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06:55
Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Adjusting for reverse causality in the relationship between obesity and mortality.
1Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA. flanders@sph.emory.edu
International Journal of Obesity (2005)
|August 21, 2008
Summary
Reverse causality, where disease causes weight loss and death, may bias body mass index (BMI) and mortality links. Researchers used models to show this bias can create a J-shaped curve, affecting mortality ratio interpretations.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Observed associations between body mass index (BMI) and mortality may be biased by reverse causality.
- Reverse causality occurs when obesity-induced diseases lead to weight loss and increased mortality, confounding the BMI-mortality relationship.
Purpose of the Study:
- To examine the impact of reverse causality on BMI-mortality associations.
- To assess how excluding diseases affects observed age-specific mortality ratios for BMI.
Main Methods:
- Utilized a state space model to analyze data.
- Employed sensitivity analyses to evaluate the impact of reverse causality and disease exclusion.
Main Results:
- Reverse causality was found to potentially decrease BMI-mortality ratios and create a J-shaped curve.
- Excluding diseases results in a balance of competing effects on mortality ratios, some increasing and others decreasing them.
Conclusions:
- Observed mortality ratios can be misleading due to reverse causality and analytical choices.
- Investigators should consider causal relationships and use sensitivity analyses and alternative methods for robust findings.
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