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==Do Oscar Winners Live Longer?==
If you put "Oscar winners live longer" in Google you will get  over 7,000 hits.
Here is one from the January 23, 2007 issue of
[http://www.healthandage.com/public/health-center/37/news/7655/Oscar-winners-liv
e-longer.html 'Health and Aging']


Oscar winners live longer: Reported by Susan Aldridge, PhD, medical
journalist.<br>


<blockquote>It is Oscar season again and, if you're a film fan, you'll be
==Forsooth==
following proceedings with interest. But did you know there is a health benefit
to winning an Oscar? Doctors at Harvard Medical School say that a study of
actors and actresses shows that winners live, on average, for four years more
than losers. And winning directors live longer than non-winners.</blockquote>


Source: "Harvard Health Letter" March 2006.
==Quotations==
“We know that people tend to overestimate the frequency of well-publicized, spectacular
events compared with more commonplace ones; this is a well-understood phenomenon in
the literature of risk assessment and leads to the truism that when statistics plays folklore,
folklore always wins in a rout.”
<div align=right>-- Donald Kennedy (former president of Stanford University), ''Academic Duty'', Harvard University Press, 1997, p.17</div>


The assertion that Oscar winners live longer was based on an article by Donald
----
Redelmeier, and Sheldon Singh: "Survival in Academy Award-winning actors and
actresses". ''Annals of Internal medicine'', 15 May, 2001, Vol. 134,No. 10 p
955-962.


This is the kind of study the media loves to report and Medical Journals enjoy
"Using scientific language and measurement doesn’t prevent a researcher from conducting flawed experiments and drawing wrong conclusions — especially when they confirm preconceptions."
the publicity. Another such claim that is in the news as this is written, is
that the outcome  of the Superbowl game determines whether the stock market will
go up or down this year.  Unlike the Oscar winners story the author of this
claim Leonard Koppet admits that it is all a joke, see
[http://www.dartmouth.edu/~chance/chance_news/recent_news/chance_news_13.04.html
#item11 Chance News 13.04].


A recent paper by James Hanley, Marie-Pierre Sylvestre and Ella Huszti, "Do
<div align=right>-- Blaise Agüera y Arcas, Margaret Mitchell and Alexander Todoorov, quoted in: The racist history behind facial recognition, ''New York Times'', 10 July 2019</div>
Oscar winners live longer than less successful peers? A reanalysis of the
evidence," ''Annals of Internal medicine'', 5 September 2006, Vol 145, No. 5, p
361-363, claims that the Redelmeier, Singh paper was flawed and their reanalysis
of the data does not  support the claim that Oscar winners live longer.


For their study Redelmeier and Singh identified all actors and actresses ever
==In progress==
nominated for an academy award in a leading or a supporting role up to the time
[https://www.nytimes.com/2018/11/07/magazine/placebo-effect-medicine.html What if the Placebo Effect Isn’t a Trick?]<br>
of the study (n = 762). Among these there were 235 Oscar winners. For each
by Gary Greenberg, ''New York Times Magazine'', 7 November 2018
nominee another cast member of the same sex who was in the same film and was
born in the same era was identified (n= 887) and used as controls. 


The authors  used the Kaplan-Meier method to provide a life table for the Oscar
[https://www.nytimes.com/2019/07/17/opinion/pretrial-ai.html The Problems With Risk Assessment Tools]<br>
winners and the control group. A life table estimates for each x the probability
by Chelsea Barabas, Karthik Dinakar and Colin Doyle, ''New York Times'', 17 July 2019
of living x years.  We illustrated the Kaplan-Meier method, using data obtained
from Dr. Radelmeier, in
[http://www.dartmouth.edu/~chance/chance_news/recent_news/chance_news_10.06.html
#item10  Chance News 10.06]. Our results agreed with the following life tables
the authors provided for the two groups.


<center> http://www.dartmouth.edu/~chance/forwiki/oscar.jpg </center>
==Hurricane Maria deaths==
Laura Kapitula sent the following to the Isolated Statisticians e-mail list:


The areas under the two curves  are estimates for the life expectance for the
:[Why counting casualties after a hurricane is so hard]<br>
two groups. Using a test called the "log-rank test" they conclude that the
:by Jo Craven McGinty, Wall Street Journal, 7 September 2018
overall difference in life expectancy was 3.9 years (79.7 vs. 75.8 years; P) =
.003.


While the life tables look like  standard life tables there is one big
The article is subtitled: Indirect deaths—such as those caused by gaps in medication—can occur months after a storm, complicating tallies
difference. We note that 100 percent of the Oscar winners live to be at least
   
30 years old.  Of course this is not surprising because they are known to be
Laura noted that  
Oscar winners. Thus we know ahead of time that the Oscar winners will live
:[https://www.washingtonpost.com/news/fact-checker/wp/2018/06/02/did-4645-people-die-in-hurricane-maria-nope/?utm_term=.0a5e6e48bf11 Did 4,645 people die in Hurricane Maria? Nope.]<br>
longer than a traditional life table would predict. This gives them an advantage
:by Glenn Kessler, ''Washington Post'', 1 June 2018
in their life expectancy. This is called a selection bias. Of course the
controls also have an advantage because we know that were in a movie at about
the same age as a nominee. But there is no reason to believe that these
advantages are the same.


Here is a more obvious example of selection bias discussed in Robert Abelson's
The source of the 4645 figure is a [https://www.nejm.org/doi/full/10.1056/NEJMsa1803972 NEJM article]. Point estimate, the 95% confidence interval ran from 793 to 8498.
book "Statistics as Principled Argument' and reported in Chance News 4.05.


<Blockquote>A study found that the average life expectancy
President Trump has asserted that the actual number is
    of famous orchestral conductors was 73.4 years,
[https://twitter.com/realDonaldTrump/status/1040217897703026689 6 to 18].
    significantly higher than the life expectancy for
The ''Post'' article notes that Puerto Rican official had asked researchers at George Washington University to do an estimate of the death toll.  That work is not complete.
    males, 68.5,  at the time of the study.  Jane
[https://prstudy.publichealth.gwu.edu/ George Washington University study]
    Brody in her "New York Times" health column
    reported that this was thought to be due to arm
    exercise.  J. D  Caroll gave an alternative
    suggestion, remarking that it was reasonable to
    assume that a famous orchestra conductor was
    at least 32 years old. The life expectancy for
    a 32 year old male was 72 years making the 73.4
    average not at all surprising. </blockquote>


To avoid the possible of selective bias Redelmier and Singh did an analysis
:[https://fivethirtyeight.com/features/we-still-dont-know-how-many-people-died-because-of-katrina/?ex_cid=538twitter We sttill don’t know how many people died because of Katrina]<br>
using time-dependent covariates, in which winners were counted as controls until
:by Carl Bialik, FiveThirtyEight, 26 August 2015
the time of first they won the Oscar.  This  resulted in a difference in life
expentance of 20% (CI, 0% to 35%). Since the confidence interval includes 0 the
difference is not significant. In a letter to the editor in response to the
study by Hanley et.al.  Redelmier and Singh report that they did the same
analysis with one more years data and obtained a result even more obviously not
significant.


Sylvester and colleagues analyzed the data by comparing the life expectancy of
----
the winners from the moment they win with others alive at that age. In the
[https://www.nytimes.com/2018/09/11/climate/hurricane-evacuation-path-forecasts.html These 3 Hurricane Misconceptions Can Be Dangerous. Scientists Want to Clear Them Up.]<br>
McGill Press Release,  Hanley remarks "The results are not as, shall we say,
[https://journals.ametsoc.org/doi/abs/10.1175/BAMS-88-5-651 Misinterpretations of the “Cone of Uncertainty” in Florida during the 2004 Hurricane Season]<br>
dramatic, but they're more accurate."
[https://www.nhc.noaa.gov/aboutcone.shtml Definition of the NHC Track Forecast Cone]
----
[https://www.popsci.com/moderate-drinking-benefits-risks Remember when a glass of wine a day was good for you? Here's why that changed.]
''Popular Science'', 10 September 2018
----
[https://www.economist.com/united-states/2018/08/30/googling-the-news Googling the news]<br>
''Economist'', 1 September 2018


We recommend reading this [http://www.mcgill.ca/newsroom/news/?ItemID=21645
[https://www.cnbc.com/2018/09/17/google-tests-changes-to-its-search-algorithm-how-search-works.html We sat in on an internal Google meeting where they talked about changing the search algorithm — here's what we learned]
press release] for more information about the study by Sylvester et al.  
----
[http://www.wyso.org/post/stats-stories-reading-writing-and-risk-literacy Reading , Writing and Risk Literacy]


When the Redelmier and Singh paper came out, our colleague Peter Doyle was
[http://www.riskliteracy.org/]
skeptical of the results and suggested simple ways to to see that Oscar winners
-----
do not live longer.   He described one of these methods as follows:
[https://twitter.com/i/moments/1025000711539572737?cn=ZmxleGlibGVfcmVjc18y&refsrc=email Today is the deadliest day of the year for car wrecks in the U.S.]


<blockquote> You can do a simulation to rewrite history, having the computer
==Some math doodles==
select at random new OSCAR winners from among each year's nominees.  Each time
<math>P \left({A_1 \cup A_2}\right) = P\left({A_1}\right) + P\left({A_2}\right) -P \left({A_1 \cap A_2}\right)</math>
you rewrite history you compute a new p-value, and you discover that you get a
value less than .05 more than 5 percent of the time.  You can do the same thing
with data that are much more easily simulated, but still, it's kind of cool to
have the computer churning out new OSCAR winners.  Richard Burton would approve,
because he generally comes out a winner! </blockquote>


Here is another way to see that Oscar winners do not live longer that Peter
<math>P(E)  = {n \choose k} p^k (1-p)^{ n-k}</math>
described in the form of a game of points:


<blockquote>We decide to compare those who have
<math>\hat{p}(H|H)</math>
won an Oscar (call them 'winners') with those who have merely been nominated
(call them 'also-rans'). Our 'null hypothesis' is that having won an Oscar
doesn't
help your health. We create a contest by associating a point to the death of
anyone who has ever been nominated for an Oscar. Points are bad: the winners
get a point if the deceased was a star; the also-rans get a point if the
deceased
was an also-ran. Suppose that the deceased died in their d'th day of life. Over
the course of history, some number a of nominees will have made it to the kth.
day of their lives, and been a winner on that day; some number b of nominees
will have made it to the d'th day of their lives, and been an also-ran on that
day.
If our hypothesis is correct, and having won an Oscar really doesn't help your
health, then the probability that the winners get this point should be a/(a+b).
So now we've got a game of points. with known probability of winning for each
point. If you carry out this analysis correctly, you will that the winners win
very nearly the expected number of points, leaving us no reason to suppose that
winning an Oscar helps you live longer.</blockquote>


Despite the fact that, in their paper  Redelmier and Singh said the data they
<math>\hat{p}(H|HH)</math>
used would be available on their website, it never was.  Thus Peter with the
help of a student  Mark Mixer had to determine their own data set which is
available  [http://www.dartmouth.edu/~chance/forwiki/ocar4.nb here] in a
Mathematica program that Peter wrote. If you do not have Mathematica you can
read this using the free
[http://www.wolfram.com/products/mathreader/MathReader].


For the paper by Hanley and his colleagues, Redelmier and Singh did make their
==Accidental insights==
data available but it still was not the original data since it included the
results of one more year of Oscars winners.  This data is available
[http://www.dartmouth.edu/~chance/forwiki/OscarData.xls here].


===Homework===
My collective understanding of Power Laws would fit beneath the shallow end of the long tail. Curiosity, however, easily fills the fat end.  I long have been intrigued by the concept and the surprisingly common appearance of power laws in varied natural, social and organizational dynamics.  But, am I just seeing a statistical novelty or is there meaning and utility in Power Law relationships? Here’s a case in point.


Carry out one of Peter's methods using your favorite math program and either of
While carrying a pair of 10 lb. hand weights one, by chance, slipped from my grasp and fell onto a piece of ceramic tile I had left on the carpeted floor. The fractured tile was inconsequential, meant for the trash.
the two data sets. Laurie's homework assigned by Peter was to do this using
<center>[[File:BrokenTile.jpg | 400px]]</center>
True Basic which is the only program he knows.  Report your findings in the next
As I stared, slightly annoyed, at the mess, a favorite maxim of the Greek philosopher, Epictetus, came to mind: “On the occasion of every accident that befalls you, turn to yourself and ask what power you have to put it to use.” Could this array of large and small polygons form a Power Law? With curiosity piqued, I collected all the fragments and measured the area of each piece.
Chance News.


Submitted by Laurie Snell
<center>
{| class="wikitable"
|-
! Piece !! Sq. Inches !! % of Total
|-
| 1 || 43.25 || 31.9%
|-
| 2 || 35.25 ||26.0%
|-
|  3 || 23.25 || 17.2%
|-
| 4 || 14.10 || 10.4%
|-
| 5 || 7.10 || 5.2%
|-
| 6 || 4.70 || 3.5%
|-
| 7 || 3.60 || 2.7%
|-
| 8 || 3.03 || 2.2%
|-
| 9 || 0.66 || 0.5%
|-
| 10 || 0.61 || 0.5%
|}
</center>
<center>[[File:Montante_plot1.png | 500px]]</center>
The data and plot look like a Power Law distribution. The first plot is an exponential fit of percent total area. The second plot is same data on a log normal format. Clue: Ok, data fits a straight line.  I found myself again in the shallow end of the knowledge curve. Does the data reflect a Power Law or something else, and if it does what does it reflect?  What insights can I gain from this accident? Favorite maxims of Epictetus and Pasteur echoed in my head:
“On the occasion of every accident that befalls you, remember to turn to yourself and inquire what power you have to turn it to use” and “Chance favors only the prepared mind.”
 
<center>[[File:Montante_plot2.png | 500px]]</center>
My “prepared” mind searched for answers, leading me down varied learning paths. Tapping the power of networks, I dropped a note to Chance News editor Bill Peterson. His quick web search surfaced a story from ''Nature News'' on research by Hans Herrmann, et. al. [http://www.nature.com/news/2004/040227/full/news040223-11.html Shattered eggs reveal secrets of explosions].  As described there, researchers have found power-law relationships for the fragments produced by shattering a pane of glass or breaking a solid object, such as a stone. Seems there is a science underpinning how things break and explode; potentially useful in Forensic reconstructions.
Bill also provided a link to [http://cran.r-project.org/web/packages/poweRlaw/vignettes/poweRlaw.pdf a vignette from CRAN] describing a maximum likelihood procedure for fitting a Power Law relationship. I am now learning my way through that.
 
Submitted by William Montante
 
----

Latest revision as of 20:58, 17 July 2019


Forsooth

Quotations

“We know that people tend to overestimate the frequency of well-publicized, spectacular events compared with more commonplace ones; this is a well-understood phenomenon in the literature of risk assessment and leads to the truism that when statistics plays folklore, folklore always wins in a rout.”

-- Donald Kennedy (former president of Stanford University), Academic Duty, Harvard University Press, 1997, p.17

"Using scientific language and measurement doesn’t prevent a researcher from conducting flawed experiments and drawing wrong conclusions — especially when they confirm preconceptions."

-- Blaise Agüera y Arcas, Margaret Mitchell and Alexander Todoorov, quoted in: The racist history behind facial recognition, New York Times, 10 July 2019

In progress

What if the Placebo Effect Isn’t a Trick?
by Gary Greenberg, New York Times Magazine, 7 November 2018

The Problems With Risk Assessment Tools
by Chelsea Barabas, Karthik Dinakar and Colin Doyle, New York Times, 17 July 2019

Hurricane Maria deaths

Laura Kapitula sent the following to the Isolated Statisticians e-mail list:

[Why counting casualties after a hurricane is so hard]
by Jo Craven McGinty, Wall Street Journal, 7 September 2018

The article is subtitled: Indirect deaths—such as those caused by gaps in medication—can occur months after a storm, complicating tallies

Laura noted that

Did 4,645 people die in Hurricane Maria? Nope.
by Glenn Kessler, Washington Post, 1 June 2018

The source of the 4645 figure is a NEJM article. Point estimate, the 95% confidence interval ran from 793 to 8498.

President Trump has asserted that the actual number is 6 to 18. The Post article notes that Puerto Rican official had asked researchers at George Washington University to do an estimate of the death toll. That work is not complete. George Washington University study

We sttill don’t know how many people died because of Katrina
by Carl Bialik, FiveThirtyEight, 26 August 2015

These 3 Hurricane Misconceptions Can Be Dangerous. Scientists Want to Clear Them Up.
Misinterpretations of the “Cone of Uncertainty” in Florida during the 2004 Hurricane Season
Definition of the NHC Track Forecast Cone

Remember when a glass of wine a day was good for you? Here's why that changed. Popular Science, 10 September 2018

Googling the news
Economist, 1 September 2018

We sat in on an internal Google meeting where they talked about changing the search algorithm — here's what we learned

Reading , Writing and Risk Literacy

[1]

Today is the deadliest day of the year for car wrecks in the U.S.

Some math doodles

<math>P \left({A_1 \cup A_2}\right) = P\left({A_1}\right) + P\left({A_2}\right) -P \left({A_1 \cap A_2}\right)</math>

<math>P(E) = {n \choose k} p^k (1-p)^{ n-k}</math>

<math>\hat{p}(H|H)</math>

<math>\hat{p}(H|HH)</math>

Accidental insights

My collective understanding of Power Laws would fit beneath the shallow end of the long tail. Curiosity, however, easily fills the fat end. I long have been intrigued by the concept and the surprisingly common appearance of power laws in varied natural, social and organizational dynamics. But, am I just seeing a statistical novelty or is there meaning and utility in Power Law relationships? Here’s a case in point.

While carrying a pair of 10 lb. hand weights one, by chance, slipped from my grasp and fell onto a piece of ceramic tile I had left on the carpeted floor. The fractured tile was inconsequential, meant for the trash.

BrokenTile.jpg

As I stared, slightly annoyed, at the mess, a favorite maxim of the Greek philosopher, Epictetus, came to mind: “On the occasion of every accident that befalls you, turn to yourself and ask what power you have to put it to use.” Could this array of large and small polygons form a Power Law? With curiosity piqued, I collected all the fragments and measured the area of each piece.

Piece Sq. Inches % of Total
1 43.25 31.9%
2 35.25 26.0%
3 23.25 17.2%
4 14.10 10.4%
5 7.10 5.2%
6 4.70 3.5%
7 3.60 2.7%
8 3.03 2.2%
9 0.66 0.5%
10 0.61 0.5%
Montante plot1.png

The data and plot look like a Power Law distribution. The first plot is an exponential fit of percent total area. The second plot is same data on a log normal format. Clue: Ok, data fits a straight line. I found myself again in the shallow end of the knowledge curve. Does the data reflect a Power Law or something else, and if it does what does it reflect? What insights can I gain from this accident? Favorite maxims of Epictetus and Pasteur echoed in my head: “On the occasion of every accident that befalls you, remember to turn to yourself and inquire what power you have to turn it to use” and “Chance favors only the prepared mind.”

Montante plot2.png

My “prepared” mind searched for answers, leading me down varied learning paths. Tapping the power of networks, I dropped a note to Chance News editor Bill Peterson. His quick web search surfaced a story from Nature News on research by Hans Herrmann, et. al. Shattered eggs reveal secrets of explosions. As described there, researchers have found power-law relationships for the fragments produced by shattering a pane of glass or breaking a solid object, such as a stone. Seems there is a science underpinning how things break and explode; potentially useful in Forensic reconstructions. Bill also provided a link to a vignette from CRAN describing a maximum likelihood procedure for fitting a Power Law relationship. I am now learning my way through that.

Submitted by William Montante

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