SPSoutco {USPS} | R Documentation |
Examine Within-Bin Treatment Differences on an Outcome Measure and Average these Differences across Bins.
outobj <- SPSoutco(dframe, trtm, qbin, yvar, faclev=3)
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. |
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.
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. |
Bob Obenchain <wizbob@att.net>
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.
SPSlogit
, SPSbalan
and SPSnbins
.
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)