Compared to fixed effect models, random effects suffer lower bias and trade inflated type I errors for improved power. Contrasting hazard rates between trials prevent accurate estimates from both fixed and random effects models.

Crossing survival curves complicate how we interpret results from a clinical trial’s primary endpoint. We find the function to determine a crossing point’s location depends exponentially on individual survival curves. This exponential relationship between survival curves and the crossing point transforms small survival curve errors into large crossing point errors. In most cases, crossing points are sensitive to individual survival errors and may make accurately locating a crossing point unsuccessful. We argue more complicated analyses for mitigating crossing points should be reserved only after first exploring a crossing point’s variability, or hypothesis tests account for crossing point variability....

By modeling question sets as networks of interrelated questions, we introduce algorithms to help curtail the growth bias by efficiently distributing workers between exploring new questions and addressing current questions.

We find that causal statements have a number of significant lexical and grammatical differences compared to non-causal language

Accounting for spatial effects in both construction and robustness is important for designing efficient microgrids and other network infrastructure.