help for cureregr (nov2013) (see also: [R] stset)

cureregr

, cure model regression (split-population model) or,

parametric-cure model (PCM)

cureregr [varlist] [if exp] [in range]
,
[distribution_option(weibull | lognormal | logistic |
gamma | exponential |
lognv, lognormalv, normallnt |
lognv1, lognormalv1, normallntv1 |
logns1, lognormals1, normallnts1
)
class_option(mixture | non-mixture)
link_option(logistic | lml, log-minus-log | linear
)
scale_option(varlist)
shape_option(varlist)
noconstant
scale_noc
shape_noc
nodescriptionmodel
noshow
eform
noheader
auxuse
left]

note varlist, scale_option(varlist),
and shape_option(varlist) may contain factor variables; see
fvvarlist. stepwise is allowed; see prefix.

Description

cureregr fits a parametric cure model in either the non-mixture or mixture class.

important -- cureregr requires that the data be stset prior
to use. cureregr will correctly estimate multiple records per subject, to
include time-varying-covariates. Please note that for multiple records per
subject the records must be in contiguous intervals defined in stset,
[_t0, _t]; in other words, there may be no gaps (see: stdes).

Options

distribution_option(weibull | lognormal | logistic | gamma | exponential | lognv | lognv1 | logns1)

define Kernel:

weibull: exponential in tt

lognormal: normal(ln(tt))

logistic: tt/(1-tt)

gamma: gammap(shape, scale, time)

exponential: gammap(shape==1, scale, time)

lognv: lognormalv, normal(ln(t)) fit variance

lognv1: lognormalv1, normal(ln(t)) variance=1

logns1: lognormals1, normal(ln(scale*t)) shape=1

class_option(mixture | non-mixture)

define parametric-cure model class:

mixture: pi+(1-pi)*(1-Kernel)

non-mixture: pi^Kernel

link_option(logistic | lml, log-minus-log | linear)

define cure_fraction (pi) link funtion

logistic: exp(xb_pi)/(1+exp(xb_pi))

lml: exp(-exp(xb_pi))

linear: exp(xb_pi)

where xb_pi is the linear model for the cure_fraction, pi

scale_option(varlist)

varlist for scale model

shape_option(varlist)

varlist for shape model

noconstant

specifies that no constant term for cure-fraction model is to be fit

scale_noc

specifies that no constant term for scale model is to be fit

shape_noc

specifies that no constant term for shape model is to be fit

--

left specifies that there are left censored subjects.
the failures defined by the last value specified by the failure()
argument of stset are taken to be left censored. this creates
(or overwrites) a variable called _leftd and is
indicated
in e(depvar).

--

nodescriptionmodel

do not show cureregr model definition information

noshow

do not show st setting information

please note these definitions

tt = (scale*time)^shape function of time, t-tilde

scale=exp(xb_scale) for the scale model

shape=exp(xb_shape) for the shape model

Example

. cureregr g1_ageyr, dist(weibull) class(n) link(logi) noconstant

. xi: cureregr g0_ageyr i.g0_wbc i.g0_plt, sc(i.g0_wbc i.g0_plt)
sh(i.g0_plt) dist(lognormal) class(non) link(logi) scale_noc shape_noc

. xi: cureregr (i.study_r) i.g_liver i.race2 if analset1 ,
dist(weibull) link(lml) sc((i.study_r) ) sh((i.study_r) )
class(nonmixture) technique(nr)

predicting

predict [if exp] [in range] ,
[at(numlist) |
all |
survival |
ses |
ucs |
lcs |
hazard |
fd]
[gen(varlist)]
[level(number)]
[fmtprint(%8.6f)]

After model estimation the survival, standard error, and confidence
interval can be displayed with predict,
at() specified time points. The survival estimates are displayed
for all covariate patterns and if or in may be used to limit the
number of covariate patterns displayed; An example can be found in the ancillary
file, cureregr_predict_eg.pdf. The fmt() option allows any acceptable
numeric format specification for the displayed numeric results. Please note also
that the confidence limits are based on a ln(-ln(S)) transform.

Six quantities may be estimated and placed into a new variable:
survival (S), and its standard error (seS), lower confidence limit (lciS),
and upper confidence limit (uciS). In addition, the hazard (haz), or the fail
density (fd) may be estimated. One of the key words (s, se, l, u, h, fd,
or all) can be specified. With the option gen(), the estimate
requested by the keyword will be placed in the new variable, while the
keyword all requires six new variable names in order:
survival, ses, lcs, ucs, hazard, and fd. Where the default names are: S,
seS, lciS, uciS, haz, and fd, respectively.

. predict if study_r==1 & g_liver==1 & race2==2,
at(30(30)365 2922)

. predict , all gen(s_v2 se_v2 lci90_v2 uci90_v2 haz_v2 fd_v2)
level(90)

. predict , hazard gen(myhaz)

. predict , lcs gen(lci95_v1) level(95)

. predict , ucs gen(uci95_v1) level(95)

Author

Allen Buxton, Children's Oncology Group, Monrovia, CA

- acknowledgements -

Richard Sposto, Mark Krailo, & Todd Alonzo, Keck School of Medicine,
University of Southern California, USA

John Thompson & Clarie Weston, Department of Health Sciences,
University of Leicester, UK

References

Sposto R. Cure model analysis in cancer: An application to data from
the Children's Cancer Group. Statistics in Medicine, 21: 293-312, 2002

Stephen Jenkins, spsurv (spsurv.ado for Stata, 23may2001), University
of Essex, UK

Maller RA. & Zhou, X. Survival Analysis with Long Term Survivors,
John Wiley, 1997.

Schmidt P. & Witte A. Predicting criminal recidivism using
'split-population' survival time models, Journal of Econometrics, 40: 141-159,
1989

Files

main: cureregr.ado, lcureregr_lf.mlib

predict: cureregr_p.ado & cureregr_gen_p.ado

- ancillary -

pcm_key.pdf, a description of the models cureregr will fit.

wittelognormal_notes.pdf, comments on the lognormal distributions fit.

cureregr_predict_eg.pdf, on predicting.

example_stata.do, an example with factor variables.

example_stata.dta, dataset in the above example.

note regarding the Mata function library lcureregr_lf.mlib

lcureregr_lf.do refers to: PCM_evaluators_00.do and PCM_evaluators_01.do

Also see

Manual: [R] stset