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course for a single event model, and will instead be the outcome of a
multi-event model. The events do not have to follow one after another
or be arranged in any particular pattern. The key is at least partial in-
dependence in the time course of progression for each event, and final
measured outcome (mortality) only occurring after multiple events have
occurred.
Similarly, a condition for a midlife rise in acceleration is a slow in-
crease in the rate at which individual components fail (Frank 2004a).

10.4 Conclusions

I have included a discussion of mortality in a book otherwise devoted
to cancer for two reasons. First, from the vantage point of the general
reliability problem, one can more easily see what is necessary to ex-
plain patterns of cancer incidence. Second, the extensive development
of multistage theory I presented in earlier chapters provides just the sort
of quantitative background needed to use reliability theory fruitfully in
the general study of mortality.
One might now ask: If reliability theory applies to everything, then
does it have any explanatory power? This question seems reasonable,
but I think it is the wrong question. The reliability framework provides
tools to help us formulate testable hypotheses. That framework by itself
is not a hypothesis.
For cancer, I have shown how multistage theory leads to many useful
hypotheses. For example, I have used the theory to predict how age-
incidence curves change in response to genetic perturbations (inherited
mutations) and environmental perturbations (mutagens and mitogens).
Reliability theory will develop into a useful tool for studies of mortality
and aging to the extent that one can develop useful hypotheses about
how age-incidence curves change in response to measurable perturba-
tions.

10.5 Summary

This chapter finishes my three empirical analyses of disease dynamics
in light of multistage progression models. The three empirical analyses
covered genetics, chemical carcinogenesis, and aging. The next section