SPSbalan {USPS}R Documentation

Test for Within-Bin X-covariate Balance in Supervised Propensiy Scoring

Description

Test for Conditional Independence of X-covariate Distributions from Treatment Selection within Given, Adjacent PS Bins.

Usage

  xcvobj <- SPSbalan(dframe, trtm, qbin, xvar, faclev=3)

Arguments

dframe Name of augmented data.frame written to the appn="" argument of SPSlogit().
trtm Name of the two-level treatment factor variable.
qbin Name of variable containing bin numbers.
xvar Name of one baseline covariate X variable used in the SPSlogit() PS model.
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 a proportion.

Details

The second step in Supervised Propensity Scoring analyses is to verify that baseline X-covariates have the same distribution, regardless of treatment, within each fitted PS bin.

Value

An output list object of class SPSbalan.
"contin"uous xvar => only the following 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 output for the nested within bin model.
df3 Output data.frame containing 3 variables: X-covariate, treatment and bin.
factab Marginal table of counts by X-factor level and treatment.
tab Three-way table of counts by X-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, SPSnbins and SPSoutco.

Examples

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

  SPSbalvs <- SPSbalan(lindSPS, abcix, PSbin, ves1proc)
  SPSbalvs
  plot(SPSbalvs)

[Package USPS version 1.2-0 Index]