Perspective: Limiting Dependence on Nonrandomized Studies and Improving Randomized Trials in Human Nutrition Research: Why and How

Abstract

A large majority of human nutrition research uses nonrandomized observational designs, but this has led to little reliable progress. This is mostly due to many epistemologic problems, the most important of which are as follows: difficulty detecting small (or even tiny) effect sizes reliably for nutritional risk factors and nutrition-related interventions; difficulty properly accounting for massive confounding among many nutrients, clinical outcomes, and other variables; difficulty measuring diet accurately; and suboptimal research reporting. Tiny effect sizes and massive confounding are largely unfixable problems that narrowly confine the scenarios in which nonrandomized observational research is useful. Although nonrandomized studies and randomized trials have different priorities (assessment of long-term causality compared with assessment of treatment effects), the odds for obtaining reliable information with the former are limited. Randomized study designs should therefore largely replace nonrandomized studies in human nutrition research going forward. To achieve this, many of the limitations that have traditionally plagued most randomized trials in nutrition, such as small sample size, short length of follow-up, high cost, and selective reporting, among others, must be overcome. Pivotal megatrials with tens of thousands of participants and lifelong follow-up are possible in nutrition science with proper streamlining of operational costs. Fixable problems that have undermined observational research, such as dietary measurement error and selective reporting, need to be addressed in randomized trials. For focused questions in which dietary adherence is important to maximize, trials with direct observation of participants in experimental in-house settings may offer clean answers on short-term metabolic outcomes. Other study designs of randomized trials to consider in nutrition include registry-based designs and "N-of-1" designs. Mendelian randomization designs may also offer some more reliable leads for testing interventions in trials. Collectively, an improved randomized agenda may clarify many things in nutrition science that might never be answered credibly with nonrandomized observational designs.

FIGURE 1

Epidemiologists may take aim at effects that differ greatly in size. One of the greatest achievements of observational epidemiology was the demonstration that smoking has a causal effect on lung cancer risk. In the case of smoking and lung cancer, the RR is very large (RR ≥ 10); this is akin to hitting the outer ring of the target. The middle ring must be hit when seeking the minimum effect size needed to upgrade the strength of observational evidence using the GRADE criteria (corresponding to RR = 3) (19). Few epidemiologic associations have such effect sizes. And the hit-to-miss ratio will be dismal when firing away at the small bullseye, which represents a typical association between a single nutrient and a clinical outcome (RR ≤ 1.05). GRADE, Grading of Recommendations Assessment, Development, and Evaluation.

FIGURE 2

A beautiful, jumbled globe of correlations. This correlation globe depicts associations with fasting serum TGs and 575 exposures, including nutrients, food components, and other families of exposure variables. The strength of each association corresponds to line thickness, with red lines depicting positive associations and blue lines depicting negative associations. To examine whether any of these exposures causes fasting hypertriglyceridemia (rather than being merely correlated with it), the exposure of interest must first be disentangled from all the others, a daunting task. Data for this depiction derive from 4 individual survey periods, spanning the years 1999–2006, of the NHANES. Similar analyses have been presented in reference . Figure art courtesy of Chirag Patel.