I am a student, working with a team on a large-scale ecological experiment. We want to analyze survival data which has been derived from an experimental design with some pseudo-replication. This pseudo-replication was not discovered, unfortunately, until the middle of the experiment, at which point the design could not be altered.
The experimental design involves comparing the survival data of several treatment groups, each comprised of 10 replicates (aquaria) with 10 individuals in each tank. We have measured mortality as a response variable, in response to different environmental stressors. The trouble is that we cannot say that each of the deaths that occurred, have occurred independently since each tank has many individuals. We would like to acknowledge this problem and address it in our analysis.
All of the survival analysis tools we are aware of, assume independence between replicates. We are considering using Kaplan-Meier curves, Cox proportional hazard, or even a glm with Gamma error distributions.
Any ideas about how we can properly address this problem in order to detect a difference in survivorship between the treatment groups?