Comparing statistical methods

Peggy Zuckerman peggyzuckerman at GMAIL.COM
Thu Aug 9 17:17:13 UTC 2018


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-
> paradigms-by-studying-the-quality-of-decisions-to-which-
> they-lead/372?u=f2harrell
>
> Peter Elias
>
>
>
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Moderator: David Meyers, Board Member, Society to Improve Diagnosis in Medicine


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