“Highly publicized cases of fabrication or falsification of data in clinical trials have occurred in recent years and it is likely that there are additional undetected or unreported cases. We review the available evidence on the incidence of data fraud in clinical trials, describe several prominent cases, present information on motivation and contributing factors and discuss cost-effective ways of early detection of data fraud as part of routine central statistical monitoring of data quality. Adoption of these clinical trial monitoring procedures can identify potential data fraud not detected by conventional on-site monitoring and can improve overall data quality.”
The New York Times: The Age That Women Have Babies: How a Gap Divides America by Quoctrunk Bui and Claire Cain Miller
“How not to collaborate with a biostatistician. This is what happens when two people are speaking different research languages! My current workplace is nothing like this, but I think most biostatisticians have had some kind of similar experiences like this in the past!”
YouTube: Biostatistics vs. Lab Research by JavaMama926
Blood on the Tracks – Podcast Episode 38
Learn about a piece of epidemiological history: one of the earliest examples of population-level clinical studies influencing medical practice. This podcast tells the story of how French physician Pierre Charles Alexandre Louis studied a group of patients and ended up discovering quantitative evidence on the detriment of bloodletting. Learning the history helps place these tools in a broader context, which isn’t crucial, but interesting nonetheless.
The first population study in history was born out of a dramatic debate involving leeches, “medical vampires,” professional rivalries, murder accusations, and, of course, bloodletting, all in the backdrop of the French Revolution. The second of a multipart series on the development of population medicine, this episode contextualizes Pierre Louis’ “numerical method,” his famous trial on bloodletting, and the birth of a new way for doctors to “know”.
“The series of reviews commissioned by SPPE over the past year shed important insights on the current state of psychiatric epidemiology [1-5]. Our reading of this series has led us into discussions of the scope and goals of our discipline, and how, within a historical context, it is expanding in both predicted and unforeseen ways. In this editorial we first reflect on the history of our field, and how the wealth of information in these reviews provides insight into newly emerging directions of inquiry. Then we discuss major advances and remaining challenges in the field not covered in the series. Finally, we consider the overall scope and future directions of psychiatric epidemiologic inquiry in the years to come.”
“Simpson’s paradox, or the Yule–Simpson effect, is a phenomenon in probability and statistics, in which a trend appears in several different groups of data but disappears or reverses when these groups are combined. It is sometimes given the descriptive title reversal paradox or amalgamation paradox.”
This seems counterintuitive, but the 5 minute video below explains the concept well.
Wikipedia: Simpson’s paradox
Minute Physics: Simpson’s Paradox
A set of training materials for professionals working in intervention epidemiology, public health microbiology and infection control and hospital hygiene.
Physician Payments from Industry Are Associated with Greater Medicare Part D Prescribing Costs