"Shaking the ladder" reveals how analytic choices can influence associations in nutrition epidemiology: beef intake and coronary heart disease as a case study.

Nutrition epidemiological models involve many analytic decisions, such as defining exposures, selecting which covariates to include, or configuring variables in different ways. We explored the impact of analytical decisions on conclusions in nutrition epidemiology using self-reported beef intake and incident coronary heart disease as a case study. We used REasons for Geographic and Racial Differences in Stroke (REGARDS) data, and selected covariates and their configurations from published literature to recapitulate common models used to assess associations between meat intake and health outcomes. Three model sets were designed: sets one and two used continuous and quintile-defined beef intakes, respectively, each with ∼500,000 randomly sampled specifications. Set three models directly emulated published covariate combinations. Few models (<1%) were statistically significant at p < 0.05. More hazard ratio (HR) point estimates were >1 when beef was polychotomized via quintiles (95% of models) vs. continuous intake (79% of models). Including covariates for race or multivitamin use shifted HRs toward the null with similar confidence interval widths. Models emulating existing published associations were all above HR of 1. For our case study, exposure configuration and exposure inclusion resulted in substantially different HR distributions, illustrating how analytical decisions can affect nutrition-related exposure/outcome associations. The finding of few statistically significant models does not prove, but may suggest, minimal association between beef and CHD. Singular assessments of nutritional epidemiology questions should therefore be interpreted with caution. Modeling many analytical approaches may better establish and investigate the uncertainty of nutritional epidemiology questions and provisional answers.