Friday, June 20, 2008

Good sentences for remember

Good sentences for remember

1. Time flies.
 
 时光易逝。

2. Time is money.
 
 一寸光阴一寸金。

3. Time and tide wait for no man.
 
 岁月无情;岁月易逝;岁月不待人。

4. Time tries all.
 
 时间检验一切。

5. Time tries truth.
 
 时间检验真理。

6. Time past cannot be called back again.
 
 光阴一去不复返。

7. All time is no time when it is past.
 
 光阴一去不复返。

8. No one can call back yesterday; Yesterday will not be called again.
 
 昨日不复来。

9. Tomorrow comes never.
 
 切莫依赖明天。

10.One today is worth two tomorrows.
 
 一个今天胜似两个明天。

11.The morning sun never lasts a day.
 
 好景不常;朝阳不能光照全日。

12.Christmas comes but once a year.
 
 圣诞一年只一度。

13.Pleasant hours fly past.
 
 快乐时光去如飞。

14.Happiness takes no account of time.
 
 欢娱不惜时光逝。

15.Time tames the strongest grief.
 
 时间能缓和极度的悲痛。

16.The day is short but the work is much.
 
 工作多,光阴迫。

17.Never deter till tomorrow that which you can do today.
 
 今日事须今日毕,切勿拖延到明天。

18.Have you somewhat to do tomorrow,do it today.
 
 明天如有事,今天就去做。

19.To him that does everything in its proper time,one day is worth three.
 
 事事及时做,一日胜三日。

20.To save time is to lengthen life.
 
 节省时间就是延长生命。

21.Everything has its time and that time must be watched.
 
 万物皆有时,时来不可失。

22.Take time when time cometh,lest time steal away.
 
 时来必须要趁时,不然时去无声息。

23.When an opportunity is neglected,it never comes back to you.
 
 机不可失,时不再来;机会一过,永不再来。

24.Make hay while the sun shines.
  晒草要趁太阳好。

25.Strike while the iron is hot.
 
 趁热打铁。

26.Work today,for you know not how much you may be hindered tomrrow.
 
 今朝有事今朝做,明朝可能阻碍多。

27.Punctuality is the soul of business.
 
 守时为立业之要素。

28.Procrastination is the thief of time.
 
 因循拖延是时间的大敌;拖延就是浪费时间。

29.Every tide hath ist ebb.
 
 潮涨必有潮落时。

30.Knowledge is power.
 
 知识就是力量。

31.Wisdom is more to be envied than riches.
 
 知识可羡,胜于财富。

32.Wisdom is better than gold or silver.
 
 知识胜过金银,

33.Wisdom in the mind is better than money in the hand.
 
 胸中有知识,胜于手中有钱。

34.Wisdom is a good purchase though we pay dear for it.
 
 为了求知识,代价虽高也值得。

35.Doubt is the key of knowledge.
 
 怀疑是知识之钥。

36.If you want knowledge,you must toil for it.
 
 若要求知识,须从勤苦得。

37.A little knowledge is a dangerous thing.
 
 浅学误人。

38.A handful of common sense is worth a bushel of learning.
 
 少量的常识,当得大量的学问。

39.Knowledge advances by steps and not by leaps.
 
 知识只能循序渐进,不能跃进。

40.Learn wisdom by the follies of others.
 
 从旁人的愚行中学到聪明。

41.It is good to learn at another man's cost.
 
 前车可鉴。

42.Wisdom is to the mind what health is to the body.
 
 知识之于精神,一如健康之于肉体。

43.Experience is the best teacher.
 
 经验是最好的教师。

44.Experience is the father of wisdom and memory the mother.
 
 经验是知识之父,记忆是知识之母。

45.Dexterity comes by experience.
 
 熟练来自经验。

46.Practice makes perfect.
 
 熟能生巧。

47.Experience keeps a dear school,but fools learn in no other.
 
 经验学校学费高,愚人旁处学不到。

48. Experience without learning is better than learning without experience.
 
 有经验而无学问,胜于有学问而无经验。

49.Wit once bought is worth twice taught.
 
 由经验而得的智慧,胜于学习而得的智慧;一次亲身的体会,胜过两次学习。

50.Seeing is believing.
 
 百闻不如一见。
From http://www.bcbay.com/forum/bay/bbsviewer.php?trd_id=214996

Thursday, May 15, 2008

FW: New Tutorial on Quality of Care

 
 
 


New on kaiserEDU.org...

Research Tools - Direct access to searchable databases, links to publicly available national surveys and data sources, and easy access to major government websites dealing with health policy.

SmartLinks - Provides "pre-queried" searches on health policy topics from PubMed, Kaisernetwork Daily Reports, Google Uncle Sam, Google Scholar, and NY Academy of Medicine Grey Literature Report.

Tutorial: Measuring Health Care Quality New
In this new narrated slide tutorial, Carolyn Clancy, M.D., director of the Agency for Healthcare Research and Quality in the U.S. Department of Health and Human Services, presents an overview of the state of health care quality in the U.S. She explains how quality is measured, discusses the federal role in tracking and measuring health care quality, suggests opportunities for system improvement, and areas for future research and development.

Related Resources:

 

 
  Please forward this notice to interested colleagues. KaiserEDU.org is a Kaiser Family Foundation website.



Tuesday, April 15, 2008

LIFEREG null model FPZD ERROR: Floating Point Zero Divide.

LIFEREG null model FPZD ERROR: Floating Point Zero Divide.

Dear Yiling:

I have heard back from the LIFEREG developer regarding the FPZD error
with a null model on your data set. If you recall I was able to
overcome the FPZD error and as it turns out the sorting I did (by upper
lower) was helpful with your data. Below, I have edited the text of the
LIFEREG developer's reply slightly for clarity:

+ - - - - - - -
The reason the model converges after some data manipulation is the PROC
SORT step, which rearranges the data. Sometimes order of observations
can influence numerical computations, and it happens in this case that
the rearranged data converges with no error, but the original data
causes a FPZD. This can't be guaranteed to work for any data; it just
happens to work in this case.

The user might try different initial values; e.g. -

model (lower,upper)= / itprint dist=Weibull intercept=1 scale=2;

which works for this data.

The FPZD has been eliminated in the SAS9.2 (Phase 2) release due to some
numerical improvements in the function that computes the Weibull
likelihood. Note that the problem still exists in the current SAS9.2
(Phase 1) release which is available now, but is fixed in the SAS9.2
(Phase 2) release.
+- - - - - - -

Note that specifying initial values can be helpful as indicated in
Example 39.3: Overcoming Convergence Problems by Specifying Initial
Values - -
http://support.sas.com/onlinedoc/913/getDoc/en/statug.hlp/lifereg_sect30
.htm


I hope that helps.


Paul

Monday, March 31, 2008

How useful are normality tests?



How useful are normality tests?
source: graphpad.com


Many data analysis methods (t test, ANOVA, regression) depend on the assumption that data were sampled from a Gaussian distribution. The best way to evaluate how far your data are from Gaussian is to look at a graph and see if the distribution deviates grossly from a bell-shaped normal distribution.
How useful are statistical methods to test for normality? Less useful than you’d guess. Consider these potential problems:
  • Small samples almost always pass a normality test. Normality tests have little power to tell whether or not a small sample of data comes from a Gaussian distribution.
  • With large samples, minor deviations from normality may be flagged as statistically significant, even though small deviations from a normal distribution won’t affect the results of a t test or ANOVA.
  • Decisions about when to use parametric vs. nonparametric tests should usually be made to cover an entire series of analyses. It is rarely appropriate to make the decision based on a normality test of one data set.

I think it is usually a mistake to test every data set for normality, and use the result to decide between parametric and nonparametric statistical tests. But normality tests can help you understand your data, especially when you get similar results in many experiments.


Which normality test is best?

When we added a normality test to Prism several years ago, we selected the one that was best known, the Kolmogorov-Smirnov test. This test compares the cumulative distribution of the data with the expected cumulative Gaussian distribution, and bases its P value on the largest discrepancy.
The Kolmogorov-Smirnov test was designed to compare two experimentally-determined distributions. When testing for normality, you compare an experimental distribution against a hypothetical ideal, so its necessary to apply the Dallal-Wilkinson-Lilliefors correction. Prism versions 4.02 and 4.0b do this, but earlier versions did not.
The Kolmogorov-Smirnov test is based on a simple way to quantify the discrepancy between the observed and expected distributions. It turns out, however, that it is too simple, and doesn't do a good job of discriminating whether or not your data was sampled from a Gaussian distribution. An expert on normality tests, R.B. D’Agostino, makes a very strong statement: “The Kolmogorov-Smirnov test is only a historical curiosity. It should never be used.” (“Tests for Normal Distribution” in Goodness-of-fit Techniques, Marcel Decker, 1986).
In Prism 4.02 and 4.0b, we added two more normality tests to Prism’s column statistics analysis the Shapiro-Wilk normality test and the D’Agostino-Pearson omnibus test.
All three procedures test the same the null hypothesis – that the data are sampled from a Gaussian distribution. The P value answers this question: If the null hypothesis were true, what is the chance of randomly sampling data that deviate as much (or more) from Gaussian as the data we actually collected? The three tests differ in how they quantify the deviation of the actual distribution from a Gaussian distribution.

The Shapiro-Wilk normality test is difficult for nonmathematicians to understand, and it doesn't work well when several values in your data set are the same. In contrast, the D’Agostino-Pearson omnibus test is easy to understand. It first analyzes your data to determine
skewness (to quantify the asymmetry of the distribution) and kurtosis (to quantify the shape of the distribution). It then calculates how far each of these values differs from the value expected with a Gaussian distribution, and computes a single P value from the sum of the squares of these discrepancies. Unlike the Shapiro-Wilk test, this test is not affected if the data contains identical values.

If you to decide use normality tests, I recommend that you stop using the Kolmogorov-Smirnov test, and switch instead to the D’Agostino-Pearson omnibus test. But first rethink whether the normality tests are providing you with useful information.

Tuesday, March 25, 2008

What's new on BMJ.com?

 Internal and external validity of cluster randomised trials
Cluster randomised trials are essential for evaluating certain types of intervention, and there are often strong scientific reasons to conduct them. The extent to which investigators are designing and analysing these trials appropriately has improved, according to this systematic review of recent trials.


RAPID RESPONSES
Should we dump the metabolic syndrome? No
Should we dump the metabolic syndrome? Yes

Wednesday, March 19, 2008

Links to Health ABC sites

Links to Health ABC sites

So that you have it, here is some information about HealthABC.

The first is a link to the main NIH Health ABC site:
     http://www.nia.nih.gov/ResearchInformation/ScientificResources/HealthABCDescription.htm

This site contains a description of the study and further links to tables showing

     1.   Measurements in the study
               http://www.nia.nih.gov/ResearchInformation/ScientificResources/MeasurementsHealthABC.htm,

     2.  Laboratory analytes and repository plan
               http://www.nia.nih.gov/ResearchInformation/ScientificResources/LaboratoryAnalytes.htm

Another "main" location exists for the:

     1.  Health ABC website
               http://www.grc.nia.nih.gov/branches/ledb/healthabc/index.htm

          a.  Analysis Plan Proposal  [PDF]
                    http://www.grc.nia.nih.gov/branches/ledb/healthabc/analysisplan.pdf

          b.  Health ABC Contact Directory  [PDF]
                    http://www.grc.nia.nih.gov/branches/ledb/healthabc/directory.pdf

          c.  Ancillary Study Proposal  [PDF]
                    http://www.grc.nia.nih.gov/branches/ledb/healthabc/ancillaryform.pdf

          d.  Ancillary Studies Guidelines  [PDF]
                    http://www.grc.nia.nih.gov/branches/ledb/healthabc/ancillaryguidelines.pdf

          e.  Measurements in the Study  [PDF]
                    http://www.grc.nia.nih.gov/branches/ledb/healthabc/measurements.pdf

         f.  Publications and Presentations Guidelines  [PDF]
                    http://www.grc.nia.nih.gov/branches/ledb/healthabc/publicationguidelines.pdf

          g.  Health ABC Publications List  [PDF]
                    http://www.grc.nia.nih.gov/branches/ledb/healthabc/publicationslist.pdf


Friday, March 14, 2008

Microsoft Windows Hotkey Shortcut

Hotkeys/Shortcuts - Microsoft Windows, Office, etc.

Wednesday, March 12, 2008

A Thousand Winds

千风之歌 - A Thousand Winds


source: http://ripple1978.spaces.live.com/blog/cns!E3391D08951E33E!1377.entry
86 歲的李登輝首次在鏡頭前暢談生死觀,背景是一個人的教堂。他引用了日文歌曲《千風之歌》裡的一句,一言以蔽之概況了他對死亡的態度,「請別在我的墳前哭 泣,我不在那裡,我沒有沉睡在那裏,我已化為一千個風。」心內有所觸動,剛好前幾日在日本緯來台紅白大賽中截聽了此曲的後半段,旋律哀淡而清透,難得的催 人眩淚後繼爾能挺起胸膛,悲傷而不頹蘼。很震撼一曲詮釋死亡的音樂能帶給聽者這樣的感受。
聞報導的典故源自印弟安一對情侶,女人死後,男子欲相伴辭世循情,在清理愛侶遺物時發現她生前留下的詩篇,意勸情人好好地活下去,果然如亡者所願,男子不 再企圖自殺。但網路搜索的版本卻有更多種。真實已經不再重要。經典的東西總是在時光沉澱之後浸透出更濃郁的況味,超越人等與國度。
 
除了宗教力量,能夠告慰死亡的東西委實不多,很感激有一千個風,在「幸福後死不捨得,不幸時死不甘願;豐收後死不捨得,貧瘠時死不甘願」的生命步仗中,稍稍地卸下那份難於消彌的懼怕。
如果我的愛人先我而死,就當做他猶然活著的耳語,夜夜裏哼唱給我聽。如果我先死,這將是我的遺言,「最好你能忘記我,如果不能,那就唱這首歌。」 

评:千风之歌 李登辉生死观真情告白 凤凰视频 2008/01/08->01/05




千风之歌


请不要伫立在我坟前哭泣
我不在那里  我没有沉睡不醒 
化为千风  我已化身为千缕微风 
翱翔在无限宽广的天空里 
秋天  化身为阳光照射在田地间 
冬天  化身为白雪绽放钻石光芒 
晨曦升起时  幻化为飞鸟轻声唤醒你 
夜幕低垂时  幻化为星辰温柔守护你 
请不要伫立在我坟前哭泣 
我不在那里  我没有离开人间
化为千风  我已化身为千缕微风
翱翔在无限宽广的天空里 
化为千风  我已化身为千缕微风 
翱翔在无限宽广的天空里

化為千風 - Wikipedia
Do not stand at my grave and weep - Wikipedia

英文版──Do not stand at my grave and weep
by Mary Elizabeth Frye



I am not there, I do not sleep
I am a thousand winds that blow.
I am the diamond glints on snow.
I am the sunlight on ripened grain.
I am the gentle autumn rain.
When you awaken in the morning's hush
I am the swift uplifting rush
Of quiet birds in circled flight.
I am the soft stars that shine at night.
Do not stand at my grave and cry;
I am not there. I did not die.


日文版──千の風になって
 私のお墓の前で 泣かないでください
そこに私はいません 眠ってなんかいません
千の風に千の風になって
あの大きな空を吹き渡っています

秋には光になって
畑にふりそそぐ
冬はダイヤのように きらめく雪になる
朝は鳥になって あなたを

目覚めさせる
夜は星になって あなたを見守る私のお墓の前で
泣かないでください
そこに私はいません 死んでなんかいません
中文版──千風之歌
請不要佇立在我的墓前哭泣
因為我並不在那裡
我並沒有沉睡不醒
而是化為千風
我已化身為千縷微風
翱翔在無限寬廣的天空裡

秋天
我化身為陽光
照射在田野間
冬天
我化身為白雪
綻放鑽石般的閃耀光芒

晨曦升起之際
我幻化為飛鳥
輕聲地喚醒你
夜幕低垂之時
我幻化成星辰
溫柔地守護你

請不要佇立在我的墓前哭泣
因為我並不在那裡
我並沒有沉睡不醒
而是化為千風
我已化身為千縷微風
翱翔在無限寬廣的天空裡

I Am A Thousand Winds:
Don't stand at my grave and weep
I am not there, I do not sleep
I am the sunlight on the ripened grain
I am the gentle autumn rain


I am a thousand winds
I am a thousand winds that blow
I am the diamond glint on snow
I am a thousand winds that blow


Don't stand at my grave and cry
I am not there, I did not die
I am the swift rush of birds in flight
Soft stars that shine at night


I am a thousand winds
I am a thousand winds that blow
I am the diamond glint on snow
I am a thousand winds that blow


Don't stand at my grave and weep
I am not there, I do not sleep
I am the sunlight on the ripened grain
I am the gentle autumn rain


I am a thousand winds
I am a thousand winds that blow
I am the diamond glint on snow
I am a thousand winds that blow


I am the diamond glint on snow
I am a thousand winds that blow


  



Other version of wind s



不要站在我的墳前哭泣
我不在那,我未沉眠
我化為千風吹送著
我化為雪中閃耀的鑽石
我化為陽光灑落在成熟稻穀上
我化為綿綿的秋雨
當你在早晨的寧靜中醒來
我化為湍急的溪流
寧靜的鳥兒在上方盤旋
我化為溫柔的星星在夜晚閃耀著
不要站在我的墳前哭泣
我不在那,我還未離世

Reinventing Type 2 Diabetes: Pathogenesis, Treatment, and Prevention -- Unger 299 (10): 1185 -- JAMA

Reinventing Type 2 Diabetes: Pathogenesis, Treatment, and Prevention

JAMA has just put online the article by Professor Unger about the
lipocentric model of diabetes. Below is the direct link to the PDF.

http://elib.cdc.gov:2056/cgi/reprint/299/10/1185

Monday, March 10, 2008

Tips - Stata: -suest- for comparing regression coefficients between models

Tips - Stata: -suest- for  comparing regression coefficients between models
 
I found that 'suest' of Stata is a very useful command for comparing regression coefficients between different (separated) regression models EASILY. The most important, it can deal with complex survey data.
Enjoy!
 
 
suest is a postestimation command; see estcom and postest.

suest
combines the estimation results -- parameter estimates and associated (co)variance matrices -- stored under namelist into one parameter vector and simultaneous (co)variance matrix of the sandwich/robust type. This (co)variance matrix is appropriate even if the estimates were obtained on the same or on overlapping data.

Typical applications of suest are tests for intramodel and cross-model hypotheses using test or testnl, for example, a generalized Hausman specification test. lincom and nlcom may be used after suest to estimate linear combinations and nonlinear functions of coefficients. suest may also be used to adjust a standard VCE for clustering or survey design effects.
Different estimators are allowed, for example, a regress model and a probit model; the only requirement is that predict produce equation-level scores with the score option after an estimation command. The models may be estimated on different samples, due either to explicit if or in selection or to missing values. If weights are applied, the same weights (type and values) should be applied to all models in namelist. The estimators should be estimated without vce(robust) or vce(cluster clustvar) options. suest returns the robust VCE, allows the vce(cluster clustvar) option, and automatically works with results from the svy prefix command (only for vce(linearized)).

Since suest posts its results like a proper estimation command, its results can be stored via estimates store. Moreover, like other estimation commands, suest typed without arguments replays the results.

Friday, March 07, 2008

The Missing Semicolon!

 

New edition of The Missing Semicolon

www.sys-seminar.com/publications_tms.php.

SAS technical newsletter put together by our expert SAS consultants and trainers

 

 

 

Thursday, March 06, 2008

Metabolic Syndrome and Related Disorders Vol. 6, No. 1, Mar 2008 is now available online

 
Liebert Online Table of Contents Alert
Metabolic Syndrome and Related Disorders
Volume: 6, Number: 1 Mar 2008

The above issue is now available online from Liebert Online at:
http://www.liebertonline.com/toc/met/6/1

The table of contents for this issue is listed below. Click on the links below to view the abstract for each article, or click on the link above to read the table of contents online.

If you wish to update your preferences, please visit http://www.liebertonline.com/action/showPreferences?menuTab=Alerts

If you wish to unsubscribe from this alert, please visit http://www.liebertonline.com/action/removeAlert?ai=5aaq&sig=qHfFcznpGvU=

If you need any further help, please email us at e-toc@liebertpub.com.


  Nonalcoholic Steatohepatitis and the Metabolic Syndrome
  Joy Jiang and Natalie Torok
  Metabolic Syndrome and Related Disorders Mar 2008, Vol. 6, No. 1: 1-7.
Abstract | PDF (63 KB) | PDF Plus(102 KB)

  Metabolic Syndrome Status Changes with Fitness Level Change: A Retrospective Analysis
  Mark S. Maxwell, Brian R. Goslin, Ronald L. Gellish, Kenneth R. Hightower, Ronald E. Olson, Virinder K. Moudgil, and Gary D. Russi
  Metabolic Syndrome and Related Disorders Mar 2008, Vol. 6, No. 1: 8-14.
Abstract | PDF (72 KB) | PDF Plus(82 KB)

  Prediabetes and its Relationship with Obesity in Mexican Adults: The Mexican Diabetes Prevention (MexDiab) Study
  Fernando Guerrero-Romero, Martha Rodríguez-Morán, Ricardo Pérez-Fuentes, María C. Sánchez-Guillén, Manuel González-Ortiz, Esperanza Martínez-Abundis, Olga Brito-Zurita, Agustín Madero, Benigno Figueroa, Cristina Revilla-Monsalve, Silvia E. Flores-Martínez, Sergio Islas-Andrade, Ramón A. Rascón-Pacheco, Miguel Cruz, and José Sánchez-Corona
  Metabolic Syndrome and Related Disorders Mar 2008, Vol. 6, No. 1: 15-23.
Abstract | PDF (147 KB) | PDF Plus(126 KB)

  Genetic Variations at the CCAAT/Enhancer-binding Protein delta are Associated with Metabolic Phenotypes in the Japanese Population
  Hidesuke Kaji, Chika Fukano, Yukari Kimura, Etsuko Takiguchi, and Keiko Tanida
  Metabolic Syndrome and Related Disorders Mar 2008, Vol. 6, No. 1: 24-31.
Abstract | PDF (76 KB) | PDF Plus(85 KB)

  Unlimited Energy, Restricted Carbohydrate Diet Improves Lipid Parameters in Obese Children
  Brian S. Dunlap and James R. Bailes, Jr.
  Metabolic Syndrome and Related Disorders Mar 2008, Vol. 6, No. 1: 32-36.
Abstract | PDF (59 KB) | PDF Plus(67 KB)

  Comparisons of Metabolic Syndrome Definitions in Four Populations of the Asia-Pacific Region
  Crystal Man Ying Lee, Rachel R. Huxley, Mark Woodward, Paul Zimmet, Jonathan Shaw, Nam H. Cho, Hyung Rae Kim, Satu Viali, Makoto Tominaga, Dorte Vistisen, Knut Borch-Johnsen, and Stephen Colagiuri
  Metabolic Syndrome and Related Disorders Mar 2008, Vol. 6, No. 1: 37-46.
Abstract | PDF (326 KB) | PDF Plus(145 KB)

  Serum Uric Acid and Components of the Metabolic Syndrome in Non-Diabetic Populations in Mauritian Indians and Creoles and in Chinese in Qingdao, China
  Hairong Nan, Qing Qiao, Stefan Söderberg, Weiguo Gao, Paul Zimmet, Jonathan Shaw, George Alberti, Yanhu Dong, Ulla Uusitalo, Vassen Pauvaday, Pierrot Chitson, and Jaakko Tuomilehto
  Metabolic Syndrome and Related Disorders Mar 2008, Vol. 6, No. 1: 47-57.
Abstract | PDF (142 KB) | PDF Plus(123 KB)

  Metaboli Syndrome in Railway Employees and its Relation to Lifestyle Factors
  G.P. Parale, V.C. Patil, S.P. Patil, S.V. Sabale, C.V. Pethe, G.S. Manjunath, P.M. Kulkarni, V.N. Dhadke, and N.S. Deshpande
  Metabolic Syndrome and Related Disorders Mar 2008, Vol. 6, No. 1: 58-63.
Abstract | PDF (64 KB) | PDF Plus(70 KB)

  Insulin, hs-CRP, Leptin, and Adiponectin. An Analysis of their Relationship to the Metabolic Syndrome in an Obese Population with an Elevated Waist Circumference
  Eric Yan, Steve Chen, Kurt Hong, Woo Sung Kim, Anita Bajpai, Leo Treyzon, Luigi Gratton, Robert Elashoff, He-Jing Wang, Zhaoping Li, and David Heber
  Metabolic Syndrome and Related Disorders Mar 2008, Vol. 6, No. 1: 64-73.
Abstract | PDF (185 KB) | PDF Plus(134 KB)

  Dissertations
  Metabolic Syndrome and Related Disorders Mar 2008, Vol. 6, No. 1: 74-78.
Citation | PDF (57 KB) | PDF Plus(57 KB)

  Abstracts
  Metabolic Syndrome and Related Disorders Mar 2008, Vol. 6, No. 1: 79-83.
Citation | PDF (58 KB) | PDF Plus(58 KB)

Tuesday, March 04, 2008

Friday, February 29, 2008

Cluster trial: Effectiveness of the diabetes education and self management



Thank you Bob for highlighting the 'cluster trial'. Seems they are on the right track of the design of a community interventional trial, aren't they?

_____________________________________________
From:  
Effectiveness of the diabetes education and self management for ongoing and newly diagnosed (DESMOND) programme for people with newly diagnosed type 2 diabetes: cluster randomised controlled trial

M J Davies, S Heller, T C Skinner, M J Campbell, M E Carey, S Cradock, H M Dallosso, H Daly, Y Doherty, S Eaton, C Fox, L Oliver, K Rantell, G Rayman, K Khunti on behalf of the Diabetes Education and Self Management for Ongoing and Newly Diagnosed Collaborative

 << File: davies.pdf >>

Wednesday, February 27, 2008

Interesting article on behaviorial economics


What was I thinking? The latest reasoning about our irrational ways.
by
Elizabeth Kolbert
 
A couple of months ago, I went on-line to order a book. The book had a list price of twenty-four dollars; Amazon was offering it for eighteen. I clicked to add it to my "shopping cart" and a message popped up on the screen. "Wait!" it admonished me. "Add $7.00 to your order to qualify for FREE Super Saver Shipping!" I was ordering the book for work; still, I hesitated. I thought about whether there were other books that I might need, or want. I couldn't think of any, so I got up from my desk, went into the living room, and asked my nine-year-old twins. They wanted a Tintin book. Since they already own a large stack of Tintins, it was hard to find one that they didn't have. They scrolled through the possibilities. After much discussion, they picked a three-in-one volume containing two adventures they had previously read. I clicked it into the shopping cart and checked out. By the time I was done, I had saved The New Yorker $3.99 in shipping charges. Meanwhile, I had cost myself $12.91.


Why do people do things like this? From the perspective of neoclassical economics, self-punishing decisions are difficult to explain. Rational calculators are supposed to consider their options, then pick the one that maximizes the benefit to them. Yet actual economic life, as opposed to the theoretical version, is full of miscalculations, from the gallon jar of mayonnaise purchased at spectacular savings to the billions of dollars Americans will spend this year to service their credit-card debt. The real mystery, it could be argued, isn't why we make so many poor economic choices but why we persist in accepting economic theory.
In "Predictably Irrational: The Hidden Forces That Shape Our Decisions" (Harper; $25.95), Dan Ariely, a professor at M.I.T., offers a taxonomy of financial folly. His approach is empirical rather than historical or theoretical. In pursuit of his research, Ariely has served beer laced with vinegar, left plates full of dollar bills in dorm refrigerators, and asked undergraduates to fill out surveys while masturbating. He claims that his experiments, and others like them, reveal the underlying logic to our illogic. "Our irrational behaviors are neither random nor senselessthey are systematic," he writes. "We all make the same types of mistakes over and over." So attached are we to certain kinds of errors, he contends, that we are incapable even of recognizing them as errors. Offered FREE shipping, we take it, even when it costs us.


As an academic discipline, Ariely's fieldbehavioral economicsis roughly twenty-five years old. It emerged largely in response to work done in the nineteen-seventies by the Israeli-American psychologists Amos Tversky and Daniel Kahneman. (Ariely, too, grew up in Israel.) When they examined how people deal with uncertainty, Tversky and Kahneman found that there were consistent biases to the responses, and that these biases could be traced to mental shortcuts, or what they called "heuristics." Some of these heuristics were pretty obviouspeople tend to make inferences from their own experiences, so if they've recently seen a traffic accident they will overestimate the danger of dying in a car crashbut others were more surprising, even downright wacky. For instance, Tversky and Kahneman asked subjects to estimate what proportion of African nations were members of the United Nations. They discovered that they could influence the subjects' responses by spinning a wheel of fortune in front of them to generate a random number: when a big number turned up, the estimates suddenly swelled.


Though Tversky and Kahneman's research had no direct bearing on economics, its implications for the field were disruptive. Can you really regard people as rational calculators if their decisions are influenced by random numbers? (In 2002, Kahneman was awarded a Nobel PrizeTversky had died in 1996for having "integrated insights from psychology into economics, thereby laying the foundation for a new field of research.")
Over the years, Tversky and Kahneman's initial discoveries have been confirmed and extended in dozens of experiments. In one example, Ariely and a colleague asked students at M.I.T.'s Sloan School of Management to write the last two digits of their Social Security number at the top of a piece of paper. They then told the students to record, on the same paper, whether they would be willing to pay that many dollars for a fancy bottle of wine, a not-so-fancy bottle of wine, a book, or a box of chocolates. Finally, the students were told to write down the maximum figure they would be willing to spend for each item. Once they had finished, Ariely asked them whether they thought that their Social Security numbers had had any influence on their bids. The students dismissed this idea, but when Ariely tabulated the results he found that they were kidding themselves. The students whose Social Security number ended with the lowest figures00 to 19were the lowest bidders. For all the items combined, they were willing to offer, on average, sixty-seven dollars. The students in the second-lowest group20 to 39were somewhat more free-spending, offering, on average, a hundred and two dollars. The pattern continued up to the highest group80 to 99whose members were willing to spend an average of a hundred and ninety-eight dollars, or three times as much as those in the lowest group, for the same items.
This effect is called "anchoring," and, as Ariely points out, it punches a pretty big hole in microeconomics. When you walk into Starbucks, the prices on the board are supposed to have been determined by the supply of, say, Double Chocolaty Frappuccinos, on the one hand, and the demand for them, on the other. But what if the numbers on the board are influencing your sense of what a Double Chocolaty Frappuccino is worth? In that case, price is not being determined by the interplay of supply and demand; price is, in a sense, determining itself.


Another challenge to standard economic thinking arises from what has become known as the "endowment effect." To probe this effect, Ariely, who earned one of his two Ph.D.s at Duke, exploited the school's passion for basketball. Blue Devils fans who had just won tickets to a big game through a lottery were asked the minimum amount that they would accept in exchange for them. Fans who had failed to win tickets through the same lottery were asked the maximum amount that they would be willing to offer for them.
"From a rational perspective, both the ticket holders and the non-ticket holders should have thought of the game in exactly the same way," Ariely observes. Thus, one might have expected that there would be opportunities for some of the lucky and some of the unlucky to strike deals. But whether or not a lottery entrant had been "endowed" with a ticket turned out to powerfully affect his or her sense of its value. One of the winners Ariely contacted, identified only as Joseph, said that he wouldn't sell his ticket for any price. "Everyone has a price," Ariely claims to have told him. O.K., Joseph responded, how about three grand? On average, the amount that winners were willing to accept for their tickets was twenty-four hundred dollars. On average, the amount that losers were willing to offer was only a hundred and seventy-five dollars. Out of a hundred fans, Ariely reports, not a single ticket holder would sell for a price that a non-ticket holder would pay.


Whatever else it accomplishes, "Predictably Irrational" demonstrates that behavioral economists are willing to experiment on just about anybody. One of the more compelling studies described in the book involved trick-or-treaters. A few Halloweens ago, Ariely laid in a supply of Hershey's Kisses and two kinds of Snickersregular two-ounce bars and one-ounce miniatures. When the first children came to his door, he handed each of them three Kisses, then offered to make a deal. If they wanted to, the kids could trade one Kiss for a mini-Snickers or two Kisses for a full-sized bar. Almost all of them took the deal and, proving their skills as sugar maximizers, opted for the two-Kiss trade. At some point, Ariely shifted the terms: kids could now trade one of their three Kisses for the larger bar or get a mini-Snickers without giving up anything. In terms of sheer chocolatiness, the trade for the larger bar was still by far the better deal. But, faced with the prospect of getting a mini-Snickers for nothing, the trick-or-treaters could no longer reckon properly. Most of them refused the trade, even though it cost them candy. Ariely speculates that behind the kids' miscalculation was anxiety. As he puts it, "There's no visible possibility of loss when we choose a FREE! item (it's free)." Tellingly, when Ariely performed a similar experiment on adults, they made the same mistake. "If I were to distill one main lesson from the research described in this book, it is that we are all pawns in a game whose forces we largely fail to comprehend," he writes.


A few weeks ago, the Bureau of Economic Analysis released its figures for 2007. They showed that Americans had collectively amassed ten trillion one hundred and eighty-four billion dollars in disposable income and spent very nearly all of itten trillion one hundred and thirty-two billion dollars. This rate of spending was somewhat lower than the rate in 2006, when Americans spent all but thirty-nine billion dollars of their total disposable income.


According to standard economic theory, the U.S. savings rate also represents rational choice: Americans, having reviewed their options, have collectively resolved to spend virtually all the money that they have. According to behavioral economists, the low savings rate has a more immediate explanation: it provesyet againthat people have trouble acting in their own best interests. It's worth noting that Americans, even as they continue to spend, say that they should be putting more money away; one study of participants in 401(k) plans found that more than two-thirds believed their savings rate to be "too low."
In the forthcoming "Nudge: Improving Decisions About Health, Wealth, and Happiness" (Yale; $25), Richard H. Thaler and Cass R. Sunstein follow behavioral economics out of the realm of experiment and into the realm of social policy. Thaler and Sunstein both teach at the University of Chicago, Thaler in the graduate school of business and Sunstein at the law school. They share with Ariely the belief that, faced with certain options, people will consistently make the wrong choice.Therefore, they argue, people should be offered options that work with, rather than against, their unreasoning tendencies. These foolish-proof choices they label "nudges." (A "nudge," they note with scholarly care, should not be confused with a "noodge.")


A typical "nudge" is a scheme that Thaler and Sunstein call "Save More Tomorrow." One of the reasons people have such a hard time putting money away, the authors say, is that they are loss-averse. They are pained by any reduction in their take-home payeven when it's going toward their own retirement. Under "Save More Tomorrow," employees commit to contributing a greater proportion of their paychecks to their retirement over time, but the increases are scheduled to coincide with their annual raises, so their paychecks never shrink. (The "Save More Tomorrow" scheme was developed by Thaler and the U.C.L.A. economist Shlomo Benartzi, back in 1996, and has already been implemented by several thousand retirement plans.)


People aren't just loss-averse; they are also effort-averse. They hate having to go to the benefits office, pick up a bunch of forms, fill them out, and bring them all the way back. As a consequence, many eligible employees fail to enroll in their companies' retirement plans, or delay doing so for years. (This is the case, research has shown, even at companies where no employee contribution is required.) Thaler and Sunstein propose putting this sort of inertia to use by inverting the choice that's presented. Instead of having to make the trip to the benefits office to opt in, employees should have to make that trip only if they want to opt out. The same basic argument holds whenever a so-called default option is provided. For instance, most states in the U.S. require that those who want to become organ donors register their consent; in this way, many potential donors are lost. An alternativeused, for example, in Austriais to make consent the default option, and put the burden of registering on those who do not wish to be donors. (It has been estimated that if every state in the U.S. simply switched from an "explicit consent" to a "presumed consent" system several thousand lives would be saved each year.)


"Nudges" could also involve disclosure requirements. To discourage credit-card debt, for instance, Thaler and Sunstein recommend that cardholders receive annual statements detailing how much they have already squandered in late fees and interest. To encourage energy conservation, they propose that new cars come with stickers showing how many dollars' worth of gasoline they are likely to burn through in five years of driving.
Many of the suggestions in "Nudge" seem like good ideas, and even, as with "Save More Tomorrow," practical ones. The whole project, though, as Thaler and Sunstein acknowledge, raises some pretty awkward questions. If the "nudgee" can't be depended on to recognize his own best interests, why stop at a nudge? Why not offer a "push," or perhaps even a "shove"? And if people can't be trusted to make the right choices for themselves how can they possibly be trusted to make the right decisions for the rest of us?
Like neoclassical economics, much democratic theory rests on the assumption that people are rational. Here, too, empirical evidence suggests otherwise. Voters, it has been demonstrated, are influenced by factors ranging from how names are placed on a ballot to the jut of a politician's jaw. A 2004 study of New York City primary-election results put the advantage of being listed first on the ballot for a local office at more than three per centenough of a boost to turn many races. (For statewide office, the advantage was around two per cent.) A 2005 study, conducted by psychologists at Princeton, showed that it was possible to predict the results of congressional contests by using photographs. Researchers presented subjects with fleeting images of candidates' faces. Those candidates who, in the subjects' opinion, looked more "competent" won about seventy per cent of the time.


When it comes to public-policy decisions, people exhibit curiousbut, once again, predictablebiases. They value a service (say, upgrading fire equipment) more when it is described in isolation than when it is presented as part of a larger good (say, improving disaster preparedness). They are keen on tax "bonuses" but dislike tax "penalties," even though the two are functionally equivalent. They are more inclined to favor a public policy when it is labelled the status quo. In assessing a policy's benefits, they tend to ignore whole orders of magnitude. In an experiment demonstrating this last effect, sometimes called "scope insensitivity," subjects were told that migrating birds were drowning in ponds of oil. They were then asked how much they would pay to prevent the deaths by erecting nets. To save two thousand birds, the subjects were willing to pay, on average, eighty dollars. To save twenty thousand birds, they were willing to pay only seventy-eight dollars, and to save two hundred thousand birds they were willing to pay eighty-eight dollars.
What is to be done with information like this? We can try to become more aware of the patterns governing our blunders, as "Predictably Irrational" urges. Or we can try to prod people toward more rational choices, as "Nudge" suggests. But if we really are wired to make certain kinds of mistakes, as Thaler and Sunstein and Ariely all argue, we will, it seems safe to predict, keep finding new ways to make them. (Ariely confesses that he recently bought a thirty-thousand-dollar car after reading an ad offering FREE oil changes for the next three years.)


If there is any consolation to take from behavioral economicsand this impulse itself probably counts as irrationalit is that irrationality is not always altogether a bad thing. What we most value in other people, after all, has little to do with the values of economics. (Who wants a friend or a lover who is too precise a calculator?) Some of the same experiments that demonstrate people's weak-mindedness also reveal, to use a quaint term, their humanity. One study that Ariely relates explored people's willingness to perform a task for different levels of compensation. Subjects were willing to help outmoving a couch, performing a tedious exercise on a computerwhen they were offered a reasonable wage. When they were offered less, they were less likely to make an effort, but when they were asked to contribute their labor for nothing they started trying again. People, it turns out, want to be generous and they want to retain their dignityeven when it doesn't really make sense.