For a given number of patient clusters in baseline X-covariate space and a specified Y-outcome variable, linearly smooth the distribution of Local Average Treatment Effects (LATEs) plotted versus Within-Cluster Treatment Selection (PS) Percentages.
UPSivadj(envir, numclust)name of the working local control classic environment.
Number of clusters in baseline X-covariate space.
An output list object of class UPSivadj:
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.
ivhbindfVector 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.
binfreqNumber of patients by bin and treatment.
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 => next eleven outputs; "factor" => no additional output items.
pbinoutLATE regardless of treatment by cluster.
pbinpspWithin-Cluster Treatment Percentage = non-parametric Propensity Score.
pbinsizCluster radii measure: square root of total number of patients.
symsizSymbol size of largest possible Snowball in a UPSivadj() plot with 1 cluster.
ivfitlm() output for linear smooth across clusters.
ivtzeroPredicted outcome at PS percentage zero.
ivtxsdeStandard deviation of outcome prediction at PS percentage zero.
ivtdiffPredicted outcome difference for PS percentage 100 minus that at zero.
ivtdsdeStandard deviation of outcome difference.
ivt100pPredicted outcome at PS percentage 100.
ivt1pseStandard deviation of outcome prediction at PS percentage 100.
Multiple calls to UPSivadj(n) for varying numbers of clusters n are 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. UPSivadj(n) linearly smoothes the LATE distribution when plotted versus within cluster propensity score percentages.
Imbens GW, Angrist JD. (1994) Identification and Estimation of Local Average Treatment Effects (LATEs). Econometrica 62: 467-475.
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.-
McClellan M, McNeil BJ, Newhouse JP. (1994) Does More Intensive Treatment of Myocardial Infarction in the Elderly Reduce Mortality?: Analysis Using Instrumental Variables. JAMA 272: 859-866.
Rosenbaum PR, Rubin RB. (1983) The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika 70: 41-55.