Causal diagrams and the cross-sectional study

Eyal Shahar, Doron J. Shahar

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

The cross-sectional study design is sometimes avoided by researchers or considered an undesired methodology. Possible reasons include incomplete understanding of the research design, fear of bias, and uncertainty about the measure of association. Using causal diagrams and certain premises, we compared a hypothetical cross-sectional study of the effect of a fertility drug on pregnancy with a hypothetical cohort study. A side-by-side analysis showed that both designs call for a tradeoff between information bias and variance and that neither offers immunity to sampling colliding bias (selection bias). Confounding bias does not discriminate between the two designs either. Uncertainty about the order of causation (ambiguous temporality) depends on the nature of the postulated cause and the measurement method. We conclude that a cross-sectional study is not inherently inferior to a cohort study. Rather than devaluing the cross-sectional design, threats of bias should be evaluated in the context of a concrete study, the causal question at hand, and a theoretical causal structure.

Original languageEnglish (US)
Pages (from-to)57-65
Number of pages9
JournalClinical Epidemiology
Volume5
Issue number1
DOIs
StatePublished - Mar 8 2013

Keywords

  • Causal diagrams
  • Colliding bias
  • Cross-sectional study
  • Information bias

ASJC Scopus subject areas

  • Epidemiology

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