The importance of a study is often inappropriately defined by the P value, the authors write, and this problem is highlighted by the use of “trend”—a term for which there is no definition—to refer to statistically non-significant results. To make a case in point, they set out to characterize the degree of overreliance on P values in high-impact oncology literature.

Many people in the U.S. take multiple prescription drugs, but very few studies have explored the relationships between these "high-order" drug combinations. The authors set out to make a general tool for quickly and accurately estimating drug-drug interactions, or DDIs, in cases where a patient already on a given drug adds a new one and three or more drugs are involved.

When cardiac arrest occurs outside the home, men are more likely than women to receive cardiopulmonary resuscitation (CPR) from a bystander—and are more likely to survive, found a study team led by Audrey Blewer, MPH, who is assistant director for Educational Programs at Penn’s Center for Resuscitation Science and is pursuing a PhD in Epidemiology at Penn.

Type-2 diabetes is a significant risk factor for coronary heart disease, and these two common conditions are leading causes of disease and death globally. But the biological pathways that explain the connection between them have remained somewhat murky, as have the causes of T2D. In a large analysis of genetic data, an international team led by researchers in the Perelman School of Medicine sought answers about what causes T2D and clarified how the two diseases are linked.

The trend-in-trend design looks at changes in the frequency of an outcome as it relates to changes in an exposure, such as a new drug, across subgroups. This design has advantages over randomized clinical trials, because such trials are usually too small to identify rare adverse effects and are performed in a highly restricted group of patients—one that does not reflect the group that will actually take a drug once it is approved.

Cognitive behavioral therapy (CBT) is known to be an effective treatment for patients with major depressive disorder (MDD) or post-traumatic stress disorder (PTSD). But can we actually see these effects in the brain? This team sought to find out.

Prediction models have been widely adopted in clinical research and medical practice, yet there are often major concerns about whether such models can be used across different populations, in different clinical settings. Relatively little research has been done to develop general approaches that can improve model performance in these situations. The authors set out to fill this gap.