UPSivadj                package:USPS                R Documentation

_I_n_s_t_r_u_m_e_n_t_a_l _V_a_r_i_a_b_l_e _L_A_T_E _L_i_n_e_a_r _F_i_t_t_i_n_g _i_n _U_n_s_u_p_e_r_v_i_s_e_d _P_r_o_p_e_n_s_i_y _S_c_o_r_i_n_g

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

     For a given number of patient clusters in baseline X-covariate
     space and a specified Y-outcome variable, linearly smooth the
     distribution of Local Average Treatment Effects (LATEs) plotted
     versus Within-Cluster Treatment Selection (PS) Percentages.

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

       ivobj <- UPSivadj(numclust)

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

numclust: Number of clusters in baseline X-covariate space.

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

     Multiple calls to UPSivadj(n) for varying numbers of clusters n
     are made after first invoking UPShclus() to hierarchically cluster
     patients in X-space and then invoking UPSaccum() to specify a Y
     outcome variable and a two-level treatment factor t.  UPSivadj(n)
     linearly smoothes the LATE distribution when plotted versus within
     cluster propensity score percentages.

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

     An output list object of class UPSivadj: 

  hiclus: Name of clustering object created by UPShclus().

  dframe: Name of data.frame containing X, t & Y variables.

    trtm: Name of treatment factor variable.

    yvar: Name of outcome Y variable.

numclust: Number of clusters requested.

actclust: Number of clusters actually produced.

  scedas: Scedasticity assumption: "homo" or "hete"

  PStdif: Character string describing the treatment difference.

ivhbindf: Vector containing cluster number for each patient. 

 rawmean: Unadjusted outcome mean by treatment group.

 rawvars: Unadjusted outcome variance by treatment group.

 rawfreq: Number of patients by treatment group.

  ratdif: Unadjusted mean outcome difference between treatments.

  ratsde: Standard error of unadjusted mean treatment difference.

 binmean: Unadjusted mean outcome by cluster and treatment.

 binfreq: Number of patients by bin and treatment.

  faclev: Maximum number of different numerical values an outcome
          variable can assume without automatically being converted
          into a "factor" variable; faclev=1 causes a binary indicator
          to be treated as a continuous variable determining an average
          or proportion.

 youtype: "contin"uous => next eleven outputs; "factor" => no
          additional output items.

 pbinout: LATE regardless of treatment by cluster.

 pbinpsp: Within-Cluster Treatment Percentage = non-parametric
          Propensity Score.

 pbinsiz: Cluster radii measure: square root of total number of
          patients.

  symsiz: Symbol size of largest possible Snowball in a UPSivadj() plot
          with 1 cluster.

   ivfit: lm() output for linear smooth across clusters.

 ivtzero: Predicted outcome at PS percentage zero.

 ivtxsde: Standard deviation of outcome prediction at PS percentage
          zero.

 ivtdiff: Predicted outcome difference for PS percentage 100 minus that
          at zero.

 ivtdsde: Standard deviation of outcome difference.

 ivt100p: Predicted outcome at PS percentage 100.

 ivt1pse: Standard deviation of outcome prediction at PS percentage
          100.

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

     Bob Obenchain <sunsetstats@earthlink.net>

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

     Imbens GW, Angrist JD. (1994) Identification and Estimation  of
     Local Average Treatment Effects (LATEs). _Econometrica_ *62*:
     467-475.

     Obenchain RL. (2004) Unsupervised Propensity Scoring: NN and IV
     Plots. _Proceedings of the American Statistical Association (on
     CD)_ 8 pages.

     Obenchain RL. (2005) *USPSinR.pdf*  ../R_HOME/library/USPS  40
     pages.

     McClellan M, McNeil BJ, Newhouse JP. (1994) Does More Intensive
     Treatment of Myocardial Infarction in the Elderly Reduce
     Mortality?: Analysis Using Instrumental Variables. _JAMA_ *272*:
     859-866.

     Rosenbaum PR, Rubin RB. (1983) The Central Role of the Propensity
     Score in Observational Studies for Causal Effects. _Biometrika_
     *70*: 41-55.

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

     'UPSnnltd', 'UPSaccum' and 'UPSgraph'.

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

       data(lindner)
       UPSxvars <- c("stent", "height", "female", "diabetic", "acutemi", "ejecfrac", "ves1proc")
       UPSharch <- UPShclus(lindner, UPSxvars)
       UPSaccum(UPSharch, lindner, abcix, lifepres, faclev=1, scedas="homo", accobj="ABClife")

       lif100iv <- UPSivadj(100)
       lif100iv
       plot(lif100iv)

