SPSoutco: Examine Treatment Differences on an Outcome Measure in...

Description Usage Arguments Details Value Author(s) References See Also

Description

Examine Within-Bin Treatment Differences on an Outcome Measure and Average these Differences across Bins.

Usage

1
SPSoutco(envir, dframe, trtm, qbin, yvar, faclev = 3)

Arguments

envir

name of the working local control classic environment.

dframe

Name of augmented data.frame written to the appn="" argument of SPSlogit().

trtm

Name of treatment factor variable.

qbin

Name of variable containing the PS bin number for each patient.

yvar

Name of an outcome Y variable.

faclev

Maximum number of different numerical values an X-covariate 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.

Details

Once the second phase of Supervised Propensity Scoring confirms, using SPSbalan(), that X-covariate Distributions have been Balanced Within-Bins, the third phase can start: Examining Within-Bin Outcome Difference due to Treatment and Averaging these Differences across Bins. Graphical displays of SPSoutco() results feature R barplot() invocations.

Value

An output list object of class SPSoutco:

Author(s)

Bob Obenchain <[email protected]>

References

Cochran WG. (1968) The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 24: 205-213.

Obenchain RL. (2011) USPSinR.pdf USPS R-package vignette, 40 pages.

Rosenbaum PR, Rubin RB. (1983) The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika 70: 41-55.

Rosenbaum PR, Rubin DB. (1984) Reducing Bias in Observational Studies Using Subclassification on a Propensity Score. J Amer Stat Assoc 79: 516-524.

See Also

SPSlogit, SPSbalan and SPSnbins.


OHDSI/LocalControl documentation built on July 13, 2018, 3:21 p.m.