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

SPSoutco {LocalControl}

R Documentation

Examine Treatment Differences on an Outcome Measure in Supervised Propensiy Scoring

Description

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

Usage

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:

dframeName of augmented data.frame written to the appn="" argument of SPSlogit().
trtmName of the two-level treatment factor variable.
yvarName of an outcome Y variable.
binsNumber of variable containing bin numbers.
PStdifCharacter string describing the treatment difference.
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.
binvarsUnadjusted variance by cluster and treatment.
binfreqNumber of patients by bin and treatment.
awbdifAcross cluster average difference with cluster size weights.
awbsdeStandard error of awbdif.
wwbdifAcross cluster average difference, inverse variance weights.
wwbsdeStandard error of wwbdif.
formFormula for overall, marginal treatment difference on X-covariate.
faclevMaximum 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.
youtype"contin"uous => only next six outputs; "factor" => only last four outputs.
aovdiffANOVA output for marginal test.
form2Formula for differences in X due to bins and to treatment nested within bins.
bindiffANOVA summary for treatment nested within bin.
pbindifUnadjusted treatment difference by cluster.
pbinsdeStandard error of the unadjusted difference by cluster.
pbinsizCluster radii measure: square root of total number of patients.
factabMarginal table of counts by Y-factor level and treatment.
tabThree-way table of counts by Y-factor level, treatment and bin.
cumchiCumulative Chi-Square statistic for interaction in the three-way, nested table.
cumdfDegrees of-Freedom for the Cumulative Chi-Squared.

Author(s)

Bob Obenchain <wizbob@att.net>

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.