Examine Within-Bin Treatment Differences on an Outcome Measure and Average these Differences across Bins.
SPSoutco(envir, dframe, trtm, qbin, yvar, faclev = 3)An output list object of class SPSoutco:
Name of augmented data.frame written to the appn="" argument of SPSlogit().
Name of the two-level treatment factor variable.
Name of an outcome Y variable.
Number of variable containing bin numbers.
Character string describing the treatment difference.
Unadjusted outcome mean by treatment group.
Unadjusted outcome variance by treatment group.
Number of patients by treatment group.
Unadjusted mean outcome difference between treatments.
Standard error of unadjusted mean treatment difference.
Unadjusted mean outcome by cluster and treatment.
Unadjusted variance by cluster and treatment.
Number of patients by bin and treatment.
Across cluster average difference with cluster size weights.
Standard error of awbdif.
Across cluster average difference, inverse variance weights.
Standard error of wwbdif.
Formula for overall, marginal treatment difference on X-covariate.
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.
"contin"uous => only next six outputs; "factor" => only last four outputs.
ANOVA output for marginal test.
Formula for differences in X due to bins and to treatment nested within bins.
ANOVA summary for treatment nested within bin.
Unadjusted treatment difference by cluster.
Standard error of the unadjusted difference by cluster.
Cluster radii measure: square root of total number of patients.
Marginal table of counts by Y-factor level and treatment.
Three-way table of counts by Y-factor level, treatment and bin.
Cumulative Chi-Square statistic for interaction in the three-way, nested table.
Degrees of-Freedom for the Cumulative Chi-Squared.
name of the working local control classic environment.
Name of augmented data.frame written to the appn="" argument of SPSlogit().
Name of treatment factor variable.
Name of variable containing the PS bin number for each patient.
Name of an outcome Y variable.
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.
Bob Obenchain <wizbob@att.net>
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.
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.
SPSlogit, SPSbalan and SPSnbins.