SPSlogit: Propensity Score prediction of Treatment Selection from...

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

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.

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.

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().

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.

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.

See Also

SPSbalan, SPSnbins and SPSoutco.

LocalControl documentation built on May 21, 2022, 1:05 a.m.