SPSoutco {USPS}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

  outobj <- SPSoutco(dframe, trtm, qbin, yvar, faclev=3)

Arguments

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. (2009) USPSinR.pdf ../R_HOME/library/USPS 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.

Examples

  data(lindner)
  PStreat <- abcix~stent+height+female+diabetic+acutemi+ejecfrac+ves1proc
  logtSPS <- SPSlogit(lindner, PStreat, PSfit, PSrnk, PSbin, appn="lindSPS")

  SPSlifeo <- SPSoutco(lindSPS, abcix, PSbin, lifepres, faclev=1)
  SPSlifeo
  plot(SPSlifeo)    

[Package USPS version 1.2-0 Index]