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help for stepp_tail, stepp_window, stepp_plot                   Patrick Royston
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Subpopulation treatment effect pattern plot (STEPP)

        stepp_tail regression_cmd [yvar] zvar [adjvars] [if] [in] , options

        stepp_window regression_cmd [yvar] zvar [adjvars] [if] [in] , options

        stepp_plot stubname [, options]

    where

        regression_cmd may be clogit, cnreg, glm, intreg, logistic, logit, 
        mlogit, nbreg, ologit, oprobit, poisson, probit, qreg, regress, 
        stcox, streg, or xtgee.


    options                       Description
    -------------------------------------------------------------------------
      gen(stubname)               creates five new variables containing
                                    results of STEPP analysis
      g(#)                        (stepp_tail only) defines the number of
                                    subpopulation groups
      n1(#)                       (stepp_window only) defines the number of
                                    individuals belonging only to one of two
                                    neighbouring subpopulations
      n2(#)                       (stepp_window only) defines the number of
                                    individuals in a subpopulation
      treatment(trt_varlist)      defines the list of variables whose
                                    interactions with zvar are to be studied
      regression_cmd_options      options for regression_cmd

    options for stepp_plot
      vn(#)                       variable number in treatment()
      plot(plot)                  adds other plots to the generated graph
      graph_options               options of graph twoway
    -------------------------------------------------------------------------


    All weight types supported by regression_cmd are allowed; see weight.

    yvar is not allowed for streg and stcox. For these commands, you must
        first stset your data.


Description

    ststep_tail and ststep_window compute Bonetti & Gelber (2000, 2004)'s
    STEPP estimators for graphical exploration of a treatment/covariate
    interaction.  ststep_tail provides the tail-oriented estimator, and
    ststep_window the sliding-window estimator. Plotting the results may be
    done by using stepp_plot, in which case stubname is the same as in the
    gen(stubname) option of stepp_tail and stepp_window.

    zvar is the continuous covariate whose interaction with treatment is to
    be studied, and adjvars is a list of other covariates used to linearly
    adjust each model fitted to the treatment variable(s) defined by
    treatment().


Options

    Options for stepp_tail and stepp_window:

    gen(stubname) creates five new variables called stubnameb, stubnamese,
        stubnamemean, stubnamelb, stubnameub.  stubnameb is the estimated
        regression coefficient in each subpopulation, stubnamese is its
        standard error, stubnamemean contains the mean of zvar in each
        subpopulation, and stubnamelb and stubnameub are pointwise 95%
        confidence limits for stubnameb. If treatment() includes more than
        one variable, the created variables have 2, 3, ... appended to the
        names, e.g. stubnameb2.

    g(#) (stepp_tail only) defines the number of subpopulation groups.  The
        actual number of subpopulations used is 2 * # - 1.

    n1(#) (stepp_window only) defines the number of individuals belonging
        only to one of two neighbouring subpopulations.

    n2(#) (stepp_window only) defines the number of individuals in a
        subpopulation. The overlap between two neighbouring subpopulations is
        n2() minus n1() individuals.

    treatment(trt_varlist) defines the list of variables whose interactions
        with zvar are to be studied. Typically trt_varlist will comprise just
        one binary variable, representing the two arms of a parallel-group
        clinical trial.

    regression_cmd_options are options for regression_cmd.

    Options for stepp_plot:

    vn(#) # is an integer defining the variable number in treatment(), when
        more than one variable is specified. When only one variable is
        specified, vn() is not required.

    plot(plot) provides a way to add other plots to the generated graph; see
        help plot option.

    graph_options are options of graph twoway, such as xtitle(), ytitle(),
        etc.


Examples

    . stepp_tail regress y x a1 a2, g(10) gen(z) treatment(t)

    . stepp_window stcox x a1 a2, n1(40) n2(50) gen(z) treatment(t)

    . stepp_plot z, xtitle("Serum rhubarb") ytitle("log relative hazard")
        name(myplot)


Author

    Patrick Royston, MRC Clinical Trials Unit, London.
    pr@ctu.mrc.ac.uk


References

    M. Bonetti and R. D. Gelber. 2000. A graphical method to assess
        treatment-covariate interactions using the Cox model on subsets of
        the data. Statistics in Medicine 19: 2595-2609.

    M. Bonetti and R. D. Gelber. 2004. Patterns of treatment effects in
        subsets of patients in clinical trials. Biostatistics 5: 465-481.

    P. Royston and W. Sauerbre. 2009. Two techniques for investigating
        interactions between treatment and continuous covariates in clinical
        trials. Stata Journal 9(2): 230-251.

    W. Sauerbrei, P. Royston and K. Zapien. 2007. Detecting an interaction
        between treatment and a continuous covariate: a comparison of two
        approaches.  Computational Statistics and Data Analysis 51:
        4054-4063.


Also see

    Article: Stata Journal, volume 9, number 2: st0164

    Online:  mfpi