UPSivadj {LocalControl} | R Documentation |
Instrumental Variable LATE Linear Fitting in Unsupervised Propensiy Scoring
Description
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.
Usage
UPSivadj(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 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.
Value
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.
Author(s)
Bob Obenchain <wizbob@att.net>
References
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.
See Also
UPSnnltd
, UPSaccum
and UPSgraph
.