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:
- 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. (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.
See Also
UPSivadj
, UPSaccum
and UPSgraph
.