SPSlogit {USPS} | R Documentation |
Use a logistic regression model to predict Treatment Selection from Patient Baseline X-covariates in Supervised Propensity Scoring.
SPSlobj <- SPSlogit(dframe, form, pfit, prnk, qbin, bins=5, appn="")
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. |
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:
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(). |
Bob Obenchain <wizbob@att.net>
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.
SPSbalan
, SPSnbins
and SPSoutco
.
data(lindner) PStreat <- abcix~stent+height+female+diabetic+acutemi+ejecfrac+ves1proc logtSPS <- SPSlogit(lindner, PStreat, PSfit, PSrnk, PSbin, appn="lindSPS") logtSPS