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:
- 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 <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.
See Also
SPSlogit
, SPSbalan
and SPSnbins
.