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

In 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:

dframe

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

trtm

Name of the two-level treatment factor variable.

yvar

Name of an outcome Y variable.

bins

Number of variable containing bin numbers.

PStdif

Character string describing the treatment difference.

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.

binvars

Unadjusted variance by cluster and treatment.

binfreq

Number of patients by bin and treatment.

awbdif

Across cluster average difference with cluster size weights.

awbsde

Standard error of awbdif.

wwbdif

Across cluster average difference, inverse variance weights.

wwbsde

Standard error of wwbdif.

form

Formula for overall, marginal treatment difference on X-covariate.

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.

youtype

"contin"uous => only next six outputs; "factor" => only last four outputs.

aovdiff

ANOVA output for marginal test.

form2

Formula for differences in X due to bins and to treatment nested within bins.

bindiff

ANOVA summary for treatment nested within bin.

pbindif

Unadjusted treatment difference by cluster.

pbinsde

Standard error of the unadjusted difference by cluster.

pbinsiz

Cluster radii measure: square root of total number of patients.

factab

Marginal table of counts by Y-factor level and treatment.

tab

Three-way table of counts by Y-factor level, treatment and bin.

cumchi

Cumulative Chi-Square statistic for interaction in the three-way, nested table.

cumdf

Degrees 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.

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