Declining brain activity in cognitively normal apolipoprotein E ɛ4 heterozygotes: A foundation for using positron emission tomography to efficiently test treatments to prevent Alzheimer's disease

Cross-sectional positron emission tomography (PET) studies find that cognitively normal carriers of the apolipoprotein E (APOE) ɛ4 allele, a common Alzheimer's susceptibility gene, have abnormally low measurements of the cerebral metabolic rate for glucose (CMRgl) in the same regions as patients with Alzheimer's dementia. In this article, we characterize longitudinal CMRgl declines in cognitively normal ɛ4 heterozygotes, estimate the power of PET to test the efficacy of treatments to attenuate these declines in 2 years, and consider how this paradigm could be used to efficiently test the potential of candidate therapies for the prevention of Alzheimer's disease. We studied 10 cognitively normal ɛ4 heterozygotes and 15 ɛ4 noncarriers 50–63 years of age with a reported family history of Alzheimer's dementia before and after an interval of approximately 2 years. The ɛ4 heterozygotes had significant CMRgl declines in the vicinity of temporal, posterior cingulate, and prefrontal cortex, basal forebrain, parahippocampal gyrus, and thalamus, and these declines were significantly greater than those in the ɛ4 noncarriers. In testing candidate primary prevention therapies, we estimate that between 50 and 115 cognitively normal ɛ4 heterozygotes are needed per active and placebo treatment group to detect a 25% attenuation in these CMRgl declines with 80% power and P = 0.005 in 2 years. Assuming these CMRgl declines are related to the predisposition to Alzheimer's dementia, this study provides a paradigm for testing the potential of treatments to prevent the disorder without having to study thousands of research subjects or wait many years to determine whether or when treated individuals develop symptoms.

Estimating risks in declining populations with poor data

Census data on endangered species are often sparse, error-ridden, and confined to only a segment of the population. Estimating trends and extinction risks using this type of data presents numerous difficulties. In particular, the estimate of the variation in year-to-year transitions in population size (the “process error” caused by stochasticity in survivorship and fecundities) is confounded by the addition of high sampling error variation. In addition, the year-to-year variability in the segment of the population that is sampled may be quite different from the population variability that one is trying to estimate. The combined effect of severe sampling error and age- or stage-specific counts leads to severe biases in estimates of population-level parameters. I present an estimation method that circumvents the problem of age- or stage-specific counts and is markedly robust to severe sampling error. This method allows the estimation of environmental variation and population trends for extinction-risk analyses using corrupted census counts—a common type of data for endangered species that has hitherto been relatively unusable for these analyses.

Are the returns to technological change in health care declining?

Whether the U.S. health care system supports too much technological change—so that new technologies of low value are adopted, or worthwhile technologies become overused—is a controversial question. This paper analyzes the marginal value of technological change for elderly heart attack patients in 1984–1990. It estimates the additional benefits and costs of treatment by hospitals that are likely to adopt new technologies first or use them most intensively. If the overall value of the additional treatments is declining, then the benefits of treatment by such intensive hospitals relative to other hospitals should decline, and the additional costs of treatment by such hospitals should rise. To account for unmeasured changes in patient mix across hospitals that might bias the results, instrumental–variables methods are used to estimate the incremental mortality benefits and costs. The results do not support the view that the returns to technological change are declining. However, the incremental value of treatment by intensive hospitals is low throughout the study period, supporting the view that new technologies are overused.