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Re: st: Time-dependent variables and the simon & makuch method

From   Steve Samuels <>
Subject   Re: st: Time-dependent variables and the simon & makuch method
Date   Sun, 13 Oct 2013 19:44:43 -0400

• When you joined Statalist, you were asked to read the list FAQ. Section
3.4, asks for full references and gives the reasons. "Mantel-Byar",
 "Simon & Makuch", and "recent reviews" do not qualify.

Below I give the Mantel-Byar and Simon-Makuch references, with a
pertinent review by Anderson et al. (1983).

To answer your questions:

• When data with time-varying covariates are properly -stset- as
multiple-record data with the id() option, the log rank test in Stata
*is* the Mantel-Byar test.

• Replacement for KM plot

In a reply to Nichole Boyle
(, I
suggested a "future" conditional incidence function" for a binary
time-dependent variable, and said I didn't know a reference. I do now.
 Anderson and colleagues published a version in 1983 and called it
the "landmark method". They also criticized the use of the log rank test
and advocated the use of the Mantel-Byar test.

I don't have the Simon and Makuch reference, but here's a plot that
gives a reasonable result in the Stanford heart transplant data (webuse
stan3). It utilizes time-varying strata in the Cox model. The curve is
akin to a synthetic life table in demography (e.g. Arias, 2010), in which
current age-specific death rates are used to construct a life table. In
this case, waiting time to transplant plays the role of age. The table
has predictive value only if patients transplanted at _t and those
transplanted earlier have similar risks of death for subsequent times.
I'd expect dissimilar short term future risks, if only because of postoperative

***********CODE BEGINS*****************
webuse stan3, clear
stset t1, fail(died) id(id)
sts test posttran, logrank  // Mantel-Byar
stcox posttran  // p-values should be close to above

/* -stcox- to get stratum-specific KM curves */
gen dummy=1
stcox dummy, strata(posttran)
predict surv, basesurv
separate surv, by(posttran)

twoway connect surv0 surv1 _t, sort  lp(dash solid)
*********CODE ENDS*************


Anderson, James R, Kevin C Cain, and Richard D Gelber. 1983. Analysis of
survival by tumor response. Journal of Clinical Oncology 1, no. 11:
710-719, downloaded from:

Arias, E. 2010. United States life tables, 2006. Natl Vital Stat Rep 58,
no. 21: 1-40‎

Mantel, Nathan, and David P. Byar. 1974. Evaluation of Response-Time
Data Involving Transient States: An Illustration Using Heart-Transplant
Data. Journal of the American Statistical Association Journal of the
American Statistical Association 69, no. 345: 81-86.

Simon, Richard, and Robert W Makuch. 1984. A non‐parametric graphical
representation of the relationship between survival and the occurrence
of an event: Application to responder versus non‐responder bias.
Statistics in Medicine 3, no. 1: 35-44.


> On Oct 9, 2013, at 6:51 AM, Florian Posch wrote:
> Dear Statalisters,
> your help would be very much appreciated on the following:
> I'm working on a dataset where I am modeling the impact of a time-dependent variable (the onset of thrombosis during follow-up) on survival. This analysis works fine with stsplit, and is pretty much the same as done in the Stanford Heart Transplant data example:
> The Stata Journal (2004) 4 , Number 2, pp. 221–222
> Stata tip 8: Splitting time-span records with categorical time-varying covariates
> Again, my analysis works well for the Cox model, Kaplan-Meier survival curves, and the logrank-test (as illustrated in the Staa journal paper above). However, some recent reviews postulate that time-dependent covariates can validly be used in the Cox model but that is is INAPPROPRIATE to use them in Kaplan-Meier survival curves and the logrank test. These reviews suggest that the appropriate substitute methods in this setting are the Simon & Makuch plot (substitute for Kaplan-Meier curves), and the Mantel-Byar test (substitute for the logrank test).
> Searching the web I found some posts in forums and a couple of papers on Pubmed which say that Stata has implemented these methods, however, I could not find such a package on the SSC or anywhere else in Stata. A prior post regarding this issue on statalist remained unanswered:
> Do you know where I can find these analysis methods for Stata?
> Thank you very much in advance for your input!
> Flo
> Florian Posch, MSc
> MD PhD Student
> Medical University of Vienna
> *

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