UPSivadj: Instrumental Variable LATE Linear Fitting in Unsupervised...

In LocalControl: Nonparametric Methods for Generating High Quality Comparative Effectiveness Evidence

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 <[e-mail]>

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.

LocalControl documentation built on Sept. 11, 2024, 7 p.m.