  It is well-known that claims coming from observational studies often fail to
replicate when rigorously re-tested. The technical problems include multiple
testing, multiple modeling and bias. Any or all of these problems can give rise
to claims that will fail to replicate. There is a need for statistical methods
that are easily applied, are easy to understand, and are likely to give
reliable results. In particular, simple ways for reducing the influence of bias
are essential. In this paper, the Local Control method developed by Robert
Obenchain is explicated using a small air quality/longevity data set first
analyzed in the New England Journal of Medicine. The benefits of our paper are
twofold. First, we describe a reliable strategy for analysis of observational
data. Second and importantly, the global claim that longevity increases with
improvements in air quality made in the NEJM paper needs to be modified. There
is subgroup heterogeneity in the effect of air quality on longevity (one size
does not fit all), and this heterogeneity is largely explained by factors other
than air quality.
