SPSbalan: Test for Within-Bin X-covariate Balance in Supervised...

In OHDSI/LocalControl: Nonparametric Methods for Generating High Quality Comparative Effectiveness Evidence

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. 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.

Usage

SPSbalan(envir, dframe, trtm, yvar, qbin, xvar, faclev = 3)

Arguments

envir	The local control environment
dframe	Name of augmented data.frame written to the appn="" argument of SPSlogit().
trtm	Name of the two-level treatment factor variable.
yvar	The outcome 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.

Value

An output list object of class SPSbalan. The first four are returned with a continuous x-variable. The next 4 are used if it is a factor variable.

• aovdiffANOVA output for marginal test.

• form2Formula for differences in X due to bins and to treatment nested within bins.

• bindiffANOVA output for the nested within bin model.

• df3Output data.frame containing 3 variables: X-covariate, treatment and bin.

• factabMarginal table of counts by X-factor level and treatment.

• tabThree-way table of counts by X-factor level, treatment and bin.

• cumchiCumulative Chi-Square statistic for interaction in the three-way, nested table.

• cumdfDegrees of-Freedom for the Cumulative Chi-Squared.

Author(s)

Bob Obenchain <[e-mail]>

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.

OHDSI/LocalControl documentation built on Feb. 11, 2024, 9:14 a.m.