This is my test run using GoogleVis, a R! package

My test-run using GoogleVis, a R! package

This week, I attended a R! training class instructed by Dr. Aedin Culhane, a wonderful teacher. I was taught that I can use the GoogleVis, a R! package, to draw a moving bubble plot. It's amazing. Here is the output we used in the class.
    R! Codes: library(googleVis)
             M <- gvisMotionChart(Fruits, "Fruit", "Year")
             plot(M)
             cat(M$html$chart, file="tmp.html")
Here is the more information about: Using the Google Visualisation API with R

Data: Fruits • Chart ID: MotionChartIDed077347ee8
R version 2.15.0 (2012-03-30) • googleVis-0.2.16 • Google Terms of Use • Data Policy

FW: A Firm Diagnosis of Frailty - NYTimes.com

A Firm Diagnosis of Frailty
Source: NYTimes.com


... After years of observing and working with older patients, Dr. Fried recognized objective hallmarks of frailty, and in the 1990s developed a definition of frailty in people 65 and older that called for the diagnosis when three or more of the following five criteria were present: unintentional weight loss of 10 pounds or more in the past year, self-reported exhaustion, weakness as measured by grip strength, slow walking speed and low physical activity. The presence of one or two of those criteria would identify a person as "pre-frail."

Update on the alternative to HOMA-IR and HOMA-b

Update on the HOMA-IR and HOMA-b
Source:  iHOMA2
iHOMA2 is an interactive, 24-variable, HOmeostatic Model of Assessment with the baseline default characteristics of the HOMA2 computer model of fasting insulin:glucose interaction. iHOMA2 enables the mathematical functions describing the organs and tissues involved in the glucose and hormonal compartments to be modified using simple visual analogue controls. The model can be used to evaluate therapeutic agents and predict effects on fasting glucose and insulin and on beta cell function and insulin sensitivity.

Here is the main URL for the iHOMA2 website.

I am not a fan of iHOMA2; I post it here as a future reference.

Joslin's Diabetes Deskbook, A Guide for Primary Care Providers, Updated 2nd Ed., Excerpt 2: Do You Know the Conditions that May Cause Inaccurate Results from the A1C Test?

Joslin's Diabetes Deskbook, A Guide for Primary Care Providers, Updated 2nd Ed., Excerpt 2: Do You Know the Conditions that May Cause Inaccurate Results from the A1C Test?
By Richard S. Beaser, M.D.


"... With respect to the testing methodology, when HPLC laboratory techniques are used to perform measurements, the number of things that can affect the test results is limited. Using this methodology, the most common factors that can affect A1C measurements are:

• Hemolytic anemias Carbamylated and acetylated hemoglobins (rare).
• "Fast" migrating hemoglobins, most commonly hemoglobins D, J, and N, can lower readings.
• Fetal hemoglobin greater than 25% interferes with hemoglobin A1c measurement and cannot be corrected for.
• True beta-thalassemia will interfere with some HPLC methods, but the patient has to be symptomatic at the time for the effect to be significant.
• Severe lipemia in some patients can interfere with measurements. Interference can be reduced by washing red cells and making an offline dilution to report out the A1C value
• Taking medications such as salicylates can have an effect, though rarely ..."

Fatty Liver Disease in Diabetes: Good and Bad?

Fatty Liver Disease in Diabetes: Good and Bad?


When the enzyme called histone deacetylase 3 (HDAC3) was removed, the mice had massively fatty livers, but lower blood sugar, and were thus protected from glucose intolerance and insulin resistance....


The findings demonstrate that fat itself is not necessarily all bad. "It matters a lot how fat is handled and stored," notes Lazar. "It also highlights the importance of complying with our internal circadian clock. For example, since our body does not anticipate food at night and is preparing to generate more glucose, night-time eating is likely to shoot up blood sugar and thus may contribute to diabetes."


The full text here.
The original article here.

Metabolic surgery for type 2 diabetes and obesity related - Nature Medicine

Metabolic surgery for type 2 diabetes

David E Cummings

Clinicians note that bariatric operations can dramatically resolve type 2 diabetes, often before and out of proportion to postoperative weight loss. Now two randomized controlled trials formally show superior results from surgical compared with medical diabetes care, including among only mildly obese patients. The concept of 'metabolic surgery' to treat diabetes has taken a big step forward.

Full Text | PDF

Topic: Guts over glory - why diets fail

Rachel Larder and Stephen O'Rahilly

Losing weight can pose a challenge, but how to avoid putting those pounds back on can be a real struggle. A major health problem for obese people is that diseases linked to obesity, such as type 2 diabetes and cardiovascular disease, put their lives at risk, even in young individuals. Although bariatric surgery[mdash]a surgical method to reduce or modify the gastrointestinal tract[mdash]was originally envisioned for the most severe cases of obesity, evidence suggests that the benefit of this procedure may not be limited to the staggering weight loss it causes. Endogenous factors released from the gut, and modified after surgery, may explain why bariatric surgery can be beneficial for obesity-related diseases and why operated individuals successfully maintain the weight loss. In 'Bedside to Bench,' Rachel Larder and Stephen O'Rahilly peruse a human study with dieters who regained weight despite a successful diet. Appetite-regulating hormones in the gut may be responsible for this relapse in the long term. In 'Bench to Bedside,' Keval Chandarana and Rachel Batterham examine how two different methods of bariatric surgery highlight the relevance of gut-derived hormones not only in inducing sustained weight loss but also in improving glucose homeostasis. These insights may open new avenues to bypass the surgery and obtain the same results with targeted drugs.

Full Text | PDF

Topic: Metabolic insights from cutting the gut

Keval Chandarana and Rachel L Batterham

Losing weight can pose a challenge, but how to avoid putting those pounds back on can be a real truggle. A major health problem for obese people is that diseases linked to obesity, such as type 2 diabetes and cardiovascular disease, put their lives at risk, even in young individuals. Although bariatric surgery[mdash]a surgical method to reduce or modify the gastrointestinal tract[mdash]was originally envisioned for the most severe cases of obesity, evidence suggests that the benefit of this procedure may not be limited to the staggering weight loss it causes. Endogenous factors released from the gut, and modified after surgery, may explain why bariatric surgery can be beneficial for obesity-related diseases and why operated individuals successfully maintain the weight loss. In 'Bedside to Bench,' Rachel Larder and Stephen O'Rahilly peruse a human study with dieters who regained weight despite a successful diet. Appetite-regulating hormones in the gut may be responsible for this relapse in the long term. In 'Bench to Bedside,' Keval Chandarana and Rachel Batterham examine how two different methods of bariatric surgery highlight the relevance of gut-derived hormones not only in inducing sustained weight loss but also in improving glucose homeostasis. These insights may open new avenues to bypass the surgery and obtain the same results with targeted drugs.

Full Text | PDF 

Stata: useful and Interesting user-written ado programs

Stata: Useful and Interesting User-written ado Programs/packages
Manage the ado programs

• Stata Syntax-highlighting for Notepad++ or download from the Notepad++ website
• Syntax Highlight Packages (Dynamic documents in Stata: MarkDoc, Ketchup, and Weaver): Synlight, Weaver, MarkDoc
• Determine build-in command or ado-file: which command
• Check codes: viewsource command or type "directory\xxx.ado"
• list of the packages: ado dir
• Install: ssc install package [,replace]
• ssc describe { pkgname | letter }: Describe a specified package at SSC
• check update: adooupdate
• update: adoupdate, update
• uninstall: ado uninstall package|[#]
• re-run profile.do: do profile.do

A list of ado packages I am using (. ssc install -package-)

• -groups- Stata module to list group frequencies. Speaking Stata (2017): Tables as Lists: The Groups Command. groups updated on SSC
• -fre- Stata module to display one-way frequency table
• -vioplot- Stata module to produce violin plots with current graphics
• -qqplot3- Stata module to plot the unweighted and weighted quantiles of one variable against the corresponding quantiles of another
• -qreg2- Stata module to perform quantile regression with robust and clustered standard errors
• -intcens- Stata module to perform interval-censored survival analysis, which is a program that fits parametric _continuous_ survival time distributions to interval-censored survival time data. The program doesn't allow
  time-varying covariates.
• -midiagplots-, a user-written ado, makes diagnostic plots for multiple imputations created by mi impute.
• -fitstat-, a user-written program is to compute fit statistics for single equation regression models.
• -mgof-, a user-written goodness-of-fit tests for the distribution/multinomial data, can deals with the survey design svy(). Jann (2008).Multinomial goodness–of–fit
• -relrank- is to generate relative data (Relative Distribution Methods in Stata)
• -svr- is a user-written program to comupte estimates with survey replication based standard errors. The -survwgt- command creates brr, jk1, jk2 and jkn replicate weights.
• -somersd- is a package to calculate Kendall's tau-a, Somers'D, AUC (Harrell's c). It can deal with the complex survey design. UCLA idre
• -st0314- Attributable and unattributable risks and fractions and other scenario comparisons (can deal with complex sampled data)
• -svylorenz-, -sumdist- Jenkins (2006): calculate Gini/Lorenz/inequality from complex survey data, summary of income distributions
• -stpm2- (slides), a extension of - stpm -, include improvement in the way time-dependent covariates are modeled; the ability to incorporate expected mortality and thus fit relative survival models; and a superior predict command that enables simple quantification of differences between any two covariate patterns through calculation of time-dependent hazard ratios, hazard differences, and survival differences. stpm has a option "left(leftvar)" to dealing with interval-censored data, and stpm2 does not have an option to deal with interval censoring (checked @ 04/016/2016, wish they could have added it).
• -tabstat- Stata module to produce and export table of results in a matricial format.
• -univar- How can I get descriptive statistics and the five number summary on one line?
• -outreg2- The 'outreg2' is based on the earlier package 'outreg' but has much more features.
• -tabout- is a table building program for oneway and twoway tables of frequencies and percentages, and for summary tables.
• -estout- How can I use -estout- to make regression tables that look like those in journal articles?
• -renvar- Stata module to rename variables
• -wridit- Stata module to generate weighted ridits
• -gtools- Faster Stata for big data. This packages uses C plugins and hashes to provide a massive speed improvements to common Stata commands.
• -ftools- A faster Stata for large datasets (Sergio's slides)
• -psmatch2- implements full Mahalanobis matching and a variety of propensity score matching methods
• -psmatch2- vs. - teffects psmatch- 

Other Resources for adding features to Stata by Stata

R Is Not Enough For "Big Data"

R Is Not Enough For "Big Data"

by Douglas Merrill

“… // Side note 1: I was an undergraduate at the University of Tulsa, not a school that you’ll find listed on any list of the best undergraduate schools.  I did pretty well at Princeton in my doctoral studies.  I’ve hired a lot of people from “bad” schools — like Washington State University — that have been very successful.  Although school is a decent proxy for intellectual horsepower, it’s only a proxy — I believe that the top 1% at any school will likely be pretty awesome.  The hard part is finding that 1%, because there’s likely a material difference between the mean of a second-rate school and the mean of a, say, Harvard. //

// Side note 3: OK, I’m about to take some real liberties with the math here, to help make my point.  All the real mathematicians out there are going to experience almost uncontrollable body twitches over the next few paragraphs.  Breathe deeply, it will pass.  //

// Side note 3: There are all kinds of mathematical problems with most regression models, notably that few things are linearly related and that many things have “correlated errors”, but I’ll leave that to Wikipedia if you’re interested. // …”

Full text here

In pursuit of scientific excellence: sex matters

In pursuit of scientific excellence: sex matters
by Virginia M. Miller
The comments of this article is from the perspective of biology, but may help us to understand existed sex/gender arguments from the perspective of public health.
"...
In the era of physiological genomics and individualized medicine, the presence of an XX or XY chromosomal complement is fundamental to the genome of an individual person, animal, tissue, or cell. Every cell has a sex.
Therefore, based on existing knowledge, it is inappropriate to assume that results from studies conducted on only one sex apply to the other (13). For some studies of neonates and embryos, cells derived from males and females are mixed in a single culture and should be reported as such. The scientific community needs to determine whether this technique is valid by providing sufficient data to control and confirm survival, differentiation, and function of cells of each sex. Similarly, cell-based therapies need to validate survival and function of the cell graft in the same- and opposite-sexed recipients.
...
How then should the sex of experimental material be reported? Use of the terms "sex" and "gender" has evolved over the last decade. According to definitions proposed by the Institute of Medicine (23), "sex" is a biological construct dictated by the presence of sex chromosomes and in animals and humans the presence of functional reproductive organs. "Gender" is a cultural construct and refers to behaviors that might be directed by specific stimuli (visual, olfactory, etc.) or by psychosocial expectations that result from assigned or perceived sex. Gender, thus, can influence biological outcomes. In most studies conducted on isolated cells, tissues can be classified as male or female by the sex chromosomal complement and for experimental animals by the sex chromosomal complement and anatomical features. Similar information may be available for humans. However, humans may self-report their sex according to gender and some studies in animals can be designed to address influences of psychosocial (gender) constructs on physiological outcomes (12). The new editorial policy for all APS journals requires the reporting of sex for cells, tissues, and experimental animals and humans (i.e., male and female) or gender where appropriate. The investigator must decide based on the experimental design which terms are most appropriate for a given study.
..."
Full text of article

In defence of white rice | BMJ

In defence of white rice
by Kadoch
The finding of an increased risk of type 2 diabetes with higher consumption of white rice1 is not surprising because suboptimal results are to be expected whenever a whole plant food is refined. This is especially true with other poor lifestyle practices. Nevertheless, I worry that we are losing the forest among the trees.
White rice has been the staple of the Asian diet for thousands of years. For most of that time it produced some of the most slender people in history. Western diseases such as diabetes and coronary artery disease were almost unheard of in this region.2 Only after the comparatively recent adoption of high fat Western dietary habits, focused primarily on animal products and highly processed junk foods, have these illnesses become more prevalent in Asia.
Diets centred on white rice have, in fact, produced some of the most dramatic health benefits reported in the medical literature. The rice diet, as pioneered by Walter Kempner, has repeatedly been shown to drastically reduce hypertension, insulin resistance, and obesity.3 Low fat diets emphasising starch have reversed diabetes and coronary artery disease.4 5 These remarkable studies were all inspired by the traditional Asian cuisine.
Encouraging patients to choose intact whole grains such as brown rice is certainly warranted. However, to rescue the Asian population from a mounting epidemic of chronic lifestyle diseases, most effort should be focused on removing the cause-the toxic Western diet. This may even justify promoting a return to white rice, instead of condemning it outright.
Full text of article

R Tips

R! Tips

• R Tips: A lot of R tips - Paul E. Johnson  the latest html version is here and the PDF version is here.
• R Tips and Links - Howard Seltman.
• Set user and default R library folder when R starting?
• in RStudio, which is my favorite open source integrated development environment (IDE) for R
• in the R console, type ".libPaths()" to find the default library folder(s).
• go to "\RStudio\R".
• edit file "Options.R".
• add ".libPaths(c("../RStudio/R/library","x:/MyRLib"))".
• save the change.
• Find argument in a function using "args()" for example: args(ls). The three-dots construct in R
• Set user's R library folders as the first library after R started?
• in the R console, type ".libPaths(c("xxx/MyRLibrary1"," xxx/MyRLibrary2"))"
• or, in the RStudio, go to the "\R\" under the RStudio, edit file "Options.R", add one line ".libPaths(c("c:/myRlibsxxxx", .libPaths())", and save it.
• Customize startup of R! (Where to setup R startup environment)
• you can change options through RStudio File -> Tools - > Options
• or,
• open or create file named 'Rprofile.site' under the "..\etc\"
• add options line you want:
• options(digits = 5)           # to set decimal place
• option(width = 100)
• ".First" function will run at the start of the R: 
• .First<-function() {library(a); library(b)}
• ".Last" function will run at the end of the session ( I don't use this usually): 
• .Last <-function(){
• xxx
• }. 
• Do Basic Calculus Using R!
• RMarkdown: Generate a color-coded/formatted Word file of R! using RStudio
• 10 Tips for making your R graphics look their best
• Quantile Regression for Complex Survey Data
• Piecewise/Segmented Regression Related
• How to get orthogonal polynomial coefficient/vector/codes

Diabetes diet: What to eat when you have diabetes - Chicago Tribune

Decoding the diabetic diet
Source: Chicago Tribune.


"A focus on carb- and portion-control should be top priority, but that doesn't mean the occasional treat is out of the question."


Eat more

• Fish
• Nuts
• Nonstarchy vegetables
• Magnesium-rich foods (spinach, almonds, broccoli, lentils, tofu, pumpkin seeds, sunflower seeds)
• Foods rich in omega-3s (flaxseed, walnuts, salmon, tuna, sardines)
• Whole grains (quinoa, brown rice, wild rice, amaranth)
• Whole fruit (in servings the size of a tennis ball)
• Nonfat or low-fat Greek yogurt
• Olive oil
• Cinnamon
• Vinegar

Eat less

• Stick margarine, butter, shortening or lard
• Fried foods
• Refined grains (white bread, white rice, white flour)
• Sugary drinks (soda, fruit juices, sweetened ice teas, sports drinks)
• Fruity yogurts
• High-fat meats (sausage, bacon, hot dog, scrapple)

Full text here.

Matrix of Stata

Matrix of Stata

• Define a -matrix- or -mat- in short: matrix matrixname = matrix_expression.
• matrix define mymat = (1,2\3,4)
• Or, matrix mymat = (1,2\3,4)
• mat list mymat
• Print a matrix: 
• matrix list matrix_name
• -matlist-: Display a matrix and control its format: 
• matlist r(table)'*100, tw(20) format(%8.2f)
• Matrix rename: .matrix rename oldname newname
• Rename col/row names: 
• matrix colnames A = names
• matrix rownames A = names
• Convert variables to matrix and vice versa: 
• mkmat varlist
• svmat  [type] A [, names(col|eqcol|matcol|string)] // with -preserve-, -drop _all-and -restore-.
• Matrix addition & subtraction:
• mat matrix_a + matrix_b
• mat matrix_a - matrix_b
• Matrix multiplication:
• mat matrix_a * matrix_b
• Transpose of a matrix
• mat matrix_t = matrix_a'
• Inverse of a matrix, matrix function are defined by using parentheses ():
• mat matrix_inv = inv(matrix_a)
• A\B adds row of B after the rows of A; A,B adds columns B to matrix A. For example:
• mat RTab=r(table)'
• mat R1=RTab[1...,"b"]
• mat R2=RTab[1...,"ll".."ul"]
• mat RDisp=R1,R2
• The usual way to obtain matrices after a command that produces matrices is simply to refer to the returned matrix in the standard way. (source: Stata manual) Or 'get()' matrix function obtains a copy of an internal Stata system matrix.
• For instance all estimation commands return
• e(b) coefficient vector
• e(V) variance-covariance matrix of the estimates (VCE)
• And these matrices can be referenced directly. For examples:
• matrix list e(b)
• mattrix myE = e(b)*100
• matrix myV = e(V)*100
• matrix myVget=get(_b)
• Then, you can use
• "ereturn post myE myV" to Change coefficient vector and variance-covariance matrix.
• And "ereturn display" to create and display a coefficient table: "r(table)" that have been previously posted using "ereturn post" or "repost". "r(table)" is a matrix containing all the data displayed in the coefficient table (including b, se, t, pvalue, ll, ul, df, crit, and eform).
• Reset row/column names of matrix
• mat A = (1,2,3\ 4,5,6\ 7,8,9)
• matrix rownames A = myrow1 myrow2 myrow3
• mat colnames A = mycol1 mycol2 mycol3
• refix the column names of A with equation names eq1, eq2, and eq3: mat coleq A = eq1 eq2 eq3
• Matrix utilities:
• matrix dir - List the currently defined matrices
• matrix list - Display the contents of a matrix
• matrix rename - Rename a matrix
• matrix drop - Drop a matrix
• Matrix Subscripting:
• Referring to a matrix's element/cell using numbers of row and column or using the names of row and column or using mixed ways:
• disp matrix_name [r,c]
• matrix A = A/A[1,1]
• gen newvar = oldvar/A[2,2]
• mat B = V["price","price"]
• gen sdif = dif/sqrt(V["price","price"])
• Create a matrix B having the first through fifth rows and first through fifth columns of A, using two periods:
• matrix B = A[1..5,1..5]
• Create a matrix B having the all rows and first column of A, use three periods: 
• matrix B = A[1...,1]
• mat B=A[1...,"b"]
• Define an element of a maxtrix:
• mat A[1,2] = sqrt(2)
• Abstract row and column names from a matrix (-rownames- and -colnames-: Macro extended function related to matrices). Also, we can use -rowfullnames matname-, and -colfullnames matname-
• sysuse auto,clear
• regress mpg price weight length
• local rn :rownames r(table)
• local cn :colnames r(table)
• disp "`w1'" // price
• disp "`:word 1 of `:colnames r(table)''" // price
• disp "rownames = `rn'" _n "columnnames = `cn'"
• disp `:word count `cn'' // 4
• disp "`:word count `:colnames r(table)''" // 4
• disp "`:colsof r(table)'" // 4
• disp "`:rowsof r(table)'" // 9
• forval i = 1/`:word count `cn'' {
•   local varn: word `i' of `cn'
•   disp "`varn'"
•   }
• Find out number of row and column (-colsof(matname)- and -rowsof(matname)-): Matrix functions
• disp "number of row = " rowsof(r(table)) 
• disp "number of column = " colsof(r(table)) 
• How to do element-by-element operation
• mata, the new matrix language of Stata (since version 9), can do element-by-element operation easily. (read more here)
• Colon operations perform element-by-element operations: addition(:+), subtraction(:-), multiplication(:*), division(:/), power(:^), equality(:==), inequality(:!=), greater than(:>), greater than or equal to(:>=), less than(:<), less than or equal to(:<=), and(:&), or(:|)
• If you apply the exponential function on each element:
• . mata
• : x=(0,1\2,3)
• : exp(x) // x = (1,2.7\7.4,20.1)
• : exp((0,1\2,3)) // (1,2.7\7.4,20.1)
• : x:/2 // x = (0,0.5\1, 1.5)
• : end
• mata can use a Stata matrix
• matrix A=r(table)'
• mata: exp(st_matrix("A")) // display only
• mata: st_matrix("se",sqrt(diagonal(st_matrix("V")))) // create a matrix of standard deviation
• mata: st_matrix("expA", exp(st_matrix("A"))) // create a new matrix, expA
• matrix eformtab=expA[1..., "b"], expA[1...,"ll".."ul"], expA[1...,"se"]
• matlist eformtab*100, tw(30) format(%8.1f)
• More about mata
• create row vector: row=(1,2,3) or row=(1..3)
• create column vector: col=(1\2\3) or col=(1::3)
• transpose a matrix: m2=(0,1\2,3)'
• scalar functions will operate on elements: sqrtrow=sqrt(row)
• SSCC: an introduction to Mata

• Articles
• Watson (2005). Further processing of estimation results: Basic programming with matrices. (pdf).
• Mill (2009). Expedited Introduction to Stata, Programming Basics, Automating Tables and Monte Carlo Simulations

quantile regression

Percentile and Quantile Regression for Complex Survey Data

R!

• R package: "quantreg" by Roger Koenker.
• [R] Quantile regression with complex survey data, and Solution from Thomas Lumley.
• Modified example (source) from the website of my favorite book -  Applied Survey Data Analysis.

            > library(survey)
            > options(survey.lonely.psu="remove")
            > dclus1<-svydesign(id=~psu_p, strata=~strat_p, weights=~wtfa, nest=TRUE, data=nhis)
            > bclus1<-as.svrepdesign(dclus1,type="bootstrap", replicates=100)
            > withReplicates(bclus1, quote(coef(rq(api00~api99, tau=0.5, weights=.weights))))

• Survey analysis in R by Thomas Lumley.
• How to deal with singleton/lonely PSUs (How do I analyze survey data with a stratified design with certainty PSUs?):
• Default: - options(survey.lonely.psu="fail") -, which makes it an error to have a stratum with a single, non-certainty PSU. 
• - options(survey.lonely.psu="remove") -,and - options(survey.lonely.psu="certainty") -, which can be set after the - library(survey)- mean a single-PSU stratum makes no contribution to the variance.
• - options(survey.lonely.psu = "adjust") -, which is taking the average of all the strata with more than one PSU. This might be appropriate if the lonely PSUs were due to data missing at random rather than to design deficiencies.
• Difficulties in estimating variances also arise when only one PSU in a stratum has observations in a particular domain or subpopulation. R gives a warning rather than an error when this occurs, and can optionally apply the "adjust" and "average" corrections. To apply the corrections, set - options(survey.adjust.domain.lonely=TRUE) -, and set - options(survey.lonely.psu="xxx") - to the adjustment method you want to use.

Stata

• Quantile regression
• Stata: How do I obtain percentiles for survey data?
• If only need point estimates of quantiles: we can use "_pctile" (store them in r()), "pctile" (create variables containing percentiles), and "xtile" (create variable containing quantile categories) to get quantiles for survey data. Or use "qreg"
• webuse nhanes2
• _pctile height [pw=finalwgt], p(10,90)
• return list
• disp "Median of age = " scalar(r(r1))
• pctile qhgt=height [pw=finalwgt], nq(4) genp(percent)
• xtile hgtdec=height[pw=finalwgt], nq(10)
• table sex [pweight= finalwgt] , c(median age count age) row format(%9.0f)
• qreg height [pw=finalwgt], quantile(10)
• mi estimate: qreg height, quanitle(10) vsquish
• If need standard errors and CIs, use user-written command "epctile" ("findit epctile")(or directly from "net from http://members.socket.net/~skolenik/stata/").
• Francisco (1991). Quantile estimation with a complex survey design
• webuse nhanes2
• svyset psu [pw=finalwgt], strata(strata)
• epctile height, p(10) svy
• epctile height, percentiles(10 20 30 50) subpop(if sex==1) svy
• Or, see my update (3/3/2017) below
• Replicate Weights and Bootstrap sampling and estimation.
• May use bootstrap to get variances of a complex designed sample: -bsweights- (IDEAS)creates the bootstrap weights for designs specified through and supported by svy:
• bs4rw, rw(brrrwt*): qreg weight i.race if subpop==1 [pw=finalwgt], q(.75)
• Started from version 13, -qreg- supports -pweight- and -iweight-. The 'bootstrap' has 'strata()' etc. options, even though 'bootstrap' is not designed for the complex survey data, the estimates are very similar to the estimates from the "withReplicates(design=xxx, quote(coef(rq(y ~ x,tau=0.5, weights=.weights))))" of R!.
• Blog: Doing Bootstrap/Jackknife in Stata for complex survey data
• Updates(3/3/2017): The better approach is to use 'svy jackknife' or 'svy bootstrap' with jackknife or bootstrap replicate weights. After the version 10, you don't need create jackknife replicate weights using user-written command -survwgt-, the -svy jackknife- can create the weights according to the info provided by -svyset-. You do need provide the bootstrap replicate weights for -svy bootstrap-, which can be created using the user-written command such as -bsweights-:
• webuse nhanes2, clear
• svyset psu [pw=finalwgt], strata(strata)
• bsweights bs_, n(-1) reps(50) seed(4881269)
• svyset [pw=finalwgt], bsrw(bs_*)
• svy bootstrap _b: qreg weight i.race, q(.5)

SAS

• PROC QUANTREG.

Articles

• Koenker (2001). Quantile Regression.
• Cade (2003). A Gentle Introduction to Quantile Regression for Ecologists.
• Li (2011). Application of nonparametric quantile regression to body mass index percentile curves from survey data.

When It Comes To A1C Blood Test For Diabetics, One Level No Longer Fits All

When It Comes To A1C Blood Test For Diabetics, One Level No Longer Fits All

by Nancy Shute

Source: National Public Radio

 … If there's one thing that people with diabetes get pounded into their heads, it's that they've got to keep their A1C level under control. That's the blood glucose measure that's used to decide how well a person is managing their diabetes.

But new diabetes management guidelines announced today will cut many people with diabetes some slack.

Where old guidelines from the American Diabetes Association said that people should maintain an A1C of 7, the new guidelines say that patients should work with their doctors to determine an appropriate A1C target. …

Full text: here

Social Rank Affects Monkey Immunity

Social Rank Affects Monkey Immunity
Source: The Scientist


"... the immune effects of rank were not permanent. Seven monkeys that changed rank showed a rapid change in gene expression to match their new status. The finding suggests that health depends on social status, and not vice versa...."


Full text: here
Original research article: here

Let Them Eat Dirt - The Scientist

Let Them Eat Dirt
Source: The Scientist

I read this interesting article with great interest. It demonstrates the rightness of the view of my mom, grandma, great grandma, ..., all of them is/were not scientist for sure.
"...Maybe it's okay to let your toddler lick the swing set and kiss the dog. A new mouse study suggests early exposure to microbes is essential for normal immune development, supporting the so-called "hygiene hypothesis" which states that lack of such exposure leads to an increased risk of autoimmune diseases. Specifically, the study found that early-life microbe exposure decreases the number of inflammatory immune cells in the lungs and colon, lowering susceptibility to asthma and inflammatory bowel diseases later in life. ..."

Full text: here

Tools for Innovative Thinking in Epidemiology

Tools for Innovative Thinking in Epidemiology

By Roberta B. Ness

Source: American Journal of Epidemiology

“Innovation is the engine of scientific progress. Concern has been raised by the National Academies of Science about how well America is sustaining its ‘‘creative ecosystem.’’ In this commentary, the author argues that we can all improve our ability to think innovatively through instruction and practice. The author presents a series of tools that are currently being taught in a curriculum developed at the University of Texas, based on earlier evidence-based creativity training programs. The tools are these: 1) finding the right question; 2) enhancing observation; 3) using analogies; 4) juggling induction and deduction; 5) changing your point of view; 6) broadening the perspective; 7) dissecting the problem; 8) leveraging serendipity and reversal; 9) reorganization and combination of ideas; 10) getting the most out of groups; and 11) breaking out of habitual expectations and frames. Each tool is explained using examples from science and public health. It is likely that each of us will identify with and agree with the usefulness of one or two of the tools described. Broader mastery of many of these tools, particularly when used in combination, has provided our students with a powerful device for enhancing innovation.”

Read full text here

John Glenn's true hero - CNN.com

John Glenn's true hero
Source: CNN.com
"...For half a century, the world has applauded John Glenn as a heart-stirring American hero. He lifted the nation's spirits when, as one of the original Mercury 7 astronauts, he was blasted alone into orbit around the Earth; the enduring affection for him is so powerful that even now people find themselves misting up at the sight of his face or the sound of his voice.
But for all these years, Glenn has had a hero of his own, someone who he has seen display endless courage of a different kind:
Annie Glenn.
They have been married for 68 years.
He is 90; she turned 92 on Friday.
This weekend there has been news coverage of the 50th anniversary of Glenn's flight into orbit. We are being reminded that, half a century down the line, he remains America's unforgettable hero. ..."
The full text here.

The Mediterranean Diet

The Mediterranean Diet

Dr. Eduardo Farinaro from Federico II University of Naples, Italy, gave us a great presentation about the lifestyle and CHD prevention. I learned more about the Mediterranean diet.

What is the Mediterranean Diet?

There may be no single Mediterranean diet; the Mediterranean diet is the traditional dietary patterns of Spain, southern Italy, Greece and specifically the Greek island of Crete, and parts of the Middle East. The major characteristics are a few I remember:

• olive oil as a principle source of fat.
• colorful veggies, and fruits, beans & nuts (legumes), and whole grains.
• regular fish consumption.
• herb and spicy seasonings, which will reduce the need of salt and fat.
• moderate drink of wine with the meal. 

Dr. Farinaro also mentioned the persons living in the Mediterranean diet area are also with lower level of anxiety, having a good relationship with family, and moderate physical activity. He did not mention much about dairy products.

Mediterranean Diet Pyramid from Oldways Health Through Heritage. 

Mediterranean Diet -- The US News Best Diets -  USNews

Mediterranean diet: Choose this heart-healthy diet option - Mayo Clinic 

Mediterranean diet - Wikipedia, the free encyclopedia