Comparing statistical methods

Peggy Zuckerman peggyzuckerman at GMAIL.COM
Fri Aug 10 04:41:07 UTC 2018


Agree, and similarly, am concerned that the highly touchy subject of
deriving value from meds is measured in some cases as "disease-free"
months.  Heck, most of us with terminal cancer would be happy to  have the
disease for a long time...

There does seem to be a new shift to discuss clinical benefit to the
patient, which does include improved quality of life, and helpful when
tumors become stabilized, while neither shrinking or growing.  My own bias
as to the need to look at the patient and not just the mets, is my own
response to high dose interleukin.  I had a 10 cm kidney tumor removed, no
response to a trial drug, and then entered that treatment.  There was some
immediate shrinkage to the largest of my 100+ lung mets half-way through
the treatment, more shrinkage six weeks after the last dose.  However, it
was five month post that dose that my lungs were completely cleared.  The
value of teaching the immune system to get back to basics.  Yet had I been
treated more recently, with the mets still visible six week post the IL2, I
might well have been given Sutent or Torisel (wildly different efficacy,
per the trial data), and with the response being attributed to the targeted
agent vs the IL2.  Thus, my skepticism that there is a neat way to compare
trial data--how the patient is doing is a meaningful measure, but not such
a neat package to examine.

Peggy Zuckerman
PS  If the patient lives long enough to get to the next and better
medication, or perhaps his system is primed for the subsesquent response,
how is that to be measured?

Peggy Zuckerman
www.peggyRCC.com

On Thu, Aug 9, 2018 at 6:39 PM, Edward Hoffer <ehoffer at gmail.com> wrote:

> One of the biggest problems with our focus on p values is that
> "statistically significant" may be clinically meaningless.  If you have a
> large enough sample size, extending life by a month with treatment X
> compared to Y - at a cost of $500,000 and horrible GI side effects - may be
> trumpeted as a "significant prolongation of life.
> Edward Hoffer MD.
>
> On Thu, Aug 9, 2018 at 1:17 PM, Peggy Zuckerman <peggyzuckerman at gmail.com>
> wrote:
>
>> As a patient advocate who wants to provide meaningful education for other
>> cancer patients, this issue is very important to me. A real challenge is
>> that the use of the stats that come from a trial rarely give enough
>> information so that a comparison of treatments can be made.  This is true
>> whether it is in regard medications or timing of surgery, sequencing of
>> medication.  One trial may include patients who have had multiple previous
>> treatments, but not show the length of response those patients received.
>> The patients who had some success with Medicine A, now being tested with
>> Medicine NEW, may respond differently than those who had no meaningful
>> response with A.  One could ask whether Medicine A acts as a primer for
>> greater response with Medicine NEW.  If the patients in the studies have
>> metastases than emerged within months past a 'curative' surgery, can those
>> patients truly similar to those whose mets did not emerge until 3-4 years
>> later.
>>
>> The older trials often accepted patients only on the basis of the landing
>> site of the primary tumor, without regard for the pathology of that tumor.
>> Newer trials fail to differentiate between patients with or without certain
>> molecular characteristics, or choose an inappropriate level of that
>> measurement to group patients.
>>
>> And with the understanding that cancers can be subdivided into more
>> subtypes than previously recognized, some caused by hereditary tendencies,
>> others by exposure to heavy metals, other just due to aging, we need to
>> recategorize trial data accordingly.  Of course, that raises other issue
>> which aggravate the statisticians, mainly the size of the groups needed for
>> comparison.  When only 30% of patients in a study respond to a medication,
>> naturally the question must be raised as to why those 30% did respond.
>> Every study needs a way to characterize that subpopulation, as the failure
>> to do so has cost us effective treatments and billions of dollars.
>>
>> Peggy Zuckerman
>>
>> Peggy Zuckerman
>> www.peggyRCC.com
>>
>> On Thu, Aug 9, 2018 at 8:37 AM, Harold Lehmann <lehmann at jhmi.edu> wrote:
>>
>>> I am on a kick these days about causal reasoning (cf Judea Pearl, The
>>> Book of Why). He points out that there are three layers of increasing
>>> abstraction: statistical, causal, and contrafactual ("what if"). (The
>>> anti-Bayesians on this list will be happy to know that Bayesian statistics
>>> is relegated to the lowest level, although suited to represent the other
>>> layers, if directed by them.)
>>>
>>> It seems to me that decision making is all about "what if," and
>>> therefore, statistics (as traditionally conceived) are inadequate.
>>>
>>> Harold
>>>
>>>
>>> On Aug 9, 2018, at 9:40 AM, Elias Peter <pheski69 at GMAIL.COM> wrote:
>>>
>>> I was directed by my son (math person, statistician, and professional
>>> data manager)to  a post in a forum where data/stats people discuss methods.
>>> It asks for information about *outcome data* comparing different
>>> statistical models. It might be interesting for this group to follow this.
>>> Or perhaps contribute. I have not seen any answers yet. Here is the flavor
>>> of the question:
>>>
>>> "The overarching goal of statistics is to make decisions in the face of
>>> uncertainty.” …list of statistical approaches…  "What seems to be
>>> missing is head-to-head comparisons of approaches to see which ones
>>> optimize utility/loss/cost functions where such functions reflect real,
>>> concrete goals.” …  "Does anyone know of comparative studies that
>>> inform us of the value of two or more statistical approaches when the goal
>>> is making the best decisions?"
>>>
>>>
>>> https://discourse.datamethods.org/t/choosing-statistical-par
>>> adigms-by-studying-the-quality-of-decisions-to-which-they-le
>>> ad/372?u=f2harrell
>>>
>>> Peter Elias
>>>
>>>
>>>
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>>
>>
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>
>






Moderator: David Meyers, Board Member, Society to Improve Diagnosis in Medicine


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