LAUSR

Romosozumab, an anti-sclerostin antibody, has been approved for prescription in the USA, Europe, Japan, and South Korea to treat patients with osteoporosis who have a high risk of fracture. The Active-Controlled Fracture Study in Postmenopausal Women with Osteoporosis at High Risk (ARCH) trial randomly assigned women to receive alendronate or romosozumab for 12 months. It found a difference between the alendronate and romosozumab group in the incidence of major cardiovascular events (CVD), also defined as adjudicated major adverse cardiovascular events (MACE) [1]. After 12 months, those in the alendronate group continued alendronate for a median of 33 months and those assigned to romosozumab switched to alendronate for the duration of the trial. When a randomized trial with balanced baseline characteristics of subjects finds a difference in rates of events, such as CVD, between two active drugs, such as alendronate and romosozumab, there are three explanations: (1) the difference is attributable to chance, (2) alendronate reduced the rate of CVD, or (3) romosozumab increased the rate of CVD. As the probabilities of these alternatives must all sum to 1.0, a decrease in the probability of one of these explanations increases the probability that the other alternatives are true. Importantly, the likelihood that any one of these explanations is true depends on other information. This Bayesian approach to the interpretation of trials is similar the Bayesian interpretation of diagnostic tests: the result of a test must be interpreted in the context of other information [2]. For instance, the probability that a positive test is true depends on the prior probability that the patient has the disease based on other information. If the prior probability is low, then a positive result of the test is more likely to be false. Similarly, the probability that a result of a trial is true critically depends on other information. The probability that a result is true, for example, that alendronate reduced CVD rates, depends on the prior probability that alendronate reduced CVD rates in other randomized trials. Although it is difficult to quantify these probabilities, other information is critical to judging which alternative explanation is true.