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:

• hiclusName of clustering object created by UPShclus().

• dframeName of data.frame containing X, t & Y variables.

• trtmName of treatment factor variable.

• yvarName of outcome Y variable.

• numclustNumber of clusters requested.

• actclustNumber of clusters actually produced.

• scedasScedasticity assumption: "homo" or "hete"

• PStdifCharacter string describing the treatment difference.

• ivhbindfVector containing cluster number for each patient.

• rawmeanUnadjusted outcome mean by treatment group.

• rawvarsUnadjusted outcome variance by treatment group.

• rawfreqNumber of patients by treatment group.

• ratdifUnadjusted mean outcome difference between treatments.

• ratsdeStandard error of unadjusted mean treatment difference.

• binmeanUnadjusted mean outcome by cluster and treatment.

• binfreqNumber of patients by bin and treatment.

• faclevMaximum 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.

• pbinoutLATE regardless of treatment by cluster.

• pbinpspWithin-Cluster Treatment Percentage = non-parametric Propensity Score.

• pbinsizCluster radii measure: square root of total number of patients.

• symsizSymbol size of largest possible Snowball in a UPSivadj() plot with 1 cluster.

• ivfitlm() output for linear smooth across clusters.

• ivtzeroPredicted outcome at PS percentage zero.

• ivtxsdeStandard deviation of outcome prediction at PS percentage zero.

• ivtdiffPredicted outcome difference for PS percentage 100 minus that at zero.

• ivtdsdeStandard deviation of outcome difference.

• ivt100pPredicted outcome at PS percentage 100.

• ivt1pseStandard 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 May 21, 2022, 1:05 a.m.