Observational research rigour alone does not justify causal inference

Keisuke Ejima, Peng Li, Daniel L. Smith, Tim R. Nagy, Inga Kadish, Thomas van Groen, John A. Dawson, Yongbin Yang, Amit Patki, David B. Allison

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Background: Differing opinions exist on whether associations obtained in observational studies can be reliable indicators of a causal effect if the observational study is sufficiently well controlled and executed. Materials and methods: To test this, we conducted two animal observational studies that were rigorously controlled and executed beyond what is achieved in studies of humans. In study 1, we randomized 332 genetically identical C57BL/6J mice into three diet groups with differing food energy allotments and recorded individual self-selected daily energy intake and lifespan. In study 2, 60 male mice (CD1) were paired and divided into two groups for a 2-week feeding regimen. We evaluated the association between weight gain and food consumption. Within each pair, one animal was randomly assigned to an S group in which the animals had free access to food. The second paired animal (R group) was provided exactly the same diet that their S partner ate the day before. Results: In study 1, across all three groups, we found a significant negative effect of energy intake on lifespan. However, we found a positive association between food intake and lifespan among the ad libitum feeding group: 29·99 (95% CI: 8·2–51·7) days per daily kcal. In study 2, we found a significant (P = 0·003) group (randomized vs. self-selected)-by-food consumption interaction effect on weight gain. Conclusion: At least in nutrition research, associations derived from observational studies may not be reliable indicators of causal effects, even with the most rigorous study designs achievable.

Original languageEnglish
Pages (from-to)985-993
Number of pages9
JournalEuropean Journal of Clinical Investigation
Issue number12
StatePublished - Dec 1 2016


  • Causality
  • nutritional sciences
  • observational study
  • randomized controlled trial
  • research design


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