I am trying to compare the performance of two devices designed to cool anaesthetised patients. This is a substudy analysis and the patient allocation between the groups is not even. Once cooling was started, they were cooled to 33 degrees before being re-warmed. Temperature measurements were taken once every hour. Both devices work on a negative feedback principle and I am investigating the device's ability to maintain patient temperature around the target temperature (33 degrees) for the set time period (28 hours). Below is plotted the mean time course of the groups of patients with the standard deviation. As demonstrated, the standard deviation is less for the IV33 group once cooling is established, although there were fewer patients in this group.
What I am trying to show is that there is a statistically significant difference in the variance once cooling is established. To do that I have applied Levene's Test to the results from each hour which give me the following results:
Hour - p value 00 - 0.8949 01 - 0.8954 02 - 0.2522 03 - 0.01618 04 - 0.03234 05 - 0.004928 06 - 0.000227 07 - 0.0001289 08 - 0.0002498 09 - 0.001403 10 - 0.001158 11 - 0.0001553 12 - 0.01084 13 - 0.0003181 14 - 0.001402 15 - 0.005558 16 - 0.01849 17 - 0.001601 18 - 0.003469 19 - 0.01291 20 - 0.02297 21 - 0.09245 22 - 0.02421 23 - 0.03829 24 - 0.03653 25 - 0.05466 26 - 0.1282 27 - 0.1982 28 - 0.3297
As you can see, these correlate well with the graph, starting to become significant as soon as cooling takes effect. However, I am obviously applying the same test multiple times. Using a standard Bonferroni correction I would need to use a p value of around 0.0014 (36 hours) which I think is overly conservative as Bonferroni aims at some negative correlation-type dependence whereas my results are rather positively correlated.
Is this correct and if so, how can I calculate a more appropriate p value that takes into account the multiple testing?