help for stepp_tail, stepp_window, stepp_plotPatrick Royston

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.

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