UPSivadj: 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)

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.

Arguments

envir: name of the working local control classic environment.
numclust: Number of clusters in baseline X-covariate space.

Author

Bob Obenchain <wizbob@att.net>

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.

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.