UPSivadj {USPS} | R Documentation |
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.
ivobj <- UPSivadj(numclust)
numclust |
Number of clusters in baseline X-covariate space. |
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.
An output list object of class UPSivadj:
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. |
ivhbindf |
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. |
binfreq |
Number of patients by bin and treatment. |
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 => next eleven outputs; "factor" => no additional output items. |
pbinout |
LATE regardless of treatment by cluster. |
pbinpsp |
Within-Cluster Treatment Percentage = non-parametric Propensity Score. |
pbinsiz |
Cluster radii measure: square root of total number of patients. |
symsiz |
Symbol size of largest possible Snowball in a UPSivadj() plot with 1 cluster. |
ivfit |
lm() output for linear smooth across clusters. |
ivtzero |
Predicted outcome at PS percentage zero. |
ivtxsde |
Standard deviation of outcome prediction at PS percentage zero. |
ivtdiff |
Predicted outcome difference for PS percentage 100 minus that at zero. |
ivtdsde |
Standard deviation of outcome difference. |
ivt100p |
Predicted outcome at PS percentage 100. |
ivt1pse |
Standard deviation of outcome prediction at PS percentage 100. |
Bob Obenchain <wizbob@att.net>
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. (2009) USPSinR.pdf ../R_HOME/library/USPS 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.
UPSnnltd
, UPSaccum
and UPSgraph
.
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") lif100iv <- UPSivadj(100) lif100iv plot(lif100iv)