient.
rawmean Unadjusted outcome mean by treatment group.
rawvars Unadjusted outcome variance by treatment group.
rawfreq Number of patients by treatment group.
ratdif Unadjusted mean outcome difference between treatments.
ratsde Standard error of unadjusted mean treatment difference.
binmean Unadjusted mean outcome by cluster and treatment.
binvars Unadjusted variance by cluster and treatment.
binfreq Number of patients by bin and treatment.
awbdif Across cluster average difference with cluster size weights.
awbsde Standard error of awbdif.
wwbdif Across cluster average difference, inverse variance weights.
wwbsde Standard error of wwbdif.
faclev Maximum 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.
aovdiff ANOVA summary for treatment main effect only.
form2 Formula for outcome differences due to bins and to treatment nested within bins.
bindiff ANOVA summary for treatment nested within cluster.
sig2 Estimate of error mean square in nested model.
pbindif Unadjusted treatment difference by cluster.
pbinsde Standard error of the unadjusted difference by cluster.
pbinsiz Cluster radii measure: square root of total number of patients.
symsiz Symbol size of largest possible Snowball in a UPSnnltd() plot with 1 cluster.
factab Marginal table of counts by Y-factor level and treatment.
cumchi Cumulative Chi-Square statistic for interaction in the three-way, nested table.
cumdf Degrees 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. (2009) USPSinR.pdf ../R_HOME/library/USPS 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.

See Also

UPSivadj, UPSaccum and UPSgraph.

Examples

    data(lindner)
    UPSxvars <- c("stent", "height", "female", "diabetic", "acutemi", "ejecfrac", "ves1proc")
    UPSharch <- UPShclus(lindner, UPSxvars)
    UPSaccum(UPSharch, lindner, abcix, lifepres, faclev=1, scedas="homo", accobj="ABClife")
    
    lif070nn <- UPSnnltd(70)
    lif070nn
    plot(lif070nn)

[Package USPS version 1.2-0 Index]