Description Usage Arguments Details Value Author(s) References See Also

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

1 | ```
SPSlogit(envir, dframe, form, pfit, prnk, qbin, bins = 5, appn = "")
``` |

`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. |

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.

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().

Bob Obenchain <[email protected]>

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.

`SPSbalan`

, `SPSnbins`

and `SPSoutco`

.

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