A hierarchical model is developed to account for selection biases that result from processes in which events have a fixed probability of being sampled, but individuals in the population generate events at varying rates. It is shown that inferences about the population parameters from such...

Evaluations involving nonrandom assignment to treatment or control groups are vulnerable to an accidental or intentional confounding of a selection effect with the treatment effect. The resulting selection bias is compensated with two techniques, discriminant analysis and base expectancy...