Statistical Modelling of Neighbor Treatment Effects in Aquaculture Clinical Trials

In the design of clinical trials involving fish observed over time in tanks, there may be advantages in housing several treatment groups within the same tank. In particular, such “within-tank” designs will be more efficient than designs with treatment groups in separate tanks when substantial between-tank variability is expected. One potential problem with within-tank designs is that it may not be possible to include all treatments in one tank; in statistical terms this means that the blocks (tanks) are incomplete. In incomplete block designs, there may be a concern that the treatments present in the same tank (denoted here as “neighbors”) affect each other in their performance; thus the need for an assessment of neighbor effects. In this paper, we propose two statistical approaches to assess and account for neighbor effects. The first approach is based on a non-linear mixed model and the second involves cross-classified and multiple membership models. Both approaches are illustrated on simulated data as well as data from a clinical ISAV (Infectious Salmon Anaemia Virus) trial; corresponding computer code is available online.