UPSivadj {USPS}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

  ivobj <- UPSivadj(numclust)

Arguments

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. (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.

See Also

UPSnnltd, UPSaccum and UPSgraph.

Examples

  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)

[Package USPS version 1.2-0 Index]