Re: st: Main effect for time-varying covariate

[Adam Olszewski <adam.olszewski@gmail.com>]

The way you describe your analysis (if I understand it correctly)
sounds like an appropriate setup to use the split-time analysis. This
is one of the three methods to account for immortal time bias (IPTW
would be your other option), as reviewed extensively, most recently in
Hurder et al., JCO 2013 (
http://jco.ascopubs.org/content/31/23/2963.long )
One would, split each observation at the time of "on/off" switch for
your factor, effectively creating 2 observations for each patient
(time "before" risk factor and "after") - using _stsplit_, and then
run stcrreg without any TVC component.
The Stata manual TVC methodology does something different - it assumes
that the covariate value is changing during time in a continuous
(exponentially decreasing) manner, but this is not a case in your
binary covariate setup (although you could probably use texp(t>event)
to achieve the same).

Adam Olszewski





On Wed, Aug 28, 2013 at 5:36 PM, Nicole Boyle <nicboyle@gmail.com> wrote:
> Forgive me, but I don't understand how discussing these nuances is relevant
> when addressing the original inquiry: determining the theoretical utility and
> interpretation of a time-varying covariate whose time-invariant component has
> been excluded from the model. These concerns seem more in line with a
> discussion about lead/length time bias.
>
> Nevertheless, to assuage your concerns, these patients are continually
> monitored for the presence of this particular risk factor, regardless
> of exhibited symptoms.
>
> ________________________________________
> From: owner-statalist@hsphsun2.harvard.edu
> [owner-statalist@hsphsun2.harvard.edu] on behalf of Steve Samuels
> [sjsamuels@gmail.com]
> Sent: Wednesday, August 28, 2013 1:34 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: Main effect for time-varying covariate
>
> Nichole:
>
> Please explain what the risk factor is and how its activation depends
> on the medical records. Perhaps you mean that the presence of the risk
> factor is known only after some test, and that test is recorded in the
> records. If so, the fact that the test is made at time "t" doesn't
> preclude the presence of the factor before "t". Also, if the test was
> made in response to certain symptoms, then other issues arise.
>
> Steve
>
>
> On Aug 27, 2013, at 5:00 PM, Boyle, Nicole M wrote:
>
> Hi Steve,
>
> Thanks for your response! I've elaborated on the issue in more
> (perhaps excessive) detail:
>
>
> ***Variable details***
> I'd like to model a binary variable as time-varying. This binary
> variable will model the onset of a particular
> risk factor. All patients under study enter into the study with this
> risk factor "turned off." The timing of
> when this risk factor "turns on" depends entirely on each patient's
> medical records (and for some patients,
> this risk factor may never "turn on"). This risk factor can only go
> from "off" to "on"; the reverse ("on" to "off")
> is not possible.
>
>
> ***Reason for modeling this var as time-varying***
> I would like to model this particular risk factor as a time-varying
> covariate in order to assess its association
> with the outcome while avoiding possible immortal time bias. In other
> words, I'd like assess the hazard ratio
> (at any instantaneous time during observation) for the outcome event
> comparing those with the risk factor
> "turned on" vs. those with the risk factor "turned off", accounting
> for the possibility that a patient's risk factor
> may be "turned on" at any time before or after his/her outcome event.
>
>
> ***Stata's covariate vs. coefficient distinction***
> Right now, the closest I can find to an answer is a mention in the
> Stata Statistical Analysis Manual:
>
>          http://www.stata.com/manuals13/ststcox.pdf#ststcoxRemarksandexamples
>
> In said manual, Cox models are run with and without the time-invariant
> component (on page 12 and pages
> 13-14, respectively). The Stata manual differentiates between models
> fit with time-varying COVARIATES
> (without the time-invariant component) from models fit with
> time-varying COEFFICIENTS (with the time-invariant
> component), saying
>
>        "Above we used tvc() and texp() to demonstrate fitting models
> with time-varying covariates, but
>         these options can also be used to fit models with time-varying
> coefficients."
>
> I think this aforementioned covariate/coefficient distinction may be
> the source of my confusion. From the
> manual's suggestion, it seems like adding this time-invariant
> component (aka: "main effect") will only test
> the proportional hazards assumption of the coefficient.
>
>
> Thanks,
> Nicole
> ________________________________________
> From: owner-statalist@hsphsun2.harvard.edu
> [owner-statalist@hsphsun2.harvard.edu] on behalf of Steve Samuels
> [sjsamuels@gmail.com]
> Sent: Wednesday, August 21, 2013 2:13 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: Main effect for time-varying covariate
>
> I'd need to know details about the internal covariate before I can
> answer your question. So please describe what it is and how its values
> are determined.
>
> Steve
>
> On Aug 20, 2013, at 7:14 PM, Boyle, Nicole M wrote:
>
> Hi all,
>
> I'm modeling a multivariable competing risks regression model
> (stcrreg), and I want to include an internal
> time-varying covariate.
>
> (1) Should I include a main effect for this time-varying covariate in
> the model? (I'm not trying to test
> the proportionality assumption here)
>
> (2) How does one report the overall value and significance of this
> time-varying variable?
>
> Thanks,
> Nicole
>
> (my apologies if this is a duplicate... 1st email bounced)
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