Adjusting for reverse causality in the relationship between obesity and mortality.
Reverse causality, in which obesity-induced disease leads to both weight loss and higher mortality, may bias observed associations between body mass index (BMI) and mortality, but the magnitude of that bias is unknown. The authors examined the impact of reverse causality and the exclusion of various diseases on the observed age-specific mortality ratios for BMI by using a state space model and sensitivity analyses. They found that reverse causality may decrease the ratios and induce a J-shaped curve on a graph. The authors further found that the net effect of excluding various diseases becomes a balance of competing forces, some tending to increase observed mortality ratios, where as others, such as selection based on common effects, may decrease them. Instead of studying just the change in observed mortality ratios, which can be misleading, investigators need to consider causal relationships and evaluate the conceptual and theoretical impact of any analytic maneuver. Analyses should be balanced with sensitivity approaches as well as with alternative analytic approaches such as the use of structural models, G-estimation, simulations and ancillary data from animal studies.
Flanders
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Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA. flanders@sph.emory.edu
Augestad
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D000328:Adult; D017677:Age Distribution; D000368:Aged; D015982:Bias; D015992:Body Mass Index; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000209:etiology / Q000401:mortality*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D055815:Young Adult