Causal Models for Estimating the Effects of Weight Gain on Mortality

TL;DR

This paper introduces a novel causal modeling approach using g-estimation to accurately estimate the effects of weight gain on mortality, accounting for complex confounders and reverse causation in longitudinal epidemiologic data.

Contribution

It develops a structural nested model and g-estimation method that adjust for both measured and unmeasured confounders, improving causal inference in epidemiology.

Findings

Successfully adjusts for time-varying confounders and reverse causation.

Addresses confounding in subgroups with severe comorbidities.

Provides a framework for counterfactual mortality estimation.

Abstract

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.