Good examples of statistics sections in applied academic journal articles I am a biostatistician working in an applied field and I am responsible for writing the statistics methods section for the papers I collaborate on.  In reading a lot of academic papers I've come across plenty of examples of badly written stats sections (mostly they are boring, uninformative, and lack precision, detail, and understanding of the methodology used).  
Regardless of the subject matter and sophistication of the statistical methods used, what are some good examples of well written statistics sections in applied research articles?
How to define "well written" is subjective, but I would describe a statistics section as well written if it is lucid, gives (or seems to give) a full picture of how the analysis was conducted, addresses the assumptions made during the analysis, and incorporates the statistical process into the flow of the paper.
Here are some examples of papers that I think have good statistics sections:
BCG Vaccination Reduces Risk of Tuberculosis Infection in Vaccinated Badgers and Unvaccinated Badger Cubs
A Model for Predicting Mortality in Acute ST-Segment Elevation Myocardial Infarction Treated With Primary Percutaneous Coronary Intervention : Results From the Assessment of Pexelizumab in Acute Myocardial Infarction Trial
Others?  Thoughts about what a "good" statistics section should include are also very welcome.
 A: In mid 2000s, a group of medical statisticians put their heads together and issued STROBE statement (http://www.strobe-statement.org): STrengthening the Reporting of OBservational studies in Epidemiology. It was published in the same form in Lancet, PLoS Medicine, Journal of Clinical Epidemiology, and several others, which to me seems like the most amazing part of the whole exercise: putting heads together is not nearly as difficult as convincing a diverse group of editors to publish anything as is. There are various checklists based on the STROBE statement that help you define what a "well written" statistical part is.
In an unrelated area, the U.S. Institute of Education has been accumulating evidence on performance of the various educational programs in their What Works Clearinghouse.  Their Procedures and Standards Handbook delineates what constitutes a solid study (by the education community standards; biostatisticians with clinical trials background find them falling quite short of what FDA requires). Spoiler alert: of the 10,000 study reports in the WWC data base, only 500 "meet  WWC standards without reservations"... so when you hear somebody say about an educational product that it is "research-based", there's exactly 95% chance it's actually bogus, with research conducted by the publishers of that product without the control group.
A: The following is a favorite article of mine: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2650104/
Here a very well controlled clinical trial was conducted to verify what was a commonly held belief that suppression of herpes outbreaks could reduce the transmission of HIV. It is an example of a null result. They also don't discredit their evidence because it is an enormous and well controlled trial. The design is immense, all aspects of possible confounding or bias were considered.
What I appreciate about the statistics section is its brevity, its focus on pre-specified analyses, clearly delineating primary vs. secondary hypotheses, and disclaimer for conflict of interest, describing intent-to-treat and per protocol analyses, and explaining the possible source(s) of bias.
