[R] bias sampling

David dwinsemius at comcast.net
Mon Mar 19 00:49:46 CET 2012

Thank you for your time, Thomas .

In case the questioner is not aware of a few facts... Thomas Lumley is both a) the person who originally ported tThereau's "survival" package to R and was also its maintainer for many years , and b) the author of the "survey" package

Sent from my iPhone

On Mar 18, 2012, at 5:51 PM, Thomas Lumley <tlumley at uw.edu> wrote:

> On Mon, Mar 19, 2012 at 10:27 AM, David Winsemius
> <dwinsemius at comcast.net> wrote:
>> On Mar 18, 2012, at 3:54 PM, Thomas Lumley wrote:
>>> On Mon, Mar 19, 2012 at 6:34 AM, David Winsemius <dwinsemius at comcast.net>
>>> wrote:
>>>> On Mar 16, 2012, at 1:09 PM, niloo javan wrote:
>>>>> hi
>>>>> i want to analyze Right Censore-Length bias data under cox model with
>>>>> covariate.
>>>>> what is the package ?
>>>> I initially left this question alone because I thought there might be
>>>> viewers for whom it all made perfect sense. After two days that
>>>> probability
>>>> seems to be declining. The problem I had was the meaning of "length bias
>>>> data". Are you talking about a non-proportional effect in which the
>>>> assumption of a constant hazard ratio over time is false and other
>>>> methods
>>>> are needed. If that is correct, then you should get a copy of Therneau
>>>> and
>>>> Grambsch's "Modeling Survival Data" and study the chapter on "Functional
>>>> Form'. The package would be "survival".
>>> Length-biased sampling is what you get when you take a cross-sectional
>>> sample of an ongoing process -- long intervals are over-represented.
>> Thank you Thomas;
>>  For example people who have survived to age 75 might be systematically
>> different with respect to both the distribution of cardiovascular risk
>> factors and their impact on the event of interest (AMI. CV death, or
>> all-cause mortality) than persons at age 45. And that would also not take
>> into account the fact those risk factors might have changed over the
>> interval from age 45 to age 75 in the survivors?
>>> If the arrival time is known for everyone in the sample, the usual Cox
>>> model facilities for left truncation apply.  If the arrival times are
>>> not known it would be much more difficult, and would probably need
>>> parametric modelling.
>>  Am I correct in thinking that additional assumptions about the
>> "length-bias" would need to be explicitly stated or modeled under a set of
>> plausible scenarios before progress in any framework could be anticipated?
>> It would seem that there could be many forms of such a "length-bias".
> Yes, as with any missing data problem things can go arbitrarily badly wrong.
> The classical 'length-biased sampling' problem is a cross-sectional
> sample from a stationary population process, and that gives good
> results.
> Obviously if you don't recruit anyone before time  T, there is no
> information about what happened before then, but there may still be
> useful information afterwards.  A good example is the research project
> on after-effects of the nuclear bombings of Nagasaki and Hiroshima,
> where recruitment started (IIRC) 5 years after the event.  There's no
> information on survival in the first five years, but very good
> subsequent information.
>    -thomas
> -- 
> Thomas Lumley
> Professor of Biostatistics
> University of Auckland

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