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

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

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 <[e-mail]>

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 Feb. 11, 2024, 9:14 a.m.