  Suppose, contrary to fact, in 1950, we had put the cohort of 18 year old
non-smoking American men on a stringent mandatory diet that guaranteed that no
one would ever weigh more than their baseline weight established at age 18. How
would the counter-factual mortality of these 18 year olds have compared to
their actual observed mortality through 2007? We describe in detail how this
counterfactual contrast could be estimated from longitudinal epidemiologic data
similiar to that stored in the electronic medical records of a large HMO by
applying g-estimation to a novel structural nested model. Our analytic approach
differs from any alternative approach in that in that, in the abscence of model
misspecification, it can successfully adjust for (i) measured time-varying
confounders such as exercise, hypertension and diabetes that are simultaneously
intermediate variables on the causal pathway from weight gain to death and
determinants of future weight gain, (ii) unmeasured confounding by undiagnosed
preclinical disease (i.e reverse causation) that can cause both poor weight
gain and premature mortality [provided an upper bound can be specified for the
maximum length of time a subject may suffer from a subclinical illness severe
enough to affect his weight without the illness becomes clinically manifest],
and (iii) the prescence of particular identifiable subgroups, such as those
suffering from serious renal, liver, pulmonary, and/or cardiac disease, in whom
confounding by unmeasured prognostic factors so severe as to render useless any
attempt at direct analytic adjustment.
