SPSlogit: Propensity Score prediction of Treatment Selection from Patient Baseline X-covariates

Description

Use a logistic regression model to predict Treatment Selection from Patient Baseline X-covariates in Supervised Propensity Scoring.

Usage

SPSlogit(envir, dframe, form, pfit, prnk, qbin, bins = 5, appn = "")

Arguments

envir

name of the working local control classic environment.

dframe

data.frame containing X, t and Y variables.

form

Valid formula for glm()with family = binomial(), with the two-level treatment factor variable as the left-hand-side of the formula.

pfit

Name of variable to store PS predictions.

prnk

Name of variable to store tied-ranks of PS predictions.

qbin

Name of variable to store the assigned bin number for each patient.

bins

optional; number of adjacent PS bins desired; default to 5.

appn

optional; append the pfit, prank and qbin variables to the input dfname when appn=="", else save augmented data.frame to name specified within a non-blank appn string.

Value

An output list object of class SPSlogit:

dframeName of input data.frame containing X, t & Y variables.
dfoutnamName of output data.frame augmented by pfit, prank and qbin variables.
trtmName of two-level treatment factor variable.
formglm() formula for logistic regression.
pfitName of predicted PS variable.
prankName of variable containing PS tied-ranks.
qbinName of variable containing assigned PS bin number for each patient.
binsNumber of adjacent PS bins desired.
glmobjOutput object from invocation of glm() with family = binomial().

Details

The first phase of Supervised Propensity Scoring is to develop a logit (or probit) model predicting treatment choice from patient baseline X characteristics. SPSlogit uses a call to glm()with family = binomial() to fit a logistic regression.

References

Cochran WG. (1968) The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 24: 205-213.

Kereiakes DJ, Obenchain RL, Barber BL, et al. (2000) Abciximab provides cost effective survival advantage in high volume interventional practice. Am Heart J 140: 603-610.

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.