SPSlogit                package:USPS                R Documentation

_P_r_o_p_e_n_s_i_t_y _S_c_o_r_e _p_r_e_d_i_c_t_i_o_n _o_f _T_r_e_a_t_m_e_n_t _S_e_l_e_c_t_i_o_n _f_r_o_m _P_a_t_i_e_n_t _B_a_s_e_l_i_n_e _X-_c_o_v_a_r_i_a_t_e_s

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

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

_A_r_g_u_m_e_n_t_s:

  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.

_D_e_t_a_i_l_s:

     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.

_V_a_l_u_e:

     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().

_A_u_t_h_o_r(_s):

     Bob Obenchain <sunsetstats@earthlink.net>

_R_e_f_e_r_e_n_c_e_s:

     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. (2005) *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.

_S_e_e _A_l_s_o:

     'SPSbalan', 'SPSnbins' and 'SPSoutco'.

_E_x_a_m_p_l_e_s:

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

