SPSlogit {USPS}R Documentation

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

  SPSlobj <- SPSlogit(dframe, form, pfit, prnk, qbin, bins=5, appn="")

Arguments

dframe Name of 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:

dframe Name of input data.frame containing X, t & Y variables.
dfoutnam Name of output data.frame augmented by pfit, prank and qbin variables.
trtm Name of two-level treatment factor variable.
form glm() formula for logistic regression.
pfit Name of predicted PS variable.
prank Name of variable containing PS tied-ranks.
qbin Name of variable containing assigned PS bin number for each patient.
bins Number of adjacent PS bins desired.
glmobj Output object from invocation of glm() with family = binomial().

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.

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. (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

SPSbalan, SPSnbins and SPSoutco.

Examples

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

[Package USPS version 1.2-0 Index]