Skip to main content

Because Statistics.

I recently stumbled onto a nearly decade old statistics/research series that's been ongoing in the academy of nutrition and dietetics journal (the series is split between JADA and JAND). I've been reading through it and appreciate its review of basic statistics and more complex methods, especially since it's framed in topics relevant to nutrition (I think what I hated most about stats was the examples being more relevant to baseball players and not biomedical scientists). In addition to JAND's efforts in stats education, ASN/AJCN have been running a series called "best (but oft-forgotten) practices).

We've all had that moment
JAND's series:

1.  Publishing nutrition research: a review of study design, statistical analyses, and other key elements of manuscript preparation


3. An Introduction to Qualitative Research for Food and Nutrition Professionals

 4. A Review of Epidemiological Methods

5. Validity, Reliability, and Diagnostic Test Assessment in Nutrition-Related Research 

6. A Review of Multivariate Techniques
Part 1:
Part 2:
Part 3:

AJCN's Best (but oft-forgotten) Practices:

 1. Intro:

2. Checking Assumptions Concerning Regression Residuals

3. Designing, Analyzing, and Reporting Cluster Randomized Controlled Trials:

4. Multiple Test Corrections

5. Testing for treatment effects in randomized trials by separate analyses of changes from baseline in each group is a misleading approach

6. Sensitivity analyses in randomized controlled trials

7. Expressing and interpreting associations and effect sizes in clinical outcome assessments

8. The design, analysis, and interpretation of Mendelian randomization studies.

9. Propensity score methods in clinical nutrition research.

10. Intention-to-treat, treatment adherence, and missing participant outcome data in the nutrition literature.

Comments

Post a Comment

Popular posts from this blog

Beware the Meta-Analysis: Fat, Guidelines, and Biases

Headlines were abuzz this week, reporting that a new review of randomized controlled trials at the time of the low-fat guidelines didn't support their institution. Time , Business Insider , and The Verge all covered the topic with sensationalist headlines (e.g. 'We should never have told people to stop eating fat' #weneverdid). I won't spend every part of this blog picking apart the entire meta-analysis; you can read it over at the open access journal, BMJ Open Heart (1) -- (note, for myself, i'm adding an extra level of skepticism for anything that gets published in this journal). I'm also not going to defend low-fat diets either, but rather, use this meta-analysis to point out some critical shortcomings in nutritional sciences research, and note that we should be wary of meta-analyses when it comes to diet trials. First off, let's discuss randomized controlled trials (RCTs). They are considered the gold standard in biomedical research; in the hierarc
21 comments
Read more

On PURE

The PURE macronutrients studies were published in the Lancet journals today and the headlines / commentaries are reminding us that everything we thought we think we were told we knew about nutrition is wrong/misguided, etc. Below is my non-epidemiologist's run down of what happened in PURE. A couple papers came out related to PURE, but the one causing the most buzz is the relationship of the macronutrients to mortality. With a median follow up of 7.4 years, 5796 people died and 4784 had a major cardiovascular event (stroke, MCI). The paper modeled the impacts of self reported dietary carbohydrate, total fat, protein, monounsaturated (MUFA), saturated (SFA), and polyunsaturated (PUFA) fatty acid intakes on cardiovascular (CVD), non-CVD and total mortality; all macros were represented as a percentage of total self reported energy intakes and reported/analyzed in quintiles (energy intakes between 500-5000kcals/day were considered plausible..). All dietary data was determined by a
10 comments
Read more

Want To Buy: A Placebo

A well-designed/performed, double-blind, randomized, placebo-controlled trial provides a high level of certainty about the effectiveness of an intervention. In scientific training, the need to utilize a placebo relative to your variable of interest is one of the first things you learn when designing an experiment. As many in the basic sciences and evidence-based medicine fields have become more interested in nutrition and its impact on health/biology (their interest is well-justified), there has been insufficient appreciation for the difficulty in performing nutrition research. This day 1 principle of "placebo-controlled" poses a particular challenge for many nutrition experiments: there is no placebo.  Consider an example that actually plagued causal inference in nutrition history: It was known that feeding diets high in saturated fatty acids was associated with higher LDL. Does that mean that saturated fat raises LDL? How would you design a study to show
Post a Comment
Read more