Wednesday, November 19, 2008
Predicting Prediabetes
Predicting Prediabetes
November 11, 2008 — An assessment tool known as Tool to Assess Likelihood of Fasting Glucose ImpairmenT (TAG-IT) is effective in screening patients for prediabetes, according to the results of a study reported in the November/December issue of the Annals of Family Medicine.
"Fifty-four million people in the United States have impaired fasting glucose (IFG); if it is identified, they may benefit from prevention strategies that can minimize progression to diabetes, morbidity, and mortality," write Richelle J. Koopman, MD, MS, from the University of Missouri in Columbia, and colleagues. "We created a tool to identify those likely to have undetected hyperglycemia....We then validated TAG-IT in a second population-based sample, and compared TAG-IT with BMI [body mass index] alone for the ability to predict IFG and undiagnosed diabetes."
Using existing data from the National Health and Nutrition Examination Survey (NHANES) 1999 to 2004, this cross-sectional analysis examined 4045 US adults aged 20 to 64 years who were not diagnosed with diabetes but who had a fasting plasma glucose measurement. The investigators developed a logistic regression model predicting IFG and undiagnosed diabetes from characteristics that are self-reported or measured without laboratory testing. On the basis of this model, TAG-IT was developed, validated with use of NHANES III, and compared with BMI alone. Subsets based on race and ethnicity were also examined.
Factors that were most predictive of IFG and included in the final version of TAG-IT were age, sex, BMI, family history of diabetes, resting heart rate, and history of hypertension (or measured high blood pressure). Area under the curve (AUC) for TAG-IT was 0.740, which was significantly better than BMI alone (AUC, 0.644).
For an aggressive case-finding strategy, a score of 5 or higher yielded 87.0% sensitivity. If high specificity is preferred to minimize additional testing and false-positive results, a score of 8 (78.8% specificity) or 9 (87.9% specificity) could be used.
"The TAG-IT efficiently identifies those most likely to have abnormal fasting glucose and can be used as a decision aid for screening in clinical and population settings, or as a prescreening tool to help identify potential participants for research," the study authors write. "The TAG-IT represents an improvement over BMI alone or a list of risk factors in both its utility in younger adult populations and its ability to provide clinicians and researchers with a strategy to assess the risks of combinations of factors."
Limitations of TAG-IT were that it was developed from cross-sectional data and examines only the present risk for elevated fasting plasma glucose levels vs a future risk for disease, use of only fasting plasma glucose level as an outcome vs impaired glucose tolerance, and race or ethnicity not included as a predictor.
"TAG-IT can be readily and immediately applied in clinical settings, can aid in the identification of potential research participants with IFG, can be widely applied in practices using electronic health records, and can improve the efficiency of population-based screening, including community and Web-based applications," the study authors conclude.
The National Institute on Aging and the Robert Wood Johnson Foundation funded this study. The study authors have disclosed no relevant financial relationships.
Ann Fam Med. 2008;6:555-561.
Wednesday, November 05, 2008
Plastic Water Bottles
Plastic Water Bottles
http://www.thegreenguide.com/doc/101/plastic
Whether you buy bottled water or conscientiously tote some from home,
you'll want to avoid swallowing chemicals along with it. Particularly
for small children, whose bodies are developing, it's best to steer
clear of plastics that can release chemicals that could harm them in the
long term. Below, the plastics not to choose (check the recycling number
on the bottom of your bottle) and those that are safer:
Plastics to Avoid
#3 Polyvinyl Chloride (PVC) commonly contains di-2-ehtylhexyl phthalate
(DEHP), an endocrine disruptor and probable human carcinogen, as a
softener.
#6 Polystyrene (PS) may leach styrene, a possible endocrine disruptor
and human carcinogen, into water and food.
#7 Polycarbonate contains the hormone disruptor bisphenol-A, which can
leach out as bottles age, are heated or exposed to acidic solutions.
Unfortunately, #7 is used in most baby bottles and five-gallon water
jugs and in many reusable sports bottles.
Better Plastics
#1 polyethylene terephthalate (PET or PETE), the most common and easily
recycled plastic for bottled water and soft drinks, has also been
considered the most safe. However, one 2003 Italian study found that the
amount of DEHP in bottled spring water increased after 9 months of
storage in a PET bottle.
#2 High Density Polyethylene
#4 Low Density Polyethylene
#5 Polypropylene
Best Reusable Bottles: Betras USA Sports Bottles, Brita Fill & Go Water
Filtration Bottle, Arrow Canteen
Better Baby Bottles: Choose tempered glass or opaque plastic made of
polypropylene (#5) or polyethylene (#1), which do not contain
bisphenol-A.
Tips for Use:
*Sniff and Taste: If there's a hint of plastic in your water, don't
drink it.
*Keep bottled water away from heat, which promotes leaching of
chemicals.
*Use bottled water quickly, as chemicals may migrate from plastic during
storage. Ask retailers how long water has been on their shelves, and
don't buy if it's been months.
*Do not reuse bottles intended for single use. Reused water bottles also
make good breeding grounds for bacteria.
*Choose rigid, reusable containers or, for hot/acidic liquids, thermoses
with stainless steel or ceramic interiors.
For more info, see Product Reports on "bottled water" and "baby
bottles."
Friday, October 31, 2008
NHANES Tutorials
NHANES Tutorials
http://www.cdc.gov/nchs/tutorials/index.htm
Basic Tutorial
Continuous NHANES tutorial
Everything you want and need to know about analyzing continuous NHANES
data is now available in a web-based product. The Continuous NHANES
Tutorial is designed to help users navigate through the dataset. Users
can browse through different modules to gain insight into NHANES data.
Supplemental Tutorials
NHANES III Tutorial
This tutorial will orient you to NHANES III data, guide you through
preparing an analytic dataset, and explain the nuances of the survey
design. Users already familiar with Continuous NHANES data and
interested in using NHANES III data should use this tutorial. New users
of NHANES data should complete the Continuous NHANES tutorial before
beginning this tutorial.
NHANES II Tutorial
This tutorial will orient you to NHANES II data, guide you through
preparing an analytic dataset, and explain the nuances of the survey
design. Users already familiar with Continuous NHANES data and
interested in using NHANES II data should use this tutorial. New users
of NHANES data should complete the Continuous NHANES tutorial before
beginning this tutorial.
NHANES I Tutorial
This tutorial will orient you to NHANES I data, guide you through
preparing an analytic dataset, and explain the nuances of the survey
design. Users already familiar with Continuous NHANES data and
interested in using NHANES I data should use this tutorial. New users of
NHANES data should complete the Continuous NHANES tutorial before
beginning this tutorial.
Wednesday, October 29, 2008
Developmental biology: Neither fat nor flesh (Brown and white fat)
Brown and white fat
In mammals, white adipose tissue stores fat, whereas brown adipose
tissue burns fat. Brown adipocytes have a common origin with muscle
cells, which could help explain their unusual function.
http://www.nature.com/nature/journal/v454/n7207/full/454947a.html
Thursday, October 16, 2008
Web Live Cameras for Fall Foliage (Autumn Leaf) and Autumn Color, 2008
Web Live Cameras for Fall Foliage (Autumn Leaf) and Autumn Color, 2008
The Rocky Mountains Over Banff- Alberta, Canada - This view just may give you the first view of leaf color turn in aspens in North America. Canada's Banff National Park is seen here. With some luck you will get a clear day and a great view.
Acadia National Park- Maine, USA - View turning leaves on 40,000 acres of Atlantic coast shoreline. Mixed hardwood colors light up the green spruce/fir forest.
Selway-Bitterroot- Montana, USA - The Selway-Bitterroot real-time digital camera system is installed outside of the Stevensville USFS Ranger Station, Montana. The camera views Crown Point, 7 miles to the northwest and overlooks the third largest wilderness in the lower 48 .
Glacier National Park- Montana, USA - There are now six outside digital cameras located in Glacier National Park. You can curser over each link to see an updated quick shot.
Great Smoky Mountains National Park - Look Rock Cam
Great Smoky Mountains National Park - Purchase Knob Cam - North Carolina, USA - The Great Smoky Mountains National Park offers views via Look Rock Tower and Purchase Knob. These digital cameras offer some of the best autumn views of the Smoky Mountains.
Dolly Sods Wilderness- West Virginia, USA - The Dolly Sods Web camera system was installed in the USFS Bearden Knob air quality monitoring compound in November 2003. The camera views the Canaan Valley and Mt. Port Crayon, 13 miles to the south.
The Nation's Capitol- Washington D.C., USA - This web cam view is from the Netherlands Carillon looking east toward the Lincoln Memorial, the Washington Monument and the Capitol Building.
Mt. Washington- New Hampshire, USA - Web cam view of part of the Presidential Range of the White Mountains, the highest mountain in New England.
Brasstown Bald- Georgia, USA - Web cam view from Georgia's highest point near Blairsville.
Mammoth Cave National Park- Kentucky, USA - View of Green River Valley looking north-northwest. The visual range is approximately 15 miles and overlooks a massive upland hardwood forest.
Shining Rock Wilderness, Pisgah National Forest, Near Ashville, North Carolina and the Blue Ridge Parkway - North Carolina, USA - A view of Cold Mountain from the largest wilderness area in North Carolina.
Penn State Campus- Pennsylvania, USA - Great campus fall foliage cams including Mount Nittany and Alumni Gardens.
New England Leaf Peeping Cams- New England, USA - Watch forests change color with About's Go New England Guide.
Old Faithful Geyser Wyoming, Montana, Idaho, USA - View of Old Faithful Geyser in Yellowstone National Park.
Fall Foliage Cams- The Entire USA - Another destination site for fall leaf viewing in North America.
by Steve Nix http://forestry.about.com/od/fallcolor/a/fall_web_cams.htm
Friday, September 05, 2008
How to set decimal place for SUDAAN
How to set decimal place for SUDAAN
There are two ways (in italic):
1) PROC CROSSTAB DATA=EYE DESIGN =WR;
SETENV DECWIDTH=3;
NEST STRATA4 PSU4/MISSUNIT;
WEIGHT MECWGT4;
SUBPOPN POPMEC=1;
CLASS DM2 OPDURL4/NOFREQ;
TABLE DM2*OPDURL4;
PRINT NSUM /*WSUM*/ ROWPER SEROW/STYLE=NCHS WSUMFMT=F10.;
RUN;
2)
PROC CROSSTAB DATA=EYE DESIGN =WR;
NEST STRATA4 PSU4/MISSUNIT;
WEIGHT MECWGT4;
SUBPOPN POPMEC=1;
CLASS DM2 OPDURL4/NOFREQ;
TABLE DM2*OPDURL4;
PRINT NSUM WSUM ROWPER SEROW/STYLE=NCHS WSUMFMT=F10.3;
RUN;
There are two ways (in italic):
1) PROC CROSSTAB DATA=EYE DESIGN =WR;
SETENV DECWIDTH=3;
NEST STRATA4 PSU4/MISSUNIT;
WEIGHT MECWGT4;
SUBPOPN POPMEC=1;
CLASS DM2 OPDURL4/NOFREQ;
TABLE DM2*OPDURL4;
PRINT NSUM /*WSUM*/ ROWPER SEROW/STYLE=NCHS WSUMFMT=F10.;
RUN;
2)
PROC CROSSTAB DATA=EYE DESIGN =WR;
NEST STRATA4 PSU4/MISSUNIT;
WEIGHT MECWGT4;
SUBPOPN POPMEC=1;
CLASS DM2 OPDURL4/NOFREQ;
TABLE DM2*OPDURL4;
PRINT NSUM WSUM ROWPER SEROW/STYLE=NCHS WSUMFMT=F10.3;
RUN;
Friday, August 15, 2008
Translating the A1C Assay Into Estimated Average Glucose Values -- Nathan et al., 10.2337/dc08-0545 -- Diabetes Care
http://care.diabetesjournals.org/cgi/reprint/dc08-0545v1
The results of the A1C-Derived Average Glucose study (ADAG), published
in Diabetes Care, have affirmed the existence of a linear relationship
between A1C and average blood glucose levels.
In light of the study results, ADA is recommending the use of a new term
in diabetes management, estimated average glucose, eAG. Health care
providers can now report A1C results to patients using the same units
(mg/dl or mmol/l) that patients see routinely in blood glucose
measurements.
A1c and average glucose level
A1c and average glucose level
The results of the A1C-Derived Average Glucose study (ADAG), published in Diabetes Care this month, have affirmed the existence of a linear relationship between A1C and average blood glucose levels. Prior studies using limited numbers of meter glucose readings primarily in type 1 Caucasian populations had been used in the past to estimate average glucose. The international ADAG study clarified the very close linkage using about 2700 glucose readings per subject per A1C measurement, and verified that the relationship holds in people with type 1 and type 2 diabetes, of all ages, of both genders, and across ethnic/racial groups. The “new numbers” are somewhat different than those in the old tables of A1C vs. average glucose.
In light of the study results, health care providers can confidently report A1C results to patients using the same units (mg/dl or mmol/l) that patients see routinely in blood glucose measurements. For more information about the ADAG study, a table of A1C and the corresponding estimated average glucose, an eAG calculator, and other materials, go to http://professional.diabetes.org/glucosecalculator.aspx
The relationship between A1C and eAG is described by the formula 28.7 * A1C – 46.7 = eAG.
A1C eAG
% mg/dl mmol/l
6 126 7.0
6.5 140 7.8
7 154 8.6
7.5 169 9.4
8 183 10.1
8.5 197 10.9
9 212 11.8
9.5 226 12.6
10 240 13.4
Monday, August 11, 2008
Physical activity in NHIS
Physical activity in NHIS
The purpose of this site is to describe the history of NHIS adult physical activity questions and provide tools for identifying, accessing, and using NHIS physical activity data, collected since 1975.
http://www.cdc.gov/nchs/about/major/nhis/physicalactivity/physical_activity_homepage.htm
Wednesday, August 06, 2008
Pre-diabetes, offical defined on March 22, 2002
http://www.hhs.gov/news/press/2002pres/20020327.html
-----Original Message-----
In this 2002 article "The Prevention or Delay of Type 2 Diabetes", the
ADA uses IFG and IGT, but not prediabetes.
But in early 2002 the DPP came out, and during 2002 prediabetes was used
pretty widely, including in articles/lettters by Venkat, Mike, Frank.
Don't know when the first use was (possibly much earlier) or who coined
the term.
By 2003, the ADA was using "pre-diabetes" in its clinical practice
guidelines.
The 2002 or 2003 web-only position statement on prediabetes seems to
have disappeared, as you mentioned. Might have a print out somewhere.
In any case, you can say that the term prediabetes, meaning IFG or IGT,
came into wider use in 2002 after release of DPP results.
Thursday, July 31, 2008
ScienceDirect Topic Alert: Diabetes
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| Topic Alert: 56 New articles Available on ScienceDirect |
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| More... Access all 56 new results in ScienceDirect for: pub-date > 2004 AND KEYWORDS (diabet*) OR title (diabet*) OR srctitle (diabet*) |
| Send my e-mail in plain text format |
| Modify or Remove My Alerts |
| Access the ScienceDirect Info site if you have questions about this message or other features of this service. |
| This email has been sent to you by ScienceDirect, a division of Elsevier B.V., Radarweg 29, 1043 NX Amsterdam, The Netherlands, Tel.+31 20 485 3911. ScienceDirect respects your privacy and does not disclose, rent or sell your personal information to any non-affiliated third parties without your consent, except as may be stated in the ScienceDirect online privacy policy. By using email or alert services, you agree to comply with the ScienceDirect Terms and Conditions. To unsubscribe to alert services, please go to the Alerts page. Copyright © 2008 ScienceDirect. All rights reserved. Any unauthorized use, reproduction, or transfer of this message or its contents, in any medium, is strictly prohibited. ScienceDirect® is a registered trademark of Elsevier B.V. Delivery Job ID: 2927:166260207:1927:142094371 |
Tuesday, July 29, 2008
Relation Between Body Mass Index, Waist Circumference, and Death After Acute Myocardial Infarction
Relation Between Body Mass Index, Waist Circumference, and Death After
Acute Myocardial Infarction -- Zeller et al. 118 (5): 482 -- Circulation
Acute Myocardial Infarction -- Zeller et al. 118 (5): 482 -- Circulation
hee-haw, this article suggests we need measure both BMI and WC. There is
no war of BMI and WC anymore. Kidding, but I enjoy the view angle of
this article.
http://circ.ahajournals.org/cgi/content/abstract/118/5/482?etoc
This article suggest we need both BMI and WC.
Conclusions- Neither BMI nor WC independently predicts death after AMI.
Much of the inverse relationship between BMI and the rate of death after
AMI is due to confounding by characteristics associated with survival.
This study emphasizes the need to measure both BMI and WC because
patients with a high WC and low BMI are at high risk of death.
Monday, July 21, 2008
Public-Use NHIS Linked Mortality Files (Lochner K. AJE 168:336-344, 2008).
Public-Use NHIS Linked Mortality Files
<<Lochner_K_08_[NHIS_Mort_Files].pdf>>
Folks,
This paper shows the similarity in findings between using the public-use version of the NHIS Linked Mortality Files (released last September?) and the restricted use data available through the NCHS Research Data Center. The former had modified information that might otherwise be used to identify individuals. The authors ran some models and revealed that results can be reasonably close for their example. However they also stated: "Moreover, caution in using the public-use files is urged for researchers requiring more detail on timing of death or age or when examining the mortality patterns of small subgroups of the population, such as numerically small racial/ethnic minority groups, very old individuals, or young adults. This is particularly the case when cause-specific analyses of such numerically small demographic subgroups are performed." So the paper offers some good (and some bad) news.
Carl
Lochner K, Hummer RA, Bartee S, Wheatcroft G, Cox C. The Public-Use National Health Interview Survey
Linked Mortality Files: Methods of Reidentification Risk Avoidance and Comparative Analysis
Am. J. Epidemiol. 2008 168: 336-344; doi:10.1093/aje/kwn123.
http://aje.oxfordjournals.org/cgi/content/abstract/168/3/336?etoc
The National Center for Health Statistics (NCHS) conducts mortality follow-up for its major population-based
surveys. In 2004, NCHS updated the mortality follow-up for the 1986–2000 National Health Interview Survey
(NHIS) years, which because of confidentiality protections was made available only through the NCHS Research
Data Center. In 2007, NCHS released a public-use version of the NHIS Linked Mortality Files that includes a limited
amount of perturbed information for decedents. The modification of the public-use version included conducting
a reidentification risk scenario to determine records at risk for reidentification and then imputing values for either
date or cause of death for a select sample of records. To demonstrate the comparability between the publicuse
and restricted-use versions of the linked mortality files, the authors estimated relative hazards for all-cause
and cause-specific mortality risk using a Cox proportional hazards model. The pooled 1986–2000 NHIS Linked
Mortality Files contain 1,576,171 records and 120,765 deaths. The sample for the comparative analyses included
897,232 records and 114,264 deaths. The comparative analyses show that the two data files yield very similar
results for both all-cause and cause-specific mortality. Analytical considerations when examining cause-specific
analyses of numerically small demographic subgroups are addressed.
confidentiality; epidemiologic methods; health surveys; longitudinal studies; mortality
Wednesday, July 09, 2008
Ankle Brachial Index
Ankle Brachial Index (ABI)
-----------------------------------------------------------------
Reviews
-----------------------------------------------------------------
Ankle Brachial Index Combined With Framingham Risk Score to Predict
Cardiovascular Events and Mortality: A Meta-analysis
Ankle Brachial Index Collaboration
JAMA 2008;300 197-208
http://jama.ama-assn.org/cgi/content/abstract/300/2/197?etoc
-----------------------------------------------------------------
Reviews
-----------------------------------------------------------------
Ankle Brachial Index Combined With Framingham Risk Score to Predict
Cardiovascular Events and Mortality: A Meta-analysis
Ankle Brachial Index Collaboration
JAMA 2008;300 197-208
http://jama.ama-assn.org/cgi/content/abstract/300/2/197?etoc
Tuesday, July 01, 2008
American Fitness Index
The inaugural data report, "Health and Community Fitness Status of 16 Large Metropolitan Areas," is a snapshot of the state of health and fitness in America's 15 most populous metropolitan areas, plus Greater Indianapolis (the headquarter city of American College of Sports Medicine and WellPoint, Inc.).
Read more about the AFI launch here or download the data report.
Read the story as it was first reported in USA Today.
AFI WEB SITE NOW ONLINE
After a successful launch during the 2008 ACSM Annual Meeting, we also launched the AFI Web site at www.AmericanFitnessIndex.org.
From the site, you can learn more about the program, the methodology for analyzing the data, download the full and metro area-specific reports, and more.
Don't see something on the site you were expecting to find? Let us know by sending an e-mail to afi@acsm.org.
Be sure to bookmark the site and check back for news and updates on the program as we move from the pilot phase to the next level.
Monday, June 23, 2008
From the May 2008 EpiMonitor: Dr. Sharon Schwartz's reading list on individual and population causes of disease
Dr. Sharon Schwartz's reading list on individual and population causes of disease (EpiMonitor May, 2008)
More Reading On The Tension Between An Individual and Population Level Focus in Epidemiology
In sharing her comments about individual and population causes of disease, Columbia University’s Sharon Schwartz kindly shared the reading list from a course she teaches and identified the most directly relevant readings in bold. These references plus a book by Rose are provided below for readers who may wish to examine these issues further.
- Beaglehole R, Bonita R. (1998) Public Health at the Crossroads: Which Way Forward? The Lancet 351: 590-592.
- Diez-Roux AV. (1998) On Genes, Individuals, Society, and Epidemiology. American Journal of Epidemiology 148:1027-1032.
- Krieger N. (1994) Epidemiology and the Web of Causation: Has Anyone Seen the Spider? Social Science and Medicine 39:887-903.
- Levins R. (1997) When Science Fails Us Forests, Trees and People Newsletter 32/33: February, p. 1-18
- Lewontin RC. (2006) The Analysis of Variance and the Analysis of Causes. International Journal of Epidemiology 35:520-525.
- McKinlay, JB. (1997) A Tale Of Three Tails. Invited paper presentation at “Prevention: Contributions from Basic and Applied Research” conference cosponsored by the Office of Behavioral and Social Sciences Research (OBSSR) of the National Institutes of Health (NIH) and the Science Directorate of the American Psychological Association (APA), Chicago, August 15-18, 1997 p.1-23.
- McMichael AJ (1999) Prisoners of the Proximate: Loosening the Constraints on Epidemiology in an Age of Change. American Journal of Epidemiology 149:887-897.
- McMichael AJ. (1995) The Health of Persons, Populations, and Planets: Epidemiology Comes Full Circle. Epidemiology 6:633-636
- Pearce N. (1996) Traditional Epidemiology, Modern Epidemiology, And Public Health. American Journal of Public Health 86: 678-683.
- Rockhill B. (2005) Theorizing about Causes at the Individual Level While Estimating Effects at the Population Level. Epidemiology 16:124-129.
- Rose G. (1985) Sick Individuals and Sick Populations. International Journal of Epidemiology 14:32-38.
- Rose G. (1992) The Strategy of Preventive Medicine. Oxford University Press.
- Schwartz S and Diez-Roux A. (2001). Causes of Incidence and Causes of Cases: A Durkheimian Perspective on Rose. International Journal of Epidemiology 30:435-439.
- Schwartz S, Susser E, Susser M. (1999) A Future For Epidemiology? Annual Review of Public Health 20: 15-33.
- Shy CM. (1997) The Failure of Academic Epidemiology: Witness for the Prosecution. American Journal of Epidemiology 145:479-484.
- Susser M, Susser E. (1996) Choosing a Future for Epidemiology: I. Eras and Paradigms. American Journal of Public Health 86:668-673.
- Susser M, Susser E. (1996) Choosing a Future for Epidemiology: II. From Black Box to Chinese Boxes and Eco-Epidemiology. American Journal of Public Health 86:674-677.
- Wing S. (1998) Whose Epidemiology, Whose Health? International Journal of Health Services 28:241-252.
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
Wednesday, June 04, 2008
Thursday, May 15, 2008
FW: New Tutorial on Quality of Care
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Tuesday, April 15, 2008
LIFEREG null model FPZD ERROR: Floating Point Zero Divide.
LIFEREG null model FPZD ERROR: Floating Point Zero Divide.
Dear Yiling:
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
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