Traditionally, checkmate is a position in the game of chess in which a player’s king is in check, without a way to remove the threat. The king cannot be captured, so the game ends when the king is checkmated. As a premium surgeon, no one ever wants to be checkmated at any stage of the surgical process, from preoperative to intraoperative to postoperative. Other forms of etymology have suggested checkmate to signify being “ambushed,” a feeling many of us have experienced in our surgical careers. A means to avoiding being a checkmated surgeon is creating “checklists” from the time of the first patient encounter until the final postoperative visit. The process of checklists can bring reproducibility to a surgical process that already yields successful outcomes in a premium surgeon’s practice.
Studies indicate, however, that more than half of the experiments involving clinical trials of new drugs and treatments are irreproducible. John Ioannidis at Stanford University, US, goes on saying that most of the search results is actually false. Ioannidis is the author of a mathematical model that predicts that the smaller the sample and less stringent are the experimental methodology, definitions, outcomes and statistical analysis, the greater the probability of error. Furthermore, studies that hold financial and other interests or of great impact are also more prone to false results.
A paper from investigators at the University of Alabama at Birmingham recently published in Obesity identifies several key statistical errors commonly seen in obesity research with discussions on how to identify and avoid making these mistakes. "Our goal is to provide researchers and reviewers with a tutorial to improve the rigor of the science in future obesity studies,” said Brandon George, Ph.D., statistician in the University of Alabama at Birmingham Office of Energetics. “Investigators who conduct primary research may find the paper useful to read or share with statistical collaborators to obtain a deeper understanding of statistical issues, avoid making the discussed errors, and increase the reproducibility and rigor of the field. Editors, reviewers and consumers will find valuable information allowing them to properly identify these common errors while critically reading the work of others."
As science grapples with what some have called a reproducibility crisis, replication studies, which aim to reproduce the results of previous studies, have been held up as a way to make science more reliable. It seems like common sense: Take a study and do it again — if you get the same result, that’s evidence that the findings are true, and if the result doesn’t turn up again, they’re false. Yet in practice, it’s nowhere near this simple.
Once again, reproducibility is in the news. Most recently we hear that irreproducibility is irreproducible and thus everything is actually fine. The most recent round was kicked off by a criticism of the Reproducibility Project followed by claim and counter claim on whether one analysis makes more sense than the other. I’m not going to comment on that but I want to tease apart what the disagreement is about, because it shows that the problem with reproducibility goes much deeper than whether or not a particular experiment replicates.
In 2005, John Ioannidis, a professor of medicine at Stanford University, published a paper, “Why most published research findings are false,” mathematically showing that a huge number of published papers must be incorrect. He also looked at a number of well-regarded medical research findings, and found that, of 34 that had been retested, 41% had been contradicted or found to be significantly exaggerated. Since then, researchers in several scientific areas have consistently struggled to reproduce major results of prominent studies. By some estimates, at least 51%—and as much as 89%—of published papers are based on studies and experiments showing results that cannot be reproduced.