Evaluating treatments in rare indications warrants a Bayesian approach.

Evaluating efficacy and real-world effectiveness for novel therapies targeting rare mutations or patient subpopulations with unmet needs is a growing challenge in health economics and outcomes research (HEOR). In these settings it may be difficult to recruit enough patients to run adequately powered randomized clinical trials, resulting in greater reliance on single-arm trials or basket trial designs. Additionally, evidence networks for performing network meta-analysis may be sparse or disconnected when comparing available treatments in narrower patient populations.

Totality of evidence of the effectiveness of repurposed therapies for COVID-19: Can we use real-world studies alongside randomized controlled trials?

Rapid and robust strategies to evaluate the efficacy and effectiveness of novel and existing pharmacotherapeutic interventions (repurposed treatments) in future pandemics are required. Observational "real-world studies" (RWS) can report more quickly than randomized controlled trials (RCTs) and would have value were they to yield reliable results. Both RCTs and RWS were deployed during the coronavirus disease 2019 (COVID-19) pandemic.

Conservation and production responses vary by disturbance intensity in a long-term forest management experiment.

Reductions in management intensity are often proposed to support a broader range of beneficial ecosystem responses than traditional management approaches. However, few studies evaluate ecosystem responses across approaches. Also, managers lack information about how species traits mediate responses across management approaches, a potentially substantial source of spatial and temporal variation in population and community responses that if ignored may hinder effectiveness of management programs.