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This is a tutorial on how to use R to evaluate a previously published prediction tool in a new dataset. Most of the good ideas came from Maarten van Smeden, and any mistakes are surely mine. This post is not intended to explain they why one might do what follows, but rather how to do it in R. It is based on a recent analysis we published (in press) that validated the HOMR model to predict all-cause mortality within one-year of a hospitalization in a cohort of patients aged 65 years or older that were under the care of geriatric medicine service at Cork University Hospital (2013-01-01 to 2015-03-06).


Everywhere I look, people are saying there is something wrong with Science.


Welcome to the world of clinical trials. You’ve got a great idea that has real potential to improve health. But I have some bad news for you. Are you ready?


“Individuals engaging in ad hominem attacks in scientific discourse should be subject to censure.” From Issues with data and analyses: Errors, underlying themes, and potential solutions. Andrew W. Brown, Kathryn A. Kaiser, and David B. Allison. PNAS March 13, 2018. 115 (11) 2563-2570; published ahead of print March 12, 2018. That is a remarkable suggestion.


I was greeted today with the news that there are 5, not 2, types of diabetes. This is earth-shattering if you are a diabetologist or diabetes researcher. However, I soon as I saw the term “data-driven clustering” I knew I could probably relax. For the uninitiated, data-driven clustering techniques can seem magical. The basic premise is that you take a sample, measure lots of things about them, and then feed all those data into an algorithm that will divide your sample into mutually exclusive groups.


Recent Publications

  • Associations of linear growth and relative weight gain during early life with adult health and human capital in countries of low and middle income: findings from five birth cohort studies


  • A structural equation model of the developmental origins of blood pressure

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