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;
Friday, September 05, 2008
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
 |
| Topic Alert: 56 New articles Available on ScienceDirect |
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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
| |||||||||||||||||||||||||
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
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.
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
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
- Indispensable for some, meaningless for others ... but what about the patients?
- Not yet
- Dubious value from a public health perspective
Monday, March 24, 2008
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.
- Computer Hope: Computer keyboard shortcut keys, Major Excel shortcut list.
- Microsoft: Keyboard shortcuts in Excel
- Microsoft: Keyboard shortcuts in Word
- Microsoft: Use Keyboard shortcuts to create PowerPoint presentations
- Microsoft: Use keyboard shortcuts to deliver PowerPoint presentations
- 15 Common Symbols You Can Insert With the Alt Key (How to input special characters w/o numpad?)
- 10+ Cool AutoHotkey Scripts & How to Make Your Own
- Switch absolute and relative references: While editing a formula, pressing 'F4' key switches cell references from relative to absolute, to partially absolute, back to relative again: A1 --> $A$1 --> A$1-- > $A1-- > A1
- Sort files in a folder by most recent with folders on top: In a folder, view files by detail, sort by "Date Modified", then <Shift + Click> on "Name", done!
- AutoHotkey: the ultimate automation scripting language for Windows
- The How-To Geek: The Best Time-Saving AutoHotkey Tricks You Should Be Using
- Hotkey Help, we can list all hotkeys using "ListHotkeys"
- Comprehensive list of Windows hotkeys
- My 10 Favorite AutoHotKey Scripts to Make Life Easier
- Jon Peltier Jon's Excel Charts and Tutorials
Subscribe to:
Posts (Atom)