Abstract
Time-to-event analysis is frequently used in medical research to investigate potential disease-modifying treatments in neurodegenerative diseases. Potential treatment effects are generally evaluated using the logrank test, which has optimal power and sensitivity when the treatment effect (hazard ratio) is constant over time. However, there is generally no prior information as to how the hazard ratio for the event of interest actually evolves. In these cases, the logrank test is not necessarily the most appropriate to use. When the hazard ratio is expected to decrease or increase over time, alternative statistical tests such as the Fleming-Harrington test, provide a better sensitivity. An example of this comes from a large, five-year randomised, placebo-controlled prevention trial (GuidAge) in 2854 community-based subjects making spontaneous memory complaints to their family physicians, which evaluated whether treatment with EGb761 can modify the risk of developing AD. The primary outcome measure was the time to conversion from memory complaint to Alzheimer's type dementia. Although there was no significant difference in the hazard function of conversion between the two treatment groups according to the preplanned logrank test, a significant treatment-by-time interaction for the incidence of AD was observed in a protocol-specified subgroup analysis, suggesting that the hazard ratio is not constant over time. For this reason, additional post hoc analyses were performed using the Fleming-Harrington test to evaluate whether there was a signal of a late effect of EGb761. Applying the Fleming-Harrington test, the hazard function for conversion to dementia in the placebo group was significantly different from that in the EGb761 treatment group (p = 0.0054), suggesting a late effect of EGb761. Since this was a post hoc analysis, no definitive conclusions can be drawn as to the effectiveness of the treatment. This post hoc analysis illustrates the interest of performing another randomised clinical trial of EGb761 explicitly testing the hypothesis of a late treatment effect, as well as of using of better adapted statistical approaches for long term preventive trials when it is expected that prevention cannot have an immediate effect but rather a delayed effect that increases over time.