Any ideas about how to analyze survival data with pseudo-replication (dependent data)?

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?

• Roughly what proportion of the individuals died by the end of the experiment? If it's reasonably low there may be little advantage of survival analysis over ignoring the time to death and treating died/survived as a binary outcome, which could be considerably simpler. Commented Nov 11, 2010 at 23:46
• A more complex option is frailty models - i'm too tired to attempt say any more now but i've added the 'frailty' tag above so you can click that and look at the answers to the other q with this tag. Commented Nov 11, 2010 at 23:53
• @onestop- Thanks for contributing some perspective and ideas! I will look into the frailty model option. We had about 20 to 40% mortality in the treatment levels. After some discussion, we are now considering analyzing the replicates, not the individual deaths, since the replicates are independent from one another. Using this perspective, maybe we can arrive with a mean mortality value for each replicate and compare them (accounting for censoring also). Another option could be to compare the chance to survive in each replicate, then compare replicates to each other. Commented Nov 12, 2010 at 11:02
• ...maybe Repeated Measures ANOVA could be an option, using replicate level information. We would need to use something that would allow for non-normal data distribution. Commented Nov 12, 2010 at 11:02
• Any thoughts on these ideas? (thanks!) Commented Nov 12, 2010 at 11:03