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.
UPSnnltd(envir, numclust)An output list object of class UPSnnltd:
Name of clustering object created by UPShclus().
Name of data.frame containing X, t & Y variables.
Name of treatment factor variable.
Name of outcome Y variable.
Number of clusters requested.
Number of clusters actually produced.
Scedasticity assumption: "homo" or "hete"
Character string describing the treatment difference.
Vector containing cluster number for each patient.
Unadjusted outcome mean by treatment group.
Unadjusted outcome variance by treatment group.
Number of patients by treatment group.
Unadjusted mean outcome difference between treatments.
Standard error of unadjusted mean treatment difference.
Unadjusted mean outcome by cluster and treatment.
Unadjusted variance by cluster and treatment.
Number of patients by bin and treatment.
Across cluster average difference with cluster size weights.
Standard error of awbdif.
Across cluster average difference, inverse variance weights.
Standard error of wwbdif.
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.
"contin"uous => only next eight outputs; "factor" => only last three outputs.
ANOVA summary for treatment main effect only.
Formula for outcome differences due to bins and to treatment nested within bins.
ANOVA summary for treatment nested within cluster.
Estimate of error mean square in nested model.
Unadjusted treatment difference by cluster.
Standard error of the unadjusted difference by cluster.
Cluster radii measure: square root of total number of patients.
Symbol size of largest possible Snowball in a UPSnnltd() plot with 1 cluster.
Marginal table of counts by Y-factor level and treatment.
Cumulative Chi-Square statistic for interaction in the three-way, nested table.
Degrees of-Freedom for the Cumulative Chi-Squared.
name of the working local control classic environment.
Number of clusters in baseline X-covariate space.
Bob Obenchain <wizbob@att.net>
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.
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.
UPSivadj, UPSaccum and UPSgraph.