SPSoutco                package:USPS                R Documentation

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

     Examine Within-Bin Treatment Differences on an Outcome Measure and
     Average these Differences across Bins.

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

       outobj <- SPSoutco(dframe, trtm, qbin, yvar, faclev=3)

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

  dframe: Name of augmented data.frame written to the appn="" argument
          of SPSlogit().

    trtm: Name of treatment factor variable.

    qbin: Name of variable containing the PS bin number for each
          patient.

    yvar: Name of an outcome Y variable.

  faclev: Maximum number of different numerical values an X-covariate
          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.

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

     Once the second phase of Supervised Propensity Scoring confirms,
     using SPSbalan(), that X-covariate Distributions have been
     Balanced Within-Bins, the third phase can start: Examining
     Within-Bin Outcome Difference due to Treatment and Averaging these
     Differences across Bins. Graphical displays of SPSoutco() results
     feature R barplot() invocations.

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

     An output list object of class SPSoutco: 

  dframe: Name of augmented data.frame written to the appn="" argument
          of SPSlogit().

    trtm: Name of the two-level treatment factor variable.

    yvar: Name of an outcome Y variable.

    bins: Number of variable containing bin numbers.

  PStdif: Character string describing the treatment difference.

 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.

 binvars: Unadjusted variance by cluster and treatment.

 binfreq: Number of patients by bin and treatment.

  awbdif: Across cluster average difference with cluster size weights.

  awbsde: Standard error of awbdif.

  wwbdif: Across cluster average difference, inverse variance weights.

  wwbsde: Standard error of wwbdif.

    form: Formula for overall, marginal treatment difference on
          X-covariate.

  faclev: Maximum number of different numerical values an X-covariate
          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 => only next six outputs; "factor" => only last
          four outputs.

 aovdiff: ANOVA output for marginal test.

   form2: Formula for differences in X due to bins and to treatment
          nested within bins.

 bindiff: ANOVA summary for treatment nested within bin.

 pbindif: Unadjusted treatment difference by cluster.

 pbinsde: Standard error of the unadjusted difference by cluster.

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

  factab: Marginal table of counts by Y-factor level and treatment.

     tab: Three-way table of counts by Y-factor level, treatment and
          bin.

  cumchi: Cumulative Chi-Square statistic for interaction in the
          three-way, nested table.

   cumdf: Degrees of-Freedom for the Cumulative Chi-Squared.

_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.

     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:

     'SPSlogit', 'SPSbalan' and 'SPSnbins'.

_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")

       SPSlifeo <- SPSoutco(lindSPS, abcix, PSbin, lifepres, faclev=1)
       SPSlifeo
       plot(SPSlifeo)    

