Re: st: Main effect for time-varying covariate

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

Hi -
As a point for the future, if your interest in the subject will
continue, there should imminently be an extensive support for modeling
CIF (or some version of it) using flexible parametric models - I
recommend reviewing this talk once it is available online:
http://www.stata.com/meeting/uk13/abstracts/#lambert
I am not sure how this will fit into the issues of cause-specific
hazard vs. CIF intrepretation, but the FPM framework might allow a
unified and easy approach to modeling both, with an easy incorporation
of TVC's as well.
AO

On Thu, Sep 5, 2013 at 2:48 PM, Nicole Boyle <nicboyle@gmail.com> wrote:
> Wonderful, Phil, thanks for the explanation! I'm going to go ahead and
> plot both outcomes.
> Thanks so much to Phil, Steve, and Adam... this has been a
> tremennnndously helpful and thought-provoking conversation. I have
> learned so much. I very much appreciate all the time each of you have
> taken to help me with this.
>
> To sum up, here are the following analysis choices I've made per our
> discussion. Feel free to chime in if anything rubs you the wrong way:
>
> -Modeling of hazard ratios will no longer be through the Fine-Gray
> model. Instead, covariate effects on the cause-specific hazard will be
> estimated through the Cox model, where the competing risk is censored.
> The only cause-specific event to be modeled will be the primary
> outcome of interest.
>
> -The CIFs will be plotted in both forms:
>     * Cause-specific CIFs for both the primary outcome and competing
> outcome (-stcompet-)
>     * Subdistribution CIF for just the primary outcome (-stcrreg-).
> Simply for comparison's sake.
>
> -I'm going to use -stsplit- instead of the -tvc- option to capture the
> time-varying nature of the time-varying risk factor, and then throw
> this risk factor into the model as a simple ["time-invariant"]
> covariate. I've decided to split at failure times, and expand the
> coding of the TVC risk factor to be "on" or "off" for each created
> time slot. Doing so will exploit the Cox model's maximum partial
> likelihood estimator property (briefly explained on page 13:
> http://www.stata.com/manuals13/ststsplit.pdf ).
>
> Nicole
>
> On Wed, Sep 4, 2013 at 4:17 PM, Phil Clayton
> <philclayton@internode.on.net> wrote:
>> From memory he used an example of breast cancer.
>>
>> If you graph the CIF of cancer recurrence by age, older patients have a lower incidence of recurrence.
>>
>> That looks good for older people until you graph the CIF of death - older patients have a higher incidence of death. Since death competes with recurrence, this makes the older patients look better on the recurrence CIF, but it's because they're dying before they get a chance to have recurrence. Doesn't look so good for older people any more.
>>
>> You need to look at both outcomes in order to disentangle the competing events and understand what's actually going on. By selectively presenting one outcome you're not telling the whole story.
>>
>> Phil
>>
>> On 05/09/2013, at 6:37 AM, Nicole Boyle <nicboyle@gmail.com> wrote:
>>
>>>> I went to a talk by Jason Fine last year and he gave the following general advice:
>>>> - use a Cox model for each of the competing outcomes (in your case infection & death)
>>>> - use a Fine-Gray model for each of the competing outcomes
>>>> - present all of those results
>>>
>>> Thanks for the advice! What's the utility of presenting model results
>>> for the outcome of death if death is not an outcome of interest in my
>>> study? Feel free to direct me to a paper if you'd like.
>>
>>
>> *
>> *   For searches and help try:
>> *   http://www.stata.com/help.cgi?search
>> *   http://www.stata.com/support/faqs/resources/statalist-faq/
>> *   http://www.ats.ucla.edu/stat/stata/
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/faqs/resources/statalist-faq/
> *   http://www.ats.ucla.edu/stat/stata/

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/faqs/resources/statalist-faq/
*   http://www.ats.ucla.edu/stat/stata/