Diabetes Medication Comparison Table: Benefits and Risks

Diabetes Medication Comparison Table: Benefits and Risks
Source: Diabetes In Control.com
read full article here: http://www.diabetesincontrol.com/articles/features/11666

GRADE guidelines (Grades of Recommendation, Assessment, Development, and Evaluation)

GRADE guidelines (Grades of Recommendation, Assessment, Development, and Evaluation)

From Journal of Clinical Epidemiology (JCE)

JCE published some systemic recommendations/guidance for rating quality of evidence and grading strength of recommendations in healthcare on two issues. I combined these related linkages below:

JCE Volume 64, Issue 4

Standards, guidelines, and norms

Challenges in guideline methodology

GRADE guidelines: A new series of articles in the Journal of Clinical Epidemiology

GRADE guidelines: 1. Introduction—GRADE evidence profiles and summary of findings tables

GRADE guidelines: 2. Framing the question and deciding on important outcomes

GRADE guidelines: 3. Rating the quality of evidence

GRADE guidelines: 4. Rating the quality of evidence—study limitations (risk of bias)

JCE Volume 64, Issue 12

Evidence, (un)certainty, and clinical practice

GRADE: complex decisions

Strength of evidence and handling uncertainty: practical considerations and general observations

GRADE guidelines: 5. Rating the quality of evidence—publication bias

GRADE guidelines 6. Rating the quality of evidence—imprecision

GRADE guidelines: 7. Rating the quality of evidence—inconsistency

GRADE guidelines: 8. Rating the quality of evidence—indirectness

GRADE guidelines: 9. Rating up the quality of evidence

 

Can 'margins' be used after 'mi estimate'?

Can 'margins' be used after 'mi estimate'?

The -margins- command may not be used in the usual way after -mi estimate-. You'll need to write a short "wrapper command" that can be run with the -mi estimate- prefix. My colleagues outlined the method on Statalist: Average marginal effects for a multiply imputed complex survey. You'll want to change the 11 in the -version 11- statement to 12. One of our FAQs will also be helpful: How can I combine results other than coefficients in e(b) with multiply imputed data? - by A Statistician, Stata


Here is my modified program based on codes of UCLA Stata Portal

use http://www.ats.ucla.edu/stat/data/hsbmar, clear
/* set MI dataset */
mi set mlong
mi register imputed female math read science socst
mi svyset [pw=write], strata(ses)
mi impute chain (logit) female (regress) math read science socst ///
       =ses write awards, add(10) rseed(123456)
/* program */
capture program drop mimargins
program mimargins, eclass properties(mi)
  version 12
  svy: logit honors i.female##i.prog read math science socst
  margins female#prog, post
end
/* run the program */ 
mi estimate, cmdok: mimargins 1
matlist r(table)'*100, tw(20) format(%8.2f)
mi estimate (_b[1.female#2.prog]/_b[1.female#1bn.prog]): mimargins 1

Notes: The 'cmdok' option forces 'mi estimate' to estimate the current Stata unsupported model. The 'cmdok' is not necessary because of already including the 'properties(mi)' in the program.

mi estimate: svy: logit honors i.female##i.prog read math science socst
mi test 1.female#2.prog 1.female#3.prog

mi estimate (diff:1.female#2.prog-1.female#3.prog), saving(miest, replace): svy: logit honors i.female##i.prog read math science socst
mi testtransform diff

mi estimate (rdiff:1.female#2.prog/1.female#3.prog - 1) using miest
mi testtr rdiff


Useful Resources:

• MIMRGNS: Stata module to run margins after mi estimate
• UCLA: How can I get margins and marginsplot with multiple imputed data?
• Klein (2016): Marginal Effects in Multiply Imputed Datasets
  

Abramowitz and Stegun: Handbook of Mathematical Functions

Math - Abramowitz and Stegun: Handbook of Mathematical Functions

“The present volume is an outgrowth of a Conference on Mathematical Tables held at Cambridge, Mass., on September 15-16, 1954, under the auspices of the National Science Foundation and the Massachusetts Institute of Technology. The purpose of the meeting was to evaluate the need for mathematical tables in the light of the availability of large scale computing machines. It was the consensus of opinion that in spite of the increasing use of the new machines the basic need for tables would continue to exist.

Numerical tables of mathematical functions are in continual demand by scientists and engineers. A greater variety of functions and higher accuracy of tabulation are now required as a result of scientific advances and, especially, of the increasing use of automatic computers. In the latter connection, the tables serve mainly for preliminary surveys of problems before programming for machine operation. For those without easy access to machines, such tables are, of course, indispensable...”

You can view or download the tenth printing of this famous reference here: http://people.math.sfu.ca/~cbm/aands/

How to Estimate Percentiles and Confidence Intervals

How to Estimate Percentiles and Confidence Intervals
By CDC

Including percentiles whose estimate falls on a value that is repeated multiple times in the dataset
A common practice to calculate confidence intervals from survey data is to use large-sample normal approximations. Ninety-five percent confidence intervals on point estimates of percentiles are often computed by adding and subtracting from the point estimate a quantity equal to twice its standard error. This normal approximation method may not be adequate, however, when estimating the proportion of subjects above or below a selected value, especially when the proportion is near 0.0 or 1.0 or when the effective sample size is small. In addition, confidence intervals on proportions deviating from 0.5 are not theoretically expected to be symmetric around the point estimate. Further, adding and subtracting a multiple of the standard error to an estimate near 0.0 or 1.0 can lead to impossible confidence limits (i.e., proportion estimates below 0.0 or above 1.0). The approach used for the Report data tables (and for previous Reports) produces asymmetric confidence intervals consistent with skewed (nonnormal) biologic data distributions. ...

You can read the whole article here: http://www.cdc.gov/exposurereport/data_tables/appendix_a.html

The 2009 ISPAD Clinical Practice Consensus Guidelines

The 2009 Clinical Practice Consensus Guidelines
By the International Society for Pediatric and Adolescent Diabetes

Here are the PDFs of chapters:

Chapter 1: Definition, epidemiology, diagnosis and classification

Chapter 2: Presentation and phases of diabetes

Chapter 3: Type 2 diabetes

Chapter 4: Monogenic diabetes

Chapter 4b: Management of cystic fibrosis-related diabetes

Chapter 5: Diabetes education

Chapter 6: The delivery of ambulatory diabetes care

Chapter 7: Assessment and monitoring of glycemic control

Chapter 8: Insulin treatment

Chapter 9: Nutritional management

Chapter 10: Diabetic ketoacidosis

Chapter 11: Hypoglycemia

Chapter 12: Sick day management

Chapter 13: Exercise

Chapter 14: Management of children with diabetes requiring surgery or fasting

Chapter 15: Psychological issues

Chapter 16: Adolescence

Chapter 17: Microvascular and macrovascular complications

Chapter 18: Other complications and associated conditions

Recipes and Meal Planning from the American Diabetes Association

Recipes and Meal Planning from the American Diabetes Association

If you are looking for healthy recipes to lose weight, prevent, or manager diabetes, you may like the MyFoodAdvisor – Recipes for healthy Living from the American Diabetes Association. You must register to access these recipes, which I don’t like the way of access, but it’s FREE.

Top 50 Statistics Blogs of 2011

Top 50 Statistics Blogs of 2011

By thebestcolleges.org

The Best Colleges has published a long list of statistic related blogs. Wish they could have reduced the number to 10.

Steve Jobs' Inspirational Advice

Steve Jobs' Inspirational Advice

• Stay Hungry, Stay Foolish (2008)
• Steve Jobs on Wikipedia
• Transcript of Commencement Speech at Stanford given by Steve Jobs
• A Collection of 60 Inspirational Steve Jobs Quotes About Life, Design and Apple 
• Steve Jobs's Best Quotes on the WSJ Blogs
• The 11 Best Steve Jobs Quotes: Remembering The Apple CEO
• The 13 Most Memorable Quotes From Steve Jobs
• Read more on Business Insider
• Steve Jobs's quotes on Wikiquote 
• in Chinese 乔布斯十条经典语录

Tips: SAS code matched to Stata code and others

Usually, I think that SAS is an analog to a product of Microsoft, Stata is an analog to a product of Apple, and R is an analog to a product of Google. I have used the SAS for many years, but have been attracted by Stata for some new features. I am always trying to find the same handy data management features using Stata, and notice there are a few websites providing these equivalents.

• On the Stata website of Carolina Population Center, UNC, there are a nice webpage displaying the equivalent command of Stata and SAS. It shows how to "Do" in Stata what you know how to "Program" in SAS. , or read this PDF file 
• UCLA also provides a similar page here
• Stata tutorials: UCLA, Princeton University online Stata tutorial
• Statistic test overview 
• Biostatistics by William D. Dupont
• Stata tips by Michael Norman Mitchell

Resources for Disability Research

• The Future of Disability in America. You can download the document for free. This report affirms the ICF as a model for disability research, and it summarizes some of the broad themes in disability.
• Workshop on Disability in America. This free book contains 14 commissioned essays on disability—ranging from models of disability (Whiteneck) to trends in youth, working age, and older people.
• Improving measurement of late Life Disability 
• Prevalence of Complex Activity Limitations, an example of CAL, using US National Health Interview Survey (NHIS)

                                            - these materials highly recommended by my colleague J.C., an expert in this disability area.