If the populations are not similar but the study population is predictably different from the original population, one could do additional modeling to account for these differences. For instance, selleck catalog if the optimal combination differed depending on time to progression to the next stage of dementia, the expected distribution of time prior to progression could be used to weight the combinations in order to get a reasonable estimate of the overall decline rate that would be expected in the study population and in order to ensure that the outcome measure is optimized for the population being enrolled. When the decline of an outcome over time is considered, it is important to consider the normal aging decline over time in healthy subjects.

Correcting for normal aging may be particularly important when the healthy group declines over time since a disease-modifying treatment effect is not likely to be able to slow normal aging effects even when it has a slowing effect on disease-specific decline. Correcting for normal aging may be less important when the healthy group has learning effects over time, since the estimates of decline over time will be conservative in this case. If a healthy control group is included in the study, then a correction for normal aging can be done on a group level or on an individual level on the basis of the specific age of the individual and a model fit to the healthy control group. In general, both corrected and uncorrected analyses should be considered when possible.

Conclusions In a pre-MCI population, retrospective enrichment based on comparing AV-951 those who progress to MCI with those who do not or on comparing mutation carriers with noncarriers offers the best setting in which to optimize a clinical outcome for measuring progression based on external responsiveness to changes over time. The combination of identifying a population of subjects who could be prospectively enrolled in a clinical study – such as MCI subjects, prodromal MCI subjects, or mutation carriers – and then optimizing a clinical outcome in that population results in a composite score that has the best chance for maximal power in a clinical study with these specific MCI populations. In both of these approaches, cross-validation is important and can be performed across different data sets if the populations are similar enoughor with split-sample validation methods applied to pooled samples when study populations differ. Quantitative outcomes are likely to be more powerful than dichotomous endpoints selleck chemicals since only large changes are captured with dichotomous endpoints and more subtle changes can be seen with quantitative outcomes.